David E. J. Waddington

The Australian Magnetic Resonance Imaging (MRI) Linear Accelerator program (MRI linac) was a major research project that aimed to build and test a unique MRI linac prototype for cancer treatment. It aimed to improve radiotherapy anatomical targeting and explore physiological targeting. The purpose of this report is to summarise the development and achievements of the program so as to provide an example of a successful large‐scale research project in Australian radiation oncology. The project involved six Australian universities and international collaborators. We developed and built a unique MRI linac configuration comprising a 1 T magnetic field and a 6 MV accelerator, with the beam delivered in line with B ₀ and the patient placed across B ₀ in the split between the two halves of the magnet. The broad research domains were personalised disease targeting, medical device innovation, and biodiscovery. Specific projects included Artificial Intelligence image enhancement, radiation dosimetry in high magnetic fields, MRI characterisation of cancer heterogeneity in human tumours, and animal and human studies. Over $27 million was obtained to support the program from competitive sources. The program published over 120 papers and supported 25 PhD completions. The learnings from the Australian MRI linac program are that Australia has world‐class radiotherapy research in physics and engineering, that major projects need a lot of time and a lot of collaboration, and that large, novel radiotherapy projects can attract significant funding and produce significant results.

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Medicine is evolving beyond therapy largely predicated on anatomical information and towards incorporating patient-specific molecular biomarkers of disease for more accurate diagnosis and effective treatment. The complementary combination of hyperpolarization by spin-lock induced crossing signal amplification by reversible exchange (SLIC SABRE) and low field magnetic resonance imaging (MRI) can enable accessible metabolic imaging to advance personalized medicine. Hyperpolarized ¹³C-enriched pyruvate has demonstrated promise for imaging metabolism in cancer, heart disease and neurodegenerative disorders; however, broader clinical adoption awaits validated clinical indications, and is further constrained by the cost and limited availability of current hyperpolarization technology. Parahydrogen-based polarization techniques, paired with low-cost high-performance MRI at millitesla fields, offer a means of broadening the reach of metabolic imaging. Here we show results demonstrating in situ hyperpolarization of pyruvate at 6.5 mT by SLIC SABRE, followed by immediate readout without field cycling or sample shuttling. We achieve ¹³C signal enhancements several million times above thermal equilibrium at 6.5 mT, corresponding to polarization levels of approximately 3%. Leveraging this enhancement, we perform ¹³C MRI and acquire NMR spectra with resolution sufficient to distinguish chemical shifts between pyruvate isotopomers. These results show a viable pathway towards accessible metabolic imaging with hyperpolarized ¹³C MRI at ultra-low field.

Detecting glioblastoma infiltration in the brain is challenging due to limited MRI contrast beyond the enhancing tumour core. This study aims to investigate the potential of superparamagnetic iron oxide nanoparticles (SPIONs) as contrast agents for improved detection of diffuse brain cancer. We examine the distribution and pharmacokinetics of SPIONs in glioblastoma models with intact and disrupted blood-brain barriers. Using MRI, we imaged RN1-luc and U87MG mice injected with Gadovist and SPIONs, observing differences in blood-brain barrier permeability. Peripheral imaging showed strong uptake of nanoparticles in the liver and spleen, while vascular and renal signals were transient. Susceptibility gradient mapping enabled positive nanoparticle contrast within tumours and provided additional information on tumour angiogenesis. This approach offers a novel method for detecting diffuse brain cancer. Our findings demonstrate that SPIONs enhance glioblastoma detection beyond conventional MRI, providing insights into tumour angiogenesis and opening new avenues for early diagnosis and targeted treatment strategies.

Detecting glioblastoma infiltration in the brain is challenging due to limited MRI contrast beyond the enhancing tumour core. This study aims to investigate the potential of superparamagnetic iron oxide nanoparticles (SPIONs) as contrast agents for improved detection of diffuse brain cancer. We examine the distribution and pharmacokinetics of SPIONs in glioblastoma models with intact and disrupted blood-brain barriers. Using MRI, we imaged RN1-luc and U87MG mice injected with Gadovist and SPIONs, observing differences in blood-brain barrier permeability. Peripheral imaging showed strong uptake of nanoparticles in the liver and spleen, while vascular and renal signals were transient. Susceptibility gradient mapping enabled positive nanoparticle contrast within tumours and provided additional information on tumour angiogenesis. This approach offers a novel method for detecting diffuse brain cancer. Our findings demonstrate that SPIONs enhance glioblastoma detection beyond conventional MRI, providing insights into tumour angiogenesis and opening new avenues for early diagnosis and targeted treatment strategies.

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B ₀ field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRI-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset (3300 images) from eleven healthy volunteers for both fully sampled and subsampled acquisitions. 768 grid phantom and 12 human brain images acquired from an open-bore 1T MRI-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSIM), residual distortions, and computation time. Imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (0.92) at four-time acceleration for simulated brain images. The RebinNet preserved more image details and substantially increased the computational efficiency (3s, ten-fold faster) compared to the conventional regularization methods (30s), and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B ₀ inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments.

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRI-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset from eleven healthy volunteers for both fully sampled and subsampled acquisitions. Grid phantom and human brain images acquired from an open-bore 1T MRI-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSIM), residual distortions, and computation time. Imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (0.92) at four-time acceleration for simulated brain images. The RebinNet could better preserve structural details and substantially improve the computational efficiency (ten-fold faster) compared to the conventional regularization methods, and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B0 inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments.

The aim of this prospective clinical study was to evaluate the potential of the prostate specific membrane antigen (PSMA) targeting ligand, [⁶⁸Ga]-PSMA–Glu–NH–CO–NH–Lys-2-naphthyl-L-Ala-cyclohexane-DOTA ([⁶⁸Ga]Ga-PSMA-617) as a positron emission tomography (PET) imaging biomarker in recurrent glioblastoma patients. Patients underwent [⁶⁸Ga]Ga-PSMA-617 and O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET scans on two separate days. [⁶⁸Ga]Ga-PSMA-617 tumour selectivity was assessed by comparing tumour volume delineation and by assessing the intra-patient correlation between tumour uptake on [⁶⁸Ga]Ga-PSMA-617 and [¹⁸F]FET PET images. [⁶⁸Ga]Ga-PSMA-617 tumour specificity was evaluated by comparing its tumour-to-brain ratio (TBR) with [¹⁸F]FET TBR and its tumour volume with the magnetic resonance imaging (MRI) contrast-enhancing (CE) tumour volume. Ten patients were recruited in this study. [⁶⁸Ga]Ga-PSMA-617-avid tumour volume was larger than the [¹⁸F]FET tumour volume (p = 0.063). There was a positive intra-patient correlation (median Pearson r = 0.51; p < 0.0001) between [⁶⁸Ga]Ga-PSMA-617 and [¹⁸F]FET in the tumour volume. [⁶⁸Ga]Ga-PSMA-617 had significantly higher TBR (p = 0.002) than [¹⁸F]FET. The [⁶⁸Ga]Ga-PSMA-617-avid tumour volume was larger than the CE tumour volume (p = 0.0039). Overall, accumulation of [⁶⁸Ga]-Ga-PSMA-617 beyond [¹⁸F]FET-avid tumour regions suggests the presence of neoangiogenesis in tumour regions that are not overly metabolically active yet. Higher tumour specificity suggests that [⁶⁸Ga]-Ga-PSMA-617 could be a better imaging biomarker for recurrent tumour delineation and secondary treatment planning than [¹⁸F]FET and CE MRI.

Background: MRI‐guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold‐standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real‐time adaptation.Purpose: Once trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep‐learning‐based image reconstruction for real‐time tracking applications on MRI‐Linacs.Methods: We use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k‐space data. The AUTOMAP neural network was trained to reconstruct images from a golden‐angle radial acquisition, a benchmark for motion‐sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion‐encoded k‐space data derived from videos in the YouTube‐8M dataset to encourage motion robust reconstruction.Results: AUTOMAP models fine‐tuned on retrospectively acquired lung cancer patient data reconstructed radial k‐space with equivalent accuracy to CS but with much shorter processing times. Validation of motion‐trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy.Conclusion: AUTOMAP can achieve real‐time, accurate reconstruction of radial data. These findings imply that neural‐network‐based reconstruction is potentially superior to alternative approaches for real‐time image guidance applications.