The value of magnetic resonance imaging for radiotherapy planning

Locally advanced squamous cervical carcinoma (M0): management and emerging therapeutic options in the precision radiotherapy era

Squamous cervical carcinoma (SCC) requires particular attention in diagnostic and clinical management. New diagnostic tools, such as (positron emission tomography-magnetic resonance imaging) PET-MRI, consent to ameliorate clinical staging accuracy. The availability of new technologies in radiation therapy permits to deliver higher dose lowering toxicities. In this clinical scenario, new surgical concepts could aid in general management. Lastly, new targeted therapies and immunotherapy will have more room in this setting. The aim of this narrative review is to focus both on clinical management and new therapies in the precision radiotherapy era.

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

Segmentation of pelvic structures in T2 MRI via MR-to-CT synthesis

Segmentation of multiple pelvic structures in MRI volumes is a prerequisite for many clinical applications, such as sarcopenia assessment, bone density measurement, and muscle-to-fat volume ratio estimation. While many CT-specific datasets and automated CT-based multi-structure pelvis segmentation methods exist, there are few MRI-specific multi-structure segmentation methods in literature. In this pilot work, we propose a lightweight and annotation-free pipeline to synthetically translate T2 MRI volumes of the pelvis to CT, and subsequently leverage an existing CT-only tool called TotalSegmentator to segment 8 pelvic structures in the generated CT volumes. The predicted masks were then mapped back to the original MR volumes as segmentation masks. We compared the predicted masks against the expert annotations of the public TCGA-UCEC dataset and an internal dataset. Experiments demonstrated that the proposed pipeline achieved Dice measures ≥65% for 8 pelvic structures in T2 MRI. The proposed pipeline is an alternative method to obtain multi-organ and structure segmentations without being encumbered by time-consuming manual annotations. By exploiting the significant research progress in CTs, it is possible to extend the proposed pipeline to other MRI sequences in principle. Our research bridges the chasm between the current CT-based multi-structure segmentation and MRI-based segmentation. The manually segmented structures in the TCGA-UCEC dataset are publicly available.

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Purpose

Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning.

Methods and Materials

Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations.

Results

The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively.

Conclusions

Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck

BACKGROUND

Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation.

PURPOSE

The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans.

METHODS

Fourteen H&N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (<250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone (>500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference > 80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric.

RESULTS

The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7 ± 0.3, 1.1 ± 0.1, 1.0 ± 0.1, 0.9 ± 0.1 and 0.8 ± 0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t = 0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences > 80 HU of note were the left and right (L/R) cochlea and mandible (>79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring > 29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5% ± 2% (3 mm, 3%) and 92.7% ± 2.1% (2 mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3% ± 3.0% and 0.7% ± 1.9% respectively.

CONCLUSIONS

In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage.

Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy

Purpose

The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90 min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes.

Methods

Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter.

Results

Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel.

Conclusion

Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.

Implementation of a retrofit MRI simulator for radiation therapy planning

Magnetic resonance imaging (MRI) is being integrated into routine radiation therapy (RT) planning workflows. To reap the benefits of this imaging modality, patient positioning, image acquisition parameters and a quality assurance programme must be considered for accurate use. This paper will report on the implementation of a retrofit MRI Simulator for RT planning, demonstrating an economical, resource efficient solution to improve the accuracy of MRI in this setting.

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Purpose/objectives

An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care.

Materials and method

In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics.

Results

The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs.

Conclusion

This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer

Purpose

To improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized.

Approach

After initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics.

Results

Sequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning.

Conclusions

Our study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network

Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

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 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline.

CONCLUSION

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.

Cone beam computed tomography image guidance within a magnetic resonance imaging-only planning workflow

Background and purpose

Magnetic Resonance Imaging (MRI)-only planning workflows offer many advantages but raises challenges regarding image guidance. The study aimed to assess the viability of MRI to Cone Beam Computed Tomography (CBCT) based image guidance for MRI-only planning treatment workflows.

Materials and methods

An MRI matching training package was developed. Ten radiation therapists, with a range of clinical image guidance experience and experience with MRI, completed the training package prior to matching assessment. The matching assessment was performed on four match regions: prostate gold seed, prostate soft tissue, rectum/anal canal and gynaecological. Each match region consisted of five patients, with three CBCTs per patient, resulting in fifteen CBCTs for each match region. The ten radiation therapists performed the CBCT image matching to CT and to MRI for all regions and recorded the match values.

