Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks

Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times. Purpose: To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy. Methods: A small convolutional neural network called MODEST is proposed to reconstruct 4D-MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio-temporal information present in 4D-MRI. This network is trained on undersampled 4D-MRI after respiratory binning to reconstruct high-quality 4D-MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D-MRI of 28 lung cancer patients acquired with a T1-weighted golden-angle radial stack-of-stars sequence. The 4D-MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung-liver interface on undersampled 4D-MRI before and after respiratory binning. The network is compared to conventional architectures such as a U-Net, which has 30 times more trainable parameters. Results: MODEST can reconstruct high-quality 4D-MRI with higher image quality than a U-Net, despite a thirty-fold reduction in trainable parameters. High-quality 4D-MRI can be obtained using MODEST in approximately 2.5 minutes, including acquisition, processing, and reconstruction. Conclusion: High-quality accelerated 4D-MRI can be obtained using MODEST, which is particularly interesting for MRI-guided radiotherapy.Comment: Code available at https://gitlab.com/computational-imaging-lab/modes

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods: CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A ``Baseline'' generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an improvement in $\gamma$-pass rate was obtained for DR vs Baseline. Conclusions: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.Comment: Preprint submitted to Physica Medica on 2023-02-16 for review. Also published in Zenodo at https://doi.org/10.5281/zenodo.774264

Radiotherapy Late Effects and Osteogenesis Imperfecta: Dos and Don’ts in Clinical Practice

In radiation oncology, little is known about possible difficulties in patients with Osteogenesis Imperfecta (OI). Because radiotherapy can cause various side-effects including bone, soft tissue and cardiovascular toxicities, we foresee that patients with OI may experience even more acute and late side-effects due to pre-existing problems. We present two cases of radiotherapy in patients with OI, measured the effects of radiation on their bone mineral density and provide clinical recommendations for patient tailored radiotherapy strategies in patients with OI

SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and validation methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in CSV files provided with the datasets. Data format and usage notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.Comment: 15 pages, 4 figures, 9 tables, pre-print submitted to Medical Physics - dataset. The training dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7260705 from April, 1st 202

Immunomodulation Through Low-Dose Radiation for Severe COVID-19:Lessons From the Past and New Developments

Low-dose radiation therapy (LD-RT) has historically been a successful treatment for pneumonia and is clinically established as an immunomodulating therapy for inflammatory diseases. The ongoing COVID-19 pandemic has elicited renewed scientific interest in LD-RT and multiple small clinical trials have recently corroborated the historical LD-RT findings and demonstrated preliminary efficacy and immunomodulation for the treatment of severe COVID-19 pneumonia. The present review explicates archival medical research data of LD-RT and attempts to translate this into modernized evidence, relevant for the COVID-19 crisis. Additionally, we explore the putative mechanisms of LD-RT immunomodulation, revealing specific downregulation of proinflammatory cytokines that are integral to the development of the COVID-19 cytokine storm induced hyperinflammatory state. Radiation exposure in LD-RT is minimal compared to radiotherapy dosing standards in oncology care and direct toxicity and long-term risk for secondary disease are expected to be low. The recent clinical trials investigating LD-RT for COVID-19 confirm initial treatment safety. Based on our findings we conclude that LD-RT could be an important treatment option for COVID-19 patients that are likely to progress to severity. We advocate the further use of LD-RT in carefully monitored experimental environments to validate its effectiveness, risks and mechanisms of LD-RT

Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes

The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments. We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference. The framework is demonstrated in 2D on a motion phantom, and in vivo on volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated by in silico 3D experiments with a digital motion phantom. The framework was compared with template matching - a reference, image-based, method - and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement. The high accuracy in combination with a total latency of less than 10 ms - including data acquisition and processing - make the proposed method a suitable candidate for tracking during STAR treatments

Experimental iodine-125 seed irradiation of intracerebral brain tumors in nude mice

<p>Abstract</p> <p>Background</p> <p>High-dose radiotherapy is standard treatment for patients with brain cancer. However, in preclinical research external beam radiotherapy is limited to heterotopic murine models– high-dose radiotherapy to the murine head is fatal due to radiation toxicity. Therefore, we developed a stereotactic brachytherapy mouse model for high-dose focal irradiation of experimental intracerebral (orthotopic) brain tumors.</p> <p>Methods</p> <p>Twenty-one nude mice received a hollow guide-screw implanted in the skull. After three weeks, 5 × 10⁵U251-NG2 human glioblastoma cells were injected. Five days later, a 2 mCi iodine-125 brachytherapy seed was inserted through the guide-screw in 11 randomly selected mice; 10 mice received a sham seed. Mice were euthanized when severe neurological or physical symptoms occurred. The cumulative irradiation dose 5 mm below the active iodine-125 seeds was 23.0 Gy after 13 weeks (BED_tumor= 30.6 Gy).</p> <p>Results</p> <p>In the sham group, 9/10 animals (90%) showed signs of lethal tumor progression within 6 weeks. In the experimental group, 2/11 mice (18%) died of tumor progression within 13 weeks. Acute side effects in terms of weight loss or neurological symptoms were not observed in the irradiated animals.</p> <p>Conclusion</p> <p>The intracerebral implantation of an iodine-125 brachytherapy seed through a stereotactic guide-screw in the skull of mice with implanted brain tumors resulted in a significantly prolonged survival, caused by high-dose irradiation of the brain tumor that is biologically comparable to high-dose fractionated radiotherapy– without fatal irradiation toxicity. This is an excellent mouse model for testing orthotopic brain tumor therapies in combination with radiation therapy.</p