Leo Cancer Care is a trans-Atlantic company that’s pioneering the development of upright radiotherapy – a totally new take on radiation delivery in which the patient is treated in an upright position and rotated in front of a fixed treatment beam. At this week’s ASTRO 2025 meeting in San Francisco, the company introduced its first upright photon therapy system, named Grace, to an enthusiastic crowd in the ASTRO exhibit hall.

Upright treatments have a host of potential advantages over conventional radiotherapy, where patients typically lie on their back during treatment. Studies have shown that the more natural upright posture could deliver more consistent anatomical positioning and organ stability, as well as enabling more comfortable treatment positions, with patients who have experienced the technology reporting improved comfort and greater patient–therapist connection.

A fixed treatment beam also simplifies system design, reduces space and shielding requirements, and lowers infrastructure costs. And for proton therapy in particular, removing the need for a bulky and expensive gantry could help increase global access to advanced cancer treatments. Indeed, a partnership between Leo Cancer Care and Mevion Medical Systems led to the development of the MEVION S250-FIT, an ultracompact upright proton therapy system that fits inside a linac vault.

Moving on from Leo Cancer Care’s initial focus on proton therapy, the new Grace system will deliver conventional X-ray radiation therapy with patients positioned upright. Grace – named after American computer scientist and US Navy rear admiral Grace Hopper – comprises an upright patient positioning system (with six degrees of freedom and 360° continuous rotation) in front of a stationary 6 MV photon linac.

“Our future innovation, Grace, will take a proven technology, photon therapy, and rethink the way it can be delivered,” Sophie Towe, the company’s director of marketing, tells Physics World. “Upright treatment isn’t just about comfort; it’s about consistency, stability and ultimately accessibility. By integrating advanced CT imaging, faster beam delivery and a more natural patient position, we are opening the door to more adaptive and affordable care. Our goal is to show that innovation in radiotherapy doesn’t always mean bigger or more complex; it can mean smarter and more human.”

The system features a fan-beam CT scanner at the treatment isocentre, enabling planning-quality imaging throughout the entire treatment workflow. It also incorporates a large, ultrafast multileaf collimator that, in combination with the stationary photon beam delivery system, is designed to optimize dose conformity and treatment efficiency.

“Leo Cancer Care is already known for delivering upright particle therapy technology, and over the past few years we have seen a real paradigm shift as a result,” says co-founder and CEO Stephen Towe in a press statement. “Grace represents a return to our original company focus of delivering more cost-effective photon treatments to a global stage without sacrificing on treatment quality. Our technology has always been bold, but we are pioneering with purpose and that purpose is to put the patient truly back at the centre of their treatments.”

The company will install the first pre-commercial Grace systems at healthcare institutions within the Upright Photon Alliance research collaboration, which include Centre Léon Bérard, Cone Health, IHH Healthcare, Mayo Clinic and OncoRay.

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Brain metastases – cancerous lesions that have spread from elsewhere in the body – are increasingly treated using stereotactic radiotherapy (SRS), a precision technique that targets each individual lesion with a high dose of radiation. Compared with whole-brain irradiation, SRS may lead to higher local control and increased cognitive sparing, as well as a shorter overall treatment duration. But to target and treat multiple brain metastases, each lesion must first be detected on an MRI scan and accurately delineated. And this can be a complex and time-consuming task.

“There are two challenges that we face in the clinic,” explains Evrim Tezcanli, professor of radiation oncology at Acibadem Atasehir Hospital in Turkey. “First, we want to treat all the lesions. But very small lesions, particularly those under 0.1 cc, can easily be missed by untrained eyes. Larger metastases, meanwhile, are more challenging to contour – you want to cover the whole lesion without missing a pixel, but don’t want to spill radiation over into the brain tissue. It’s time-consuming work, especially if there are multiple lesions.”

To address these challenges, Siemens Healthineers has developed an AI-powered software tool that automates the contouring of brain metastases. The software – integrated into the company’s syngo.via RT Image Suite and AI-Rad Companion Organs RT packages – employs advanced deep-learning algorithms to rapidly analyse a patient’s MR images and contour and label metastatic lesions. Alongside, it delineates key organs at risk, such as the brainstem and optic structures.

“One of the main strengths of this software is that it reduces the manual workloads really well,” says Tezcanli.

Meeting clinical standards

To evaluate the accuracy and time efficiency of the new software tool, Tezcanli and her team compared AI-based delineation with the performance of two experienced radiation oncologists. The study included data from 10 patients with between three and 17 brain metastases. The radiation oncologists manually contoured all lesions (82 in total) based on patients’ contrast-enhanced MRI scans; the same images were also processed by the AI software to automatically contour the metastases.

Tezcanli reports that the software performed remarkably well. “One of the most significant findings was that the manual contours and the AI-generated contours showed strong agreement, especially for lesions larger than 0.1 cc. In terms of geometric similarity, the AI-generated boundaries were well within our clinically acceptable levels,” she says.

Comparing the manual and AI-generated contours revealed a medium Dice similarity coefficient of 0.83, increasing to 0.91 when excluding very small lesions, and a median Hausdorff distance (the maximum distance between the two contours) of 0.3 mm.

AI will definitely have a place because of the time savings and accuracy it delivers

Evrim Tezcanli

To quantify the overall time efficiency, the researchers timed the contouring process for the radiation oncologists and the AI tool. They also measured the time taken for expert review of the AI-generated results, in which a radiation oncologist checks the contours and performs any necessary adjustments before they are approved for treatment planning.

The AI software completed the contouring for each patient in just one to two minutes, reducing the workload by an average of 75%, and in some cases saving over 30 minutes per patient. “We still needed to review the AI contours, but the correction time was only three to four minutes,” says Tezcanli, emphasizing that expert review remains essential when using AI. “One case required nine minutes, but even with that patient we had a time saving of 75%.”

As well as saving time for the oncology staff, AI-based contouring has a lot to offer from the patient’s perspective. Spending less time on demanding manual contouring frees up the physician to spend more time with the patient.

Lesion detection

For their study, the researchers analysed post-contrast T1 MPRAGE sequences recorded using a 3 Tesla MRI scanner. To maximize lesion enhancement, they acquired images several minutes after contrast injection, though Tezcanli notes that this timing may vary between treatment centres. They also used image slices of 1 mm or less. “This is a very precise treatment and we want to make sure everything is accurate,” she adds.

The study deliberately included patients with varying numbers of different sized metastases, to assess the algorithms under diverse clinical scenarios. In terms of lesion detection, the software exhibited an overall sensitivity of 94% – finding 77 of the 82 metastases. The five missed lesions were extremely small, 0.01 to 0.03 cc, a volume that’s challenging even for physicians to detect. The software did, however, find three additional lesions that were not originally identified and which were later confirmed as brain metastases.

The false positive rate was 8.5%, with the software mistakenly identifying seven vascular structures as metastases. “Because the algorithms work with contrast enhancement, any vascular enhancements that mimic the tumour can be mistaken,” says Tezcanli. “Here we needed to use a dedicated MRI sequence to define whether it was a metastasis or not. That’s just one thing to be cautious about. Other than that, we were very satisfied with the software’s ability to detect small lesions and find ones that we hadn’t detected.”

Automation with HyperArc

The contours generated by the AI software are exported in DICOM RT Struct format, enabling direct transfer into the treatment planning system. At Acibadem Atasehir Hospital, this next step is performed using HyperArc, a radiosurgery-specific software module within the Eclipse treatment planning infrastructure. HyperArc performs automated treatment planning and delivery, enabling fast and efficient SRS on the Varian TrueBeam and Edge linacs.

“HyperArc has proven to be highly effective, even when treating patients with multiple brain metastases,” says Burcin Ispir, a medical physicist working alongside Tezcanli. “One of its biggest powers is its ability to perform single isocentre, automated planning for multiple targets, which significantly reduces planning time while maintaining excellent plan quality. In our experience, HyperArc-generated plans offer high conformity and steep dose gradients, which are critical for sparing normal brain tissue.”

Unlike conventional radiotherapy where homogeneity is desirable, SRS plans intentionally allow controlled heterogeneity within the target volume to improve sparing of normal tissue. HyperArc also offers automation of the beam geometry, including collimator and couch angles, ensuring consistent, fast and highly reproducible plans “For selected cases, we have found this enables a same-day workflow where contouring, planning and treatment can all be completed within a single day,” Ispir explains.

The automation in AI contouring and HyperArc planning speeds up the treatment planning process, and when compared to traditional workflows, potentially allows patients to commence radiation therapy treatments earlier. The ability to commence treatment as soon as possible after the MRI scan is imperative when treating brain metastases. Most patients will also be receiving systemic therapies, which need to be delivered on schedule. But perhaps more importantly, the high spatial precision of SRS makes the technique sensitive to even small anatomical changes within lesions. If the delay between MR imaging and radiotherapy treatment is too long, any changes occurring during that time could decrease targeting accuracy.

“We are in an era where we are using the technology to have even same-day treatments,” says Tezcanli. “We have rapid contouring with AI, a quick review of a few minutes by the expert radiation oncologist, treatment planning with HyperArc, and then a few hours later the patient is treated. This is where the technology is taking us.”

Look to the future

Continuing improvements in cancer treatment techniques mean that patients are living longer, but this also increases the likelihood of metastases developing. In addition, higher quality MRI scans and enhanced imaging protocols lead to more metastases being detected. These factors combine to increase the workload on centres treating multiple metastases with SRS.

“I think we will be treating brain metastasis more and more,” says Tezcanli. “And I think radiosurgery will be the main treatment modality in the future. AI will definitely have a place because of the time savings and accuracy it delivers. And this is only the first version of the software; I’m sure it can be improved to find even smaller lesions or differentiate vascular structures.”

Following the initial software evaluation, the team has not yet fully integrated it into their clinical routine, but Tezcanli tells Physics World that they would be happy to use the software in every one of their brain metastases treatments. “I think we will be using it routinely in the future in all of our clinical cases,” she says.

• The statements by customers of Siemens Healthineers described herein are based on results that were achieved in the customer’s unique setting. Because there is no “typical” hospital or laboratory and many variables exist (e.g., hospital size, samples mix, case mix, level of IT and/or automation adoption) there can be no guarantee that other customers will achieve the same results.
  The products/features mentioned herein are not commercially available in all countries. Their future availability cannot be guaranteed.
  Autocontouring results are generated by Siemens. The displayed renderings are created with software that is not commercially available.

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Radiotherapy is a precision cancer therapy that employs personalized treatment plans to target radiation to tumours with high accuracy. Such plans are usually created from high-resolution CT scans of the patient. But interest is growing in an alternative approach: MR simulation, in which MR images are used to generate the treatment plans – for delivery on conventional linac systems as well as the increasingly prevalent MR-guided radiotherapy systems.

One site that has transitioned to this approach is the Institut Jules Bordet in Belgium, which in 2021 acquired both an Elekta Unity MR-Linac and a Siemens MAGNETOM Aera MR-Simulator. “It was a long-term objective for our clinic to have an MR-only workflow,” says Akos Gulyban, a medical physicist at Institut Jules Bordet. “When we moved to a new campus, we decided to purchase the MR-Linac. Then we thought that if we are getting into the MR world for treatment adaptation, we also need to step up in terms of simulation.”

The move to MR simulation delivers many clinical benefits, with MR images providing the detailed anatomical information required to delineate targets and organs-at-risk with the highest precision. But it also creates new challenges for the physicists, particularly when it comes to quality assurance (QA) of MR-based systems. “The biggest concern is geometric distortion,” Gulyban explains. “If there is no distortion correction, then the usability of the machine or the sequence is very limited.”

Addressing distortion

While the magnetic field gradient is theoretically linear, and MRI is indeed extremely accurate at the imaging isocentre, moving away from the isocentre increases distortion. Images of regions 30 or 40 cm away from the isocentre – a reasonable distance for a classical linac – can differ from reality by 15 to 20 mm, says Gulyban. Thankfully, 3D correction algorithms can reduce this discrepancy down to just a couple of millimetres. But such corrections first require an accurate way to measure the distortion.

To address this task, the team at Institut Jules Bordet employ a geometric distortion phantom –the QUASAR MRID^3D Geometric Distortion Analysis System from IBA Dosimetry. Gulyban explains that the MRID^3D was chosen following discussions with experienced users, and that key selling points included the phantom’s automated software and its ability to efficiently store results for long-term traceability.

“My concern was how much time we spend cross-processing, generating reports or evaluating results,” he says. “This software is fully automated, making it much easier to perform the evaluation and less dependent on the operator.”

Gulyban adds that the team was looking for a vendor-independent solution. “I think it is a good approach to use the tools provided [by the vendor] but now we have a way to measure the same thing using a different approach. Since our new campus has a mixture of Siemens MRs and the MR-Linac, this phantom provides a vendor-independent bridge between the two worlds.”

For quality control of the MR-Simulator, the team perform distortion measurements every three months, as well as after system interventions such as shimming and following any problems arising during other routine QA procedures. “We should not consider tests as individual islands in the QA process,” says Gulyban. “For instance, the ACR image quality phantom, which is used for more frequent evaluation, also partly assesses distortion. If we see that failing, I would directly trigger measurements with the more appropriate geometric distortion phantom.”

A lightweight option

To perform MR simulation, the images used for treatment planning must encompass both the target volume and the surrounding region, to ensure accurate delineation of the tumour and nearby organs-at-risk. This requires a large field-of-view (FOV) scan – plus geometric distortion QA that covers the same large FOV.

“You’re using this image to delineate the target and also to spare the organs-at-risk, so the image must reflect reality,” explains Kawtar Lakrad, medical physicist and clinical application specialist at IBA Dosimetry. “You don’t want that image to be twisted or the target volume to appear smaller or bigger than it actually is. You want to make sure that all geometric qualities of the image align with what’s real.”

Typically, geometric distortion phantoms are grid-like, with control points spaced every 0.5 or 1 cm. The entire volume is imaged in the MR scanner and the locations of control points seen in the image compared with their actual positions. “If we apply this to a large FOV phantom, which for MRI will be filled with either water or oil, it’s going to be a very large grid and it’s going to be heavy, 40 or 50 kg,” says Lakrad.

To overcome this obstacle, IBA researchers used innovative harmonic analysis algorithms to design a lightweight geometric distortion phantom with submillimetre accuracy and a large (35 x 30 cm) FOV: the MRID^3D. The phantom comprises two concentric hollow acrylic cylinders, the only liquid being a prefilled mineral oil layer between the two shells, reducing its weight to just 21 kg.

“The idea behind the phantom was very smart because it relies on a mathematical tool,” explains Lakrad. “There is a Fourier transform for the linear signal, which is used for standard grids. But there are also spherical harmonics – and this is what’s used in the MRID^3D. The control points are all on the cylinder surface, plus one in the isocentre, creating a virtual grid that measures 3D geometric distortion.” She adds that the MRID^3D can also differentiate distortion due to the main magnetic field from gradient non-linearity distortion.

Moving into the MR world

Gulyban and his team at Institut Jules Bordet first used MR simulation for pelvic treatments, particularly prostate cancer, he tells Physics World. This was followed by abdominal tumours, such as pancreatic and liver cancers (where many patients were being treated on the MR-Linac) and more recently, cranial and head-and-neck irradiations.

Gulyban points out that the introduction of the MR-Simulator was eased by the team’s experience with the MR-Linac, which helped them “step into the MR world”. Here also, the MRID^3D phantom is used to quantify geometric distortion, both for initial commissioning and continuous QA of the MR-Linac.

“It’s like a consistency check,” he explains. “We have certain manufacturer-defined conditions that we need to meet for the MR-Linac – for instance, that distortion within a 40 mm diameter should be less than 1 mm. To ensure that these are met in a consistent fashion, we repeat the measurements with the manufacturer’s phantom and with the MRID^3D. This gives us extra peace of mind that the machine is performing under the correct conditions.”

For other cancer centres looking to integrate MR into their radiotherapy clinics, Gulyban has some key points of advice. These include starting with MR-guided radiotherapy and then adding MR simulation, identifying a suitable pathology to treat first and gain familiarity, and attending relevant courses or congresses for inspiration.

“The biggest change is actually a change in culture because you have an active MRI in the radiotherapy department,” he notes. “We are used to the radioprotection aspects of radiotherapy, wearing a dosimeter and observing radiation protection principles. MRI is even less forgiving – every possible thing that could go wrong you have to eliminate. Closing all the doors and emptying your pockets must become a reflex habit. You have to prepare mentally for that.”

“When you’re used to CT-based machines, moving to an MR workflow can be a little bit new,” adds Lakrad. “Most physicists are already familiar with the MR concept, but when it comes to the QA process, that’s the most challenging part. Some people would just repeat what’s done in radiology – but the use case is different. In radiotherapy, you have to delineate the target and surrounding volumes exactly. You’re going to be delivering dose, which means the tolerance between diagnostic and radiation therapy is different. That’s the biggest challenge.”

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A combination of static proton arcs and shoot-through proton beams could increase plan conformity and homogeneity and reduce delivery times in upright proton therapy, according to new research from RaySearch Laboratories in Sweden.

Proton arc therapy (PAT) is an emerging rotational delivery technique with potential to improve plan quality – reducing dose to organs-at-risk while maintaining target dose. The first clinical PAT treatments employed static arcs, in which multiple energy layers are delivered from many (typically 10 to 30) discrete angles. Importantly, static arc PAT can be delivered on conventional proton therapy machines. It also offers simpler beam arrangements than intensity-modulated proton therapy (IMPT).

“In IMPT of head-and-neck cancers, the beam directions are normally set up in a complicated pattern in different planes, with range shifters needed to treat the shallow part of the tumour,” explains Erik Engwall, chief physicist at RaySearch Laboratories. “In PAT, the many beam directions are arranged in the same plane and no range shifters are typically needed. With all beams in the same plane, it is easier to move to upright treatments.”

Upright proton therapy involves rotating the patient (in an upright position) in front of a static horizontal treatment beam. The approach could reduce costs by using compact proton delivery systems. This compactness, however, places energy selection close to the patient, increasing scattering in the proton beam. To combat this, the team propose adding a layer of shoot-through protons to each direction of the proton arc.

The idea is that while most protons are delivered with Bragg peaks placed in the target, the sharp penumbra of the high-energy protons shooting through the target will combat beam broadening. The rotational delivery in the proton arc spreads the exit dose from these shoot-through beams over many angles, minimizing dose to surrounding tissues. And as the beamline is fixed, shoot-through protons exit in the same direction (behind the patient) for all angles, simplifying shielding to a single beam dump opposite the fixed beam.

Simulation studies

To test this approach, Engwall and colleagues simulated treatment plans for a virtual phantom containing three targets and an organ-at-risk, reporting their findings in Medical Physics. They used a development version of RayStation v2025 with a beam model of the Mevion s250-FIT system (which combines a compact cyclotron, an upright positioner and an in-room CT scanner).

For each target, the team created static arc plans with (Arc+ST) and without shoot-through beams and with/without collimation, as well as 3-beam IMPT plans with and without shoot-through beams (all with collimation). Arc plans used 20 uniformly spaced beam directions, and the shoot-through plans included an additional layer of the highest system energy (230 MeV) for each direction.

For all targets, Arc+ST plans showed superior conformity, homogeneity and target robustness to arc plans without shoot-through protons. Adding collimation slightly improved the arc plans without shoot-through protons but had little impact on Arc+ST plans.

The IMPT plans achieved similar homogeneity and robustness to the best arc plans, but with far lower conformity due to the shoot-through protons delivering a concentrated exit dose behind the target (while static arcs distribute this dose over many directions). Adding shoot-through protons improved IMPT plan quality, but to a lesser degree than for PAT plans.

Clinical case

The researchers repeated their analysis for a clinical head-and-neck cancer case, comparing static arcs with 5-beam IMPT. Again, Arc+ST plans performed better than any others for almost all metrics. “The Arc+ST plans have the best quality due to the sharpening of the penumbra of the shoot-through part, even better than when using a collimator,” says Engwall.

Notably, the findings suggest that collimation is not needed when combining arcs with shoot-through beams, enabling rapid treatments. With fast energy switching and the patient rotation at 1 rpm, Arc+ST achieved an estimated delivery time of less than 5.4 min – faster than all other plans for this case, including 5-beam IMPT.

“Treatment time is reduced when the leaves of the dynamic collimator do not need to move,” Engwall explains. “There is also no risk of mechanical failures of the collimator and the secondary neutron production will be lower when there are fewer objects in the beamline.”

Another benefit of upright delivery is that the shoot-through protons can be used for range verification during treatments, using a detector integrated into the beam dump behind the patient. The team investigated this concept with three simulated error scenarios: 5% systematic shift in stopping power ratio; 5 mm setup shift; and 2 cm shoulder movement. The technique successfully detected all errors.

As the range detector is permanently installed in the treatment room and the shoot-through protons are part of the treatment plan, this method does not add time to the patient setup and can be used in every treatment fraction to detect both intra- and inter-fraction uncertainties.

Although this is a proof-of-concept study, the researchers conclude that it highlights the combined advantages of the new treatment technique, which could “leverage the potential of compact upright proton treatments and make proton treatments more affordable and accessible to a larger patient group”.

Engwall tells Physics World that the team is now collaborating with several clinical research partners to investigate the technique’s potential across larger patient data sets, for other treatment sites and multiple treatment machines.

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Researchers in Germany have demonstrated the first cancer treatment using a radioactive carbon ion beam (¹¹C), on a mouse with a bone tumour close to the spine. Performing particle therapy with radioactive ion beams enables simultaneous treatment and visualization of the beam within the body.

Particle therapy using beams of protons or heavy ions is a highly effective cancer treatment, with the favourable depth–dose deposition – the Bragg peak – providing extremely conformal tumour targeting. This conformality, however, makes particle therapy particularly sensitive to range uncertainties, which can impact the Bragg peak position.

One way to reduce such uncertainties is to use positron emission tomography (PET) to map the isotopes generated as the treatment beam interacts with tissues in the patient. For therapy with carbon (¹²C) ions, currently performed at 17 centres worldwide, this involves detecting the beta decay of ¹⁰C and ¹¹C projectile fragments. Unfortunately, such fragments generate a small PET signal, while their lower mass shifts the measured activity peak away from the Bragg peak.

The researchers – working within the ERC-funded BARB (Biomedical Applications of Radioactive ion Beams) project – propose that treatment with positron-emitting ions such as ¹¹C could overcome these obstacles. Radioactive ion beams have the same biological effectiveness as their corresponding stable ion beams, but generate an order of magnitude larger PET signal. They also reduce the shift between the activity and dose peaks, enabling precise localization of the ion beam in vivo.

“Range uncertainty remains the main problem of particle therapy, as we do not know exactly where the Bragg peak is,” explains Marco Durante, head of biophysics at the GSI Helmholtz Centre for Heavy Ion Research and principal investigator of the BARB project. “If we ‘aim-and-shoot’ using a radioactive beam and PET imaging, we can see where the beam is and can then correct it. By doing this, we can reduce the margins around the target that spoil the precision of particle therapy.”

In vivo experiments

To test this premise, Durante and colleagues performed in vivo experiments at the GSI/FAIR accelerator facility in Darmstadt. For online range verification, they used a portable small-animal in-beam PET scanner built by Katia Parodi and her team at LMU Munich. The scanner, initially designed for the ERC project SIRMIO (Small-animal proton irradiator for research in molecular image-guided radiation-oncology), contains 56 depth-of-interaction detectors – based on scintillator blocks of pixelated LYSO crystals – arranged spherically with an inner diameter of 72 mm.

“Not only does our spherical in-beam PET scanner offer unprecedented sensitivity and spatial resolution, but it also enables on-the-fly monitoring of the activity implantation for direct feedback during irradiation,” says Parodi, co-principal investigator of the BARB project.

The researchers used a radioactive ¹¹C-ion beam – produced at the GSI fragment separator – to treat 32 mice with an osteosarcoma tumour implanted in the neck near the spinal cord. To encompass the full target volume, they employed a range modulator to produce a spread-out Bragg peak (SOBP) and a plastic compensator collar, which also served to position and immobilize the mice. The anaesthetized animals were placed vertically inside the PET scanner and treated with either 20 or 5 Gy at a dose rate of around 1 Gy/min.

For each irradiation, the team compared the measured activity with Monte Carlo-simulated activity based on pre-treatment microCT scans. The activity distributions were shifted by about 1 mm, attributed to anatomical changes between the scans (with mice positioned horizontally) and irradiation (vertical positioning). After accounting for this anatomical shift, the simulation accurately matched the measured activity. “Our findings reinforce the necessity of vertical CT planning and highlight the potential of online PET as a valuable tool for upright particle therapy,” the researchers write.

With the tumour so close to the spine, even small range uncertainties risk damage to the spinal cord, so the team used the online PET images generated during the irradiation to check that the SOPB did not cover the spine. While this was not seen in any of the animals, Durante notes that if it had, the beam could be moved to enable “truly adaptive” particle therapy. Assessing the mice for signs of radiation-induced myelopathy (which can lead to motor deficits and paralysis) revealed that no mice exhibited severe toxicity, further demonstrating that the spine was not exposed to high doses.

Following treatment, tumour measurements revealed complete tumour control after 20 Gy irradiation and prolonged tumour growth delay after 5 Gy, suggesting complete target coverage in all animals.

The researchers also assessed the washout of the signal from the tumour, which includes a slow activity decrease due to the decay of ¹¹C (which has a half-life of 20.34 min), plus a faster decrease as blood flow removes the radioactive isotopes from the tumour. The results showed that the biological washout was dose-dependent, with the fast component visible at 5 Gy but disappearing at 20 Gy.

“We propose that this finding is due to damage to the blood vessel feeding the tumour,” says Durante. “If this is true, high-dose radiotherapy may work in a completely different way from conventional radiotherapy: rather than killing all the cancer stem cells, we just starve the tumour by damaging the blood vessels.”

Future plans

Next, the team intends to investigate the use of ¹⁰C or ¹⁵O treatment beams, which should provide stronger signals and increased temporal resolution. A new Super-FRS fragment separator at the FAIR accelerator facility will provide the high-intensity beams required for studies with ¹⁰C.

Looking further ahead, clinical translation will require a realistic and relatively cheap design, says Durante. “CERN has proposed a design [the MEDICIS-Promed project] based on ISOL [isotope separation online] that can be used as a source of radioactive beams in current accelerators,” he tells Physics World. “At GSI we are also working on a possible in-flight device for medical accelerators.”

The findings are reported in Nature Physics.

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Traumatic brain injury (TBI), caused by a sudden impact to the head, is a leading cause of death and disability. After such an injury, the most important indicator of how severe the injury is intracranial pressure – the pressure inside the skull. But currently, the only way to assess this is by inserting a pressure sensor into the patient’s brain. UK-based startup Crainio aims to change this by developing a non-invasive method to measure intracranial pressure using a simple optical probe attached to the patient’s forehead.

Can you explain why diagnosing TBI is such an important clinical challenge?

Every three minutes in the UK, someone is admitted to hospital with a head injury, it’s a very common problem. But when someone has a blow to the head, nobody knows how bad it is until they actually reach the hospital. TBI is something that, at the moment, cannot be assessed at the point of injury.

From the time of impact to the time that the patient receives an assessment by a neurosurgical expert is known as the golden hour. And nobody knows what’s happening to the brain during this time – you don’t know how best to manage the patient, whether they have a severe TBI with intracranial pressure rising in the head, or just a concussion or a medium TBI.

Once at the hospital, the neurosurgeons have to assess the patient’s intracranial pressure, to determine whether it is above the threshold that classifies the injury as severe. And to do that, they have to drill a hole in the head – literally – and place an electrical probe into the brain. This really is one of the most invasive non-therapeutic procedures, and you obviously can’t do this to every patient that comes with a blow in the head. It has its risks, there is a risk of haemorrhage or of infection.

Therefore, there’s a need to develop technologies that can measure intracranial pressure more effectively, earlier and in a non-invasive manner. For many years, this was almost like a dream: “How can you access the brain and see if the pressure is rising in the brain, just by placing an optical sensor on the forehead?”

Crainio has now created such a non-invasive sensor; what led to this breakthrough?

The research goes back to 2016, at the Research Centre for Biomedical Engineering at City, University of London (now City St George’s, University of London), when the National Institute for Health Research (NIHR) gave us our first grant to investigate the feasibility of a non-invasive intracranial sensor based on light technologies. We developed a prototype, secured the intellectual property and conducted a feasibility study on TBI patients at the Royal London Hospital, the biggest trauma hospital in the UK.

It was back in 2021, before Crainio was established, that we first discovered that after we shone certain frequencies of light, like near-infrared, into the brain through the forehead, the optical signals coming back – known as the photoplethysmogram, or PPG – contained information about the physiology or the haemodynamics of the brain.

When the pressure in the brain rises, the brain swells up, but it cannot go anywhere because the skull is like concrete. Therefore, the arteries and vessels in the brain are compressed by that pressure. PPG measures changes in blood volume as it pulses through the arteries during the cardiac cycle. If you have a viscoelastic artery that is opening and closing, the volume of blood changes and this is captured by the PPG. Now, if you have an artery that is compromised, pushed down because of pressure in the brain, that viscoelastic property is impacted and that will impact the PPG.

Changes in the PPG signal due to changes arising from compression of the vessels in the brain, can give us information about the intracranial pressure. And we developed algorithms to interrogate this optical signal and machine learning models to estimate intracranial pressure.

How did the establishment of Crainio help to progress the sensor technology?

Following our research within the university, Crainio was set up in 2022. It brought together a team of experts in medical devices and optical sensors to lead the further development and commercialization of this device. And this small team worked tirelessly over the last few years to generate funding to progress the development of the optical sensor technology and bring it to a level that is ready for further clinical trials.

In 2023, Crainio was successful with an Innovate UK biomedical catalyst grant, which will enable the company to engage in a clinical feasibility study, optimize the probe technology and further develop the algorithms. The company was later awarded another NIHR grant to move into a validation study.

The interest in this project has been overwhelming. We’ve had a very positive feedback from the neurocritical care community. But we also see a lot of interest from communities where injury to the brain is significant, such as rugby associations, for example.

Could the device be used in the field, at the site of an accident?

While Crainio’s primary focus is to deliver a technology for use in critical care, the system could also be used in ambulances, in helicopters, in transfer patients and beyond. The device is non-invasive, the sensor is just like a sticking plaster on the forehead and the backend is a small box containing all the electronics. In the past few years, working in a research environment, the technology was connected into a laptop computer. But we are now transferring everything into a graphical interface, with a monitor to be able to see the signals and the intracranial pressure values in a portable device.

Following preliminary tests on patients, Crainio is now starting a new clinical trial. What do you hope to achieve with the next measurements?

The first study, a feasibility study on the sensor technology, was done during the time when the project was within the university. The second round is led by Crainio using a more optimized probe. Learning from the technical challenges we had in the first study, we tried to mitigate them with a new probe design. We’ve also learned more about the challenges associated with the acquisition of signals, the type of patients, how long we should monitor.

We are now at the stage where Crainio has redeveloped the sensor and it looks amazing. The technology has received approval by MHRA, the UK regulator, for clinical studies and ethical approvals have been secured. This will be an opportunity to work with the new probe, which has more advanced electronics that enable more detailed acquisition of signals from TBI patients.

We are again partnering with the Royal London Hospital, as well as collaborators from the traumatic brain injury team at Cambridge and we’re expecting to enter clinical trials soon. These are patients admitted into neurocritical trauma units and they all have an invasive intracranial pressure bolt. This will allow us to compare the physiological signal coming from our intracranial pressure sensor with the gold standard.

The signals will be analysed by Crainio’s data science team, with machine learning algorithms used to look at changes in the PPG signal, extract morphological features and build models to develop the technology further. So we’re enriching the study with a more advanced technology, and this should lead to more accurate machine learning models for correctly capturing dynamic changes in intracranial pressure.

The primary motivation of Crainio is to create solutions for healthcare, developing a technology that can help clinicians to diagnose traumatic brain injury effectively, faster, accurately and earlier

This time around, we will also record more information from the patients. We will look at CT scans to see whether scalp density and thickness have an impact. We will also collect data from commercial medical monitors within neurocritical care to see the relation between intracranial pressure and other physiological data acquired in the patients. We aim to expand our knowledge of what happens when a patient’s intracranial pressure rises – what happens to their blood pressures? What happens to other physiological measurements?

How far away is the system from being used as a standard clinical tool?

Crainio is very ambitious. We’re hoping that within the next couple of years we will progress adequately in order to achieve CE marking and all meet the standards that are necessary to launch a medical device.

The primary motivation of Crainio is to create solutions for healthcare, developing a technology that can help clinicians to diagnose TBI effectively, faster, accurately and earlier. This can only yield better outcomes and improve patients’ quality-of-life.

Of course, as a company we’re interested in being successful commercially. But the ambition here is, first of all, to keep the cost affordable. We live in a world where medical technologies need to be affordable, not only for Western nations, but for nations that cannot afford state-of-the-art technologies. So this is another of Crainio’s primary aims, to create a technology that could be used widely, because there is a massive need, but also because it’s affordable.

• To hear the full interview with Panicos Kyriacou listen to our Physics World Weekly podcast

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Positron emission tomography (PET) is a diagnostic imaging technique that uses an injected radioactive tracer to detect early signs of cancer, brain disorders or other diseases. At Jagiellonian University in Poland, a research team headed up by Paweł Moskal is developing a totally new type of PET scanner. The Jagiellonian PET (J-PET) can image the properties of positronium, a positron–electron bound state produced during PET scans, offering potential to increase the specificity of PET diagnoses.

The researchers have now recorded the first ever in vivo positronium image of the human brain. They also used the J-PET to show that annihilation photons generated during PET scans are not completely quantum entangled, opening up the possibility of using the degree of quantum entanglement as a diagnostic indicator. Moskal tells Physics World’s Tami Freeman about these latest breakthroughs and the team’s ongoing project to build the world’s first whole-body quantum PET scanner.

Can you describe how conventional PET images are generated?

PET is based on the annihilation of a positron with an electron to create two photons. The patient is administered a radiopharmaceutical labelled with a positron-emitting radionuclide (for example, fluoro-deoxy-glucose (FDG) labelled with ¹⁸F), which localizes in targeted tissues. The ¹⁸F emits positrons inside the body, which annihilate with electrons from the body, and the resulting annihilation photons are registered by the PET scanner.

By measuring the locations and times of the photons’ interactions in the scanner, we can reconstruct the density distribution of annihilation points in the body. With ¹⁸F-FDG, this image correlates with the density distribution of glucose, which in turn, indicates the rate of glucose metabolism. Thus the PET scanner delivers an image of the radiopharmaceutical’s metabolic rate in the body.

Such an image enables physicians to identify tissues with abnormal metabolism, such as cancers that metabolize glucose up to 10 times more intensively than healthy tissues. Therefore, PET scanners can provide information about alterations in cell function, even before cancer may be visible in anatomical images recorded using CT or MRI.

During annihilation, a short-lived atom called positronium can form. What’s the rationale for imaging this positronium?

It’s amazing that in tissue, positron–electron annihilation proceeds via the formation of positronium in about 40% of cases. Positronium, a bound state of matter and antimatter (an electron and a positron), is short lived because it can undergo self-annihilation into photons. In tissue, however, it can decay via additional processes that further shorten its lifetime. For example, its positron may annihilate by “picking off” an electron from a surrounding atom, or it may convert from the long-lived state (ortho-positronium) to the short-lived state (para-positronium) through interaction with oxygen molecules.

In tissue, therefore, positronium lifetime is an indicator of the intra- and inter-molecular structure and the concentration of oxygen molecules. Both molecular composition and the degree of oxygen concentration differ between healthy and cancerous tissues, with hypoxia (a deficit in tissue oxygenation) a major feature of solid tumours that’s related to the development of metastases and treatment resistance.

As such, imaging positronium lifetime can help in early disease recognition at the stage of molecular alterations. It can also improve diagnosis and the proper choice of anti-cancer therapy. In the case of brain diagnostics, positronium imaging may become an early diagnostic indicator for neurodegenerative disorders such as dementia, Alzheimer’s disease and Parkinson’s disease.

So how does the J-PET detect positronium?

To reconstruct the positronium lifetime we use a radionuclide (⁴⁴Sc, ⁸²Rb or ¹²⁴I, for example) that, after emitting a positron, promptly (within a few picoseconds) emits an additional gamma photon. This “prompt gamma” can be used to measure the exact time that the positron was emitted into the tissue and formed positronium.

Current PET scanners are designed to register only two annihilation photons, which makes them incapable of determining positronium lifetime. The J-PET is the first multiphoton PET scanner designed for simultaneous registration of any number of photons.

The registration of annihilation photons enables us to reconstruct the time and location of the positronium decay, while registration of the prompt gamma provides the time of its formation. The positronium lifetime is then calculated as the time difference between annihilation and prompt gamma emission.

Can you describe how your team recorded the first in vivo positronium image?

Last year we presented the world’s first in vivo images of positronium lifetime in a human, reported in Science Advances. For this, we designed and constructed a modular, lightweight and portable J-PET tomograph, consisting of 24 independent detection modules, each weighing only 2 kg. The device uses a multiphoton data acquisition system, invented by us, to simultaneously register prompt gamma and annihilation photons – the first PET scanner in the world to achieve this.

The research was performed at the Medical University of Warsaw, with studies conducted following routine procedures so as not to interfere with routine diagnostics and therapy. If a patient agreed to stay longer on the platform, we had about 10 minutes to install the J-PET tomograph around them and collect data.

The first patient was a 45-year-old man with glioblastoma (an aggressive brain tumour) undergoing alpha-particle radiotherapy. The primary aim of his therapy was to destroy the tumour using alpha particles emitted by the radionuclide ²²⁵Ac. The positronium imaging was made possible by the concurrent theranostic application of the radionuclide ⁶⁸Ga to monitor the site of cancer lesions using a PET scanner.

The patient was administered a simultaneous intra-tumoural injection of the alpha-particle-emitting radiopharmaceutical (²²⁵Ac-DOTA-SP) for therapy and the positron emitting pharmaceutical (⁶⁸Ga-DOTA-SP) for diagnosis. In about 1% of cases, after emitting a positron that annihilates with an electron, ⁶⁸Ga also emits a prompt gamma ray.

We determined the annihilation location by measuring the time and position of interaction of the annihilation photons in the scanner. For each image voxel, we also determined a lifetime spectrum as the distribution of differences between the time of annihilation and the time of prompt gamma emission.

Our study found that positronium lifetimes in glioblastoma cells are shorter than in salivary glands and healthy brain tissues. We showed for the first time that the mean lifetime of ortho-positronium in a glioma (1.77±0.58 ns) is shorter than in healthy brain tissue (2.72±0.72 ns). This finding demonstrates that positronium imaging could be used for in vivo diagnosis to differentiate between healthy and cancerous tissues.

You recently demonstrated that J-PET can detect quantum entanglement of annihilation photons, how could this impact cancer diagnostics?

For this study, reported earlier this year in Science Advances, we used the laboratory prototype of the J-PET scanner (as employed previously for the first ex vivo positronium imaging). The crucial result was the first ever observation that photons from electron–positron annihilation in matter are not completely quantum entangled. Our study is pioneering in revealing a clear dependence of the degree of photon entanglement on the material in which the annihilation occurs.

These results are totally new compared with all previous investigations of photons from positron–electron annihilations. Up to this point, all experiments had focused on showing that this entanglement is maximal, and for that purpose, were performed in metals. None of the previous studies mentioned or even hypothesized a possible material dependence.

If the degree of quantum entanglement of annihilation photons depends on the material, it may also differ according to tissue type or the degree of hypoxia. This is a hypothesis that we will test in future studies. I recently received an ERC Advanced Grant, entitled “Can tissue oxidation be sensed by positronium?”, to investigate whether the degree of oxidation in tissue can be sensed by the degree of quantum entanglement of photons originating from positron annihilation.

What causes annihilation photons to be entangled (or not)?

Quantum entanglement is a fascinating phenomenon that cannot be explained by our classical perception of the world. Entangled photons behave as if one instantly knows what is happening with the other, regardless of how far apart they are, so they propagate in space as a single object.

Annihilation photons are entangled if they originate from a pure quantum state. A state is “pure” if we know everything that can be known about it. For example, if the photons originate from the ground state of para-positronium (a pure state), then we expect them to be maximally entangled.

However, if electron–positron annihilation occurs in a mixed state (a statistical mixture of different pure states) where we have incomplete information, then the resulting photons will not be maximally entangled. In our case, this could be the annihilation of a positron from positronium with electrons from the patient’s body. Because these electrons can have different angular momenta with respect to the positron, the annihilation generally occurs from a mixed state.

You have also measured the polarization of the annihilation photons; how is this information used?

In current PET scanners, images are reconstructed based on the position and time of interaction of annihilation photons within the scanner. However, annihilation photons also carry information about their polarization.

Theoretically, annihilation photons are quantum entangled in polarization and exhibit non-local correlations. In the case of electron–positron annihilation into two photons, this means that the amplitude of the distribution of the relative angle between their polarization planes is larger when they are quantum entangled than when they propagate in space as independent objects.

State-of-the-art PET scanners, however, cannot access polarization information. Annihilation photons have energy in the mega-electronvolt range and their polarization cannot be determined using established optical methods, which are designed for optical photons in the electronvolt range. Because these energetic annihilation photons interact with single electrons, their polarization can only be sensed via Compton scattering.

The angular distribution of photons scattered by electrons is not isotropic with respect to the polarization direction. Instead, scattering is most likely to occur in a plane perpendicular to the polarization plane of the photon before scattering. Thus, by determining the scattering plane (containing the primary and scattered photon), one can estimate the direction of polarization as being perpendicular to that plane. Therefore, to practically determine the polarization plane of the photon, you need to know its directions of flight both before and after Compton scattering in the material.

In plastic scintillators, annihilation photons primarily interact via the Compton effect. As the J-PET is built from plastic scintillators, it’s ideally suited to provide information about the photons’ polarization, which can be determined by registering both the annihilation photon and the scattered photon and then reconstructing the scattering plane.

Using the J-PET scanner, we determined the distribution of the relative angle between the polarization planes of photons from positron–electron annihilation in a porous polymer. The amplitude of the observed distribution is smaller than predicted for maximally quantum-entangled two-photon states, but larger than expected for separable photons.

This result can be explained by assuming that photons from pick-off annihilation are not entangled, while photons from direct and para-positronium annihilations are maximally entangled. Our finding indicates that the degree of entanglement depends on the annihilation mechanism in matter, opening avenues for exploring polarization correlations in PET as a diagnostic indicator.

What further developments are planned for the J-PET scanner?

When creating the J-PET technology, we started with a two-strip prototype, then a 24-strip prototype in 2014, followed by a full-scale 192-strip prototype in 2016. In 2021 we completed the construction of a lightweight (60 kg) J-PET version that is both modular and portable, and which we used to demonstrate the first clinical images.

The next step is the construction of the total-body quantum J-PET scanner. We are now at the stage of collecting all the elements of this scanner and expect to complete construction in 2028. The scanner will be installed at the Center for Theranostics, established by myself and Ewa Stępień, medical head of the J-PET team, at Jagiellonian University.

Total-body PET provides the ability to image the metabolism of all tissues in the body at the same time. Additionally, due to the high sensitivity of total-body PET scanners, it is possible to perform dynamic imaging – essentially, creating a movie of how the radiopharmaceutical distributes throughout the body over time.

The total-body J-PET will also be able to register the pharmacokinetics of drugs administered to a patient. However, its true distinction is that it will be the world’s first quantum PET scanner with the ability to image the degree of quantum entanglement of annihilation photons throughout the patient’s body. Additionally, it will be the world’s first total-body multiphoton PET, enabling simultaneous positronium imaging in the entire human body.

How do you see the J-PET’s clinical applications evolving in the future?

We have already performed the first clinical imaging using J-PET at the Medical University of Warsaw and the University Hospital in Kraków. The studies included the diagnosis of patients with neuroendocrine, prostate and glioblastoma tumours. The data collected at these hospitals were used to reconstruct standard PET images as well as positronium lifetime images.

Next, we plan to conduct positronium imaging of phantoms and humans with various radionuclides to explore its clinical applications as a biomarker for tissue pathology and hypoxia. We also intend to explore the J-PET’s multiphoton capabilities for simultaneous double-tracer imaging, as well as study the degree of quantum entanglement as a function of the annihilation mechanism.

Finally, we plan to explore the possibilities of applying quantum entanglement to diagnostics, and we look forward to performing total-body positronium and quantum entanglement imaging with the total-body J-PET in the Center for Theranostics.

• Paweł Moskal is a panellist in the forthcoming Physics World Live event on 25 September 2025. The event, which also features Miles Padgett from the University of Glasgow and Matt Brookes from the University of Nottingham, will examine how medical physics can make the most of the burgeoning field of quantum science. You can sign up free here.

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Human reproduction is an inefficient process, with less than one third of conceptions leading to live births. Failure of the embryo to implant in the uterus is one of the main causes of miscarriage. Recording this implantation process in vivo in real time is not yet possible, but a team headed up at the Institute for Bioengineering of Catalonia (IBEC) has designed a platform that enables visualization of human embryo implantation in the laboratory. The researchers hope that quantifying the dynamics of implantation could impact fertility rates and help improve assisted reproductive technologies.

At its very earliest stage, an embryo comprises a small ball of cells called a blastocyst. About six days after fertilization, this blastocyst starts to embed itself into the walls of the uterus. To study this implantation process in real time, the IBEC team created an ex vivo platform that simulates the outer layers of the uterus. Unlike previous studies that mostly focused on the biochemical and genetic aspects of implantation, the new platform enables study of the mechanical forces exerted by the embryo to penetrate the uterus.

The implantation platform incorporates a collagen gel to mimic the extracellular matrix encountered in vivo, as well as globulin-rich proteins that are required for embryo development. The researchers designed two configurations: a 2D platform, in which blastocysts settle on top of a flat gel; and a 3D version where the blastocysts are placed directly inside collagen drops.

To capture the dynamics of blastocyst implantation, the researchers recorded time-lapse movies using fluorescence imaging and traction force microscopy. They imaged the matrix fibres and their deformations using light scattering and visualized autofluorescence from the embryo under multiphoton illumination. To quantify matrix deformation, they used the fibres as markers for real-time tracking and derived maps showing the direction and amplitude of fibre displacements – revealing the regions where the embryo applied force and invaded the matrix.

Quantifying implantation dynamics

In the 2D platform, 72% of human blastocysts attached to and then integrated into the collagen matrix, reaching a depth of up to 200 µm in the gel. The embryos increased in size over time and maintained a spherical shape without spreading on the surface. Implantation in the 3D platform, in which the embryo is embedded directly inside the matrix, led to 80% survival and invasion rate. In both platforms, the blastocysts showed motility in the matrix, illustrating the invasion capacity of human embryos.

The researchers also monitored the traction forces that the embryos exerted on the collagen matrix, moving and reorganising it with a displacement that increased over time. They note that the displacement was not perfectly uniform and that the pulling varied over time and space, suggesting that this pulsatile behaviour may help the embryos to continuously sense the environment.

“We have observed that human embryos burrow into the uterus, exerting considerable force during the process,” explains study leader Samuel Ojosnegros in a press statement. “These forces are necessary because the embryos must be able to invade the uterine tissue, becoming completely integrated with it. It is a surprisingly invasive process. Although it is known that many women experience abdominal pain and slight bleeding during implantation, the process itself had never been observed before.”

For comparison, the researchers also examined the implantation of mouse blastocysts. In contrast to the complete integration seen for human blastocysts, mouse embryo outgrowth was limited to the matrix surface. In both platforms, initial attachment was followed by invasion and proliferation of trophoblast cells (the outer layer of the blastocyst). The embryo applied strong pulling forces to the fibrous matrix, remodelling the collagen and aligning the fibres around it during implantation. The displacement maps revealed a fluctuating pattern, as seen for the human embryos.

“By measuring the direct impact of the embryo on the matrix scaffold, we reveal the underlying mechanics of embryo implantation,” the researchers write. “We found that mouse and human embryos generated forces during implantation using a species-specific pattern.”

The team is now working to incorporate a theoretical framework to better understand the physical processes underlying implantation. “Our observations at earlier stages show that attachment is a limiting factor at the onset of human embryo implantation,” co-first author Amélie Godeau tells Physics World. “Our next step is to identify the key elements that enable a successful initial connection between the embryo and the matrix.”

The study is reported in Science Advances.

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A new method for generating high-energy proton beams could one day improve the precision of proton therapy for treating cancer. Developed by an international research collaboration headed up at the National University of Singapore, the technique involves accelerating H₂⁺ ions and then using a novel two-dimensional carbon membrane to split the high-energy ion beam into beams of protons.

One obstacle when accelerating large numbers of protons together is that they all carry the same positive charge and thus naturally repel each other. This so-called space–charge effect makes it difficult to keep the beam tight and focused.

“By accelerating H₂⁺ ions instead of single protons, the particles don’t repel each other as strongly,” says project leader Jiong Lu. “This enables delivery of proton beam currents up to an order of magnitude higher than those from existing cyclotrons.”

Lu explains that a high-current proton beam can deliver more protons in a shorter time, making proton treatments quicker, more precise and targeting tumours more effectively. Such a proton beam could also be employed in FLASH therapy, an emerging treatment that delivers therapeutic radiation at ultrahigh dose rates to reduce normal tissue toxicity while preserving anti-tumour activity.

Industry-compatible fabrication

The key to this technique lies in the choice of an optimal membrane with which to split the H₂⁺ ions. For this task, Lu and colleagues developed a new material – ultraclean monolayer amorphous carbon (UC-MAC). MAC is similar in structure to graphene, but instead of an ordered honeycomb structure of hexagonal rings, it contains a disordered mix of five-, six-, seven and eight-membered carbon rings. This disorder creates angstrom-scale pores in the films, which can be used to split the H₂⁺ ions into protons as they pass through.

Scaling the manufacture of ultrathin MAC films, however, has previously proved challenging, with no industrial synthesis method available. To address this problem, the researchers proposed a new fabrication approach in which the emergence of long-range order in the material is suppressed, not by the conventional approach of low-temperature growth, but by a novel disorder-to-disorder (DTD) strategy.

DTD synthesis uses plasma-enhanced chemical vapor deposition (CVD) to create a MAC film on a copper substrate containing numerous nanoscale crystalline grains. This disordered substrate induces high levels of randomized nucleation in the carbon layer and disrupts long-range order. The approach enabled wafer-scale (8-inch) production of UC-MAC films within just 3 s – an order of magnitude faster than conventional CVD methods.

Disorder creates precision

To assess the ability of UC-MAC to split H₂⁺ ions into protons, the researchers generated a high-energy H₂⁺ nanobeam and focused it onto a freestanding two-dimensional UC-MAC crystal. This resulted in the ion beam splitting to create high-precision proton beams. For comparison they repeated the experiment (with beam current stabilities controlled within 10%) using single-crystal graphene, non-clean MAC with metal impurities and commercial carbon thin films (8 nm).

Measuring double-proton events – in which two proton signals are detected from a single H₂⁺ ion splitting – as an indicator for proton scattering revealed that the UC-MAC membrane produced far fewer unwanted scattered protons than the other films. Ion splitting using UC-MAC resulted in about 47 double-proton events over a 20 s collection time, while the graphene film exhibited roughly twice this number and the non-clean MAC slightly more. The carbon thin film generated around 46 times more scattering events.

The researchers point out that the reduced double-proton events in UC-MAC “demonstrate its superior ability to minimize proton scattering compared with commercial materials”. They note that as well as UC-MAC creating a superior quality proton beam, the technique provides control over the splitting rate, with yields ranging from 88.8 to 296.0 proton events per second per detector.

“Using UC-MAC to split H₂⁺ produces a highly sharpened, high-energy proton beam with minimal scattering and high spatial precision,” says Lu. “This allows more precise targeting in proton therapy – particularly for tumours in delicate or critical organs.”

“Building on our achievement of producing proton beams with greatly reduced scattering, our team is now developing single molecule ion reaction platforms based on two-dimensional amorphous materials using high-energy ion nanobeam systems,” he tells Physics World. “Our goal is to make proton beams for cancer therapy even more precise, more affordable and easier to use in clinical settings.”

The study is reported in Nature Nanotechnology.

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