Results

The median inter-observer variation for MRI-CBCT matching and CT-CBCT matching for all regions were within 2 mm and 1 degree. There was no statistically significant association in the inter-observer variation in mean match values and radiation therapist image guidance experience levels. There was no statistically significant association in inter-observer variation in mean match values for MRI experience levels for prostate soft tissue and gynaecological match regions, while there was a statistically significant difference for prostate gold seed and rectum match regions.

Conclusion

The results of this study support the concept that with focussed training, an MRI to CBCT image guidance approach can be successfully implemented in a clinical planning workflow.

Synthetic computed tomography for low-field magnetic resonance-only radiotherapy in head-and-neck cancer using residual vision transformers

Background and purpose

Synthetic computed tomography (sCT) scans are necessary for dose calculation in magnetic resonance (MR)-only radiotherapy. While deep learning (DL) has shown remarkable performance in generating sCT scans from MR images, research has predominantly focused on high-field MR images. This study presents the first implementation of a DL model for sCT generation in head-and-neck (HN) cancer using low-field MR images. Specifically, the use of vision transformers (ViTs) was explored.

Materials and methods

The dataset consisted of 31 patients, resulting in 196 pairs of deformably-registered computed tomography (dCT) and MR scans. The latter were obtained using a balanced steady-state precession sequence on a 0.35T scanner. Residual ViTs were trained on 2D axial, sagittal, and coronal slices, respectively, and the final sCTs were generated by averaging the models' outputs. Different image similarity metrics, dose volume histogram (DVH) deviations, and gamma analyses were computed on the test set (n = 6). The overlap between auto-contours on sCT scans and manual contours on MR images was evaluated for different organs-at-risk using the Dice score.

Results

The median [range] value of the test mean absolute error was 57 [37-74] HU. DVH deviations were below 1% for all structures. The median gamma passing rates exceeded 94% in the 2%/2mm analysis (threshold = 90%). The median Dice scores were above 0.7 for all organs-at-risk.

Conclusions

The clinical applicability of DL-based sCT generation from low-field MR images in HN cancer was proved. High sCT-dCT similarity and dose metric accuracy were achieved, and sCT suitability for organs-at-risk auto-delineation was shown.

Clinical application of MR-Linac in tumor radiotherapy: a systematic review

Recent years have seen both a fresh knowledge of cancer and impressive advancements in its treatment. However, the clinical treatment paradigm of cancer is still difficult to implement in the twenty-first century due to the rise in its prevalence. Radiotherapy (RT) is a crucial component of cancer treatment that is helpful for almost all cancer types. The accuracy of RT dosage delivery is increasing as a result of the quick development of computer and imaging technology. The use of image-guided radiation (IGRT) has improved cancer outcomes and decreased toxicity. Online adaptive radiotherapy will be made possible by magnetic resonance imaging-guided radiotherapy (MRgRT) using a magnetic resonance linear accelerator (MR-Linac), which will enhance the visibility of malignancies. This review's objectives are to examine the benefits of MR-Linac as a treatment approach from the perspective of various cancer patients' prognoses and to suggest prospective development areas for additional study.

Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience

PURPOSE

Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs.

METHODS AND MATERIALS

An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years.

RESULTS

From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%).

CONCLUSION

Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.

MRI-LINAC: A transformative technology in radiation oncology

Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.

Synthetic Contrasts in Musculoskeletal MRI: A Review

ABSTRACT

This review summarizes the existing techniques and methods used to generate synthetic contrasts from magnetic resonance imaging data focusing on musculoskeletal magnetic resonance imaging. To that end, the different approaches were categorized into 3 different methodological groups: mathematical image transformation, physics-based, and data-driven approaches. Each group is characterized, followed by examples and a brief overview of their clinical validation, if present. Finally, we will discuss the advantages, disadvantages, and caveats of synthetic contrasts, focusing on the preservation of image information, validation, and aspects of the clinical workflow.

Magnetic Resonance-Guided Radiation Therapy for Head and Neck Cancers

Despite the significant evolution of radiation therapy (RT) techniques in recent years, many patients with head and neck cancer still experience significant toxicities during and after treatments. The increased soft tissue contrast and functional sequences of magnetic resonance imaging (MRI) are particularly attractive in head and neck cancer and have led to the increasing development of magnetic resonance-guided RT (MRgRT). This approach refers to the inclusion of the additional information acquired from a diagnostic or planning MRI in radiation treatment planning, and now extends to online high-quality daily imaging generated by the recently developed MR-Linac. MRgRT holds numerous potentials, including enhanced baseline and planning evaluations, anatomical and functional treatment adaptation, potential for hypofractionation, and multiparametric assessment of response. This article offers a structured review of the current literature on these established and upcoming roles of MRI for patients with head and neck cancer undergoing RT.

Evaluation of a New, Highly Flexible Radiofrequency Coil for MR Simulation of Patients Undergoing External Beam Radiation Therapy

PURPOSE

To evaluate the performance of a new, highly flexible radiofrequency (RF) coil system for imaging patients undergoing MR simulation.

METHODS

Volumetric phantom and in vivo images were acquired with a commercially available and prototype RF coil set. Phantom evaluation was performed using a silicone-filled humanoid phantom of the head and shoulders. In vivo assessment was performed in five healthy and six patient subjects. Phantom data included T1-weighted volumetric imaging, while in vivo acquisitions included both T1- and T2-weighted volumetric imaging. Signal to noise ratio (SNR) and uniformity metrics were calculated in the phantom data, while SNR values were calculated in vivo. Statistical significance was tested by means of a non-parametric analysis of variance test.

RESULTS

At a threshold of p = 0.05, differences in measured SNR distributions within the entire phantom volume were statistically different in two of the three paired coil set comparisons. Differences in per slice average SNR between the two coil sets were all statistically significant, as well as differences in per slice image uniformity. For patients, SNRs within the entire imaging volume were statistically significantly different in four of the nine comparisons and seven of the nine comparisons performed on the per slice average SNR values. For healthy subjects, SNRs within the entire imaging volume were statistically significantly different in seven of the nine comparisons and eight of the nine comparisons when per slice average SNR was tested.

CONCLUSIONS

Phantom and in vivo results demonstrate that image quality obtained from the novel flexible RF coil set was similar or improved over the conventional coil system. The results also demonstrate that image quality is impacted by the specific coil configurations used for imaging and should be matched appropriately to the anatomic site imaged to ensure optimal and reproducible image quality.

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network

BACKGROUND

Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation.

PURPOSE

In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning.

METHODS

MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs).

RESULTS

MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively.

CONCLUSIONS

MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients

•

Role of target delineation uncertainties in head and neck cancer patients.

•

Knowing contouring variations for MRI allows better adaptation of MRLinac for H&N cancers.

•

An interobserver variation for GTV among 8 observers was below 2 mm using MRI.

•

Variability between observers might improve using other imaging modalities.

Background

During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC.

Material/methods

32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD).

Results

32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08–0.39 cm) and 0.07 cm (range 0.04–0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001).

Conclusion

Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.

AAPM Task Group Report 303 endorsed by the ABS: MRI Implementation in HDR Brachytherapy-Considerations from Simulation to Treatment

The Task Group (TG) on Magnetic Resonance Imaging (MRI) Implementation in High Dose Rate (HDR) Brachytherapy - Considerations from Simulation to Treatment, TG 303, was constituted by the American Association of Physicists in Medicine's (AAPM's) Science Council under the direction of the Therapy Physics Committee, the Brachytherapy Subcommittee, and the Working Group on Brachytherapy Clinical Applications. The TG was charged with developing recommendations for commissioning, clinical implementation, and on-going quality assurance (QA). Additionally, the TG was charged with describing HDR brachytherapy (BT) workflows and evaluating practical consideration that arise when implementing MR imaging. For brevity, the report is focused on the treatment of gynecologic and prostate cancer. The TG report provides an introduction and rationale for MRI implementation in BT, a review of previous publications on topics including available applicators, clinical trials, previously published BT related TG reports, and new image guided recommendations beyond CT based practices. The report describes MRI protocols and methodologies, including recommendations for the clinical implementation and logical considerations for MR imaging for HDR BT. Given the evolution from prescriptive to risk-based QA,¹ an example of a risk-based analysis using MRI-based, prostate HDR BT is presented. In summary, the TG report is intended to provide clear and comprehensive guidelines and recommendations for commissioning, clinical implementation, and QA for MRI-based HDR BT that may be utilized by the medical physics community to streamline this process. This report is endorsed by the American Brachytherapy Society (ABS). This article is protected by copyright. All rights reserved.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT‐MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT‐MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT‐MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t‐test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT‐MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t‐test showed that the displacements along the z‐axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior‐posterior and superior‐inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t‐test showed significant deviations from zero for C1 on the anterior‐posterior and roll sides. For C2, there was a significant deviation from zero for the left‐right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Validation of an MRI-only planning workflow for definitive pelvic radiotherapy

Purpose

Previous work on Magnetic Resonance Imaging (MRI) only planning has been applied to limited treatment regions with a focus on male anatomy. This research aimed to validate the use of a hybrid multi-atlas synthetic computed tomography (sCT) generation technique from a MRI, using a female and male atlas, for MRI only radiation therapy treatment planning of rectum, anal canal, cervix and endometrial malignancies.

Patients and methods

Forty patients receiving radiation treatment for a range of pelvic malignancies, were separated into male (n = 20) and female (n = 20) cohorts for the creation of gender specific atlases. A multi-atlas local weighted voting method was used to generate a sCT from a T1-weighted VIBE DIXON MRI sequence. The original treatment plans were copied from the CT scan to the corresponding sCT for dosimetric validation.

Results

The median percentage dose difference between the treatment plan on the CT and sCT at the ICRU reference point for the male cohort was − 0.4% (IQR of 0 to − 0.6), and − 0.3% (IQR of 0 to − 0.6) for the female cohort. The mean gamma agreement for both cohorts was > 99% for criteria of 3%/2 mm and 2%/2 mm. With dose criteria of 1%/1 mm, the pass rate was higher for the male cohort at 96.3% than the female cohort at 93.4%. MRI to sCT anatomical agreement for bone and body delineated contours was assessed, with a resulting Dice score of 0.91 ± 0.2 (mean ± 1 SD) and 0.97 ± 0.0 for the male cohort respectively; and 0.96 ± 0.0 and 0.98 ± 0.0 for the female cohort respectively. The mean absolute error in Hounsfield units (HUs) within the entire body for the male and female cohorts was 59.1 HU ± 7.2 HU and 53.3 HU ± 8.9 HU respectively.

Conclusions

A multi-atlas based method for sCT generation can be applied to a standard T1-weighted MRI sequence for male and female pelvic patients. The implications of this study support MRI only planning being applied more broadly for both male and female pelvic sites.

Trial registration This trial was registered in the Australian New Zealand Clinical Trials Registry (ANZCTR) (www.anzctr.org.au) on 04/10/2017. Trial identifier ACTRN12617001406392.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02023-4.

Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma

Purpose

Tumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation.

Methods

Ten patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1W_PP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1W_DP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated.

Results

Fused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1W_PP, T2W, DWI, T1W_DP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1W_PP, T2W, DWI, T1W_DP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1W_PP, T2W, DWI, T1W_DP, and fused MRI, respectively.

Conclusion

The results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1W_PP, T1W_DP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.

A narrative review of MRI acquisition for MR-guided-radiotherapy in prostate cancer

Magnetic resonance guided radiotherapy (MRgRT), enabled by the clinical introduction of the integrated MRI and linear accelerator (MR-LINAC), is a novel technique for prostate cancer (PCa) treatment, promising to further improve clinical outcome and reduce toxicity. The role of prostate MRI has been greatly expanded from the traditional PCa diagnosis to also PCa screening, treatment and surveillance. Diagnostic prostate MRI has been relatively familiar in the community, particularly with the development of Prostate Imaging - Reporting and Data System (PI-RADS). But, on the other hand, the use of MRI in the emerging clinical practice of PCa MRgRT, which is substantially different from that in PCa diagnosis, has been so far sparsely presented in the medical literature. This review attempts to give a comprehensive overview of MRI acquisition techniques currently used in the clinical workflows of PCa MRgRT, from treatment planning to online treatment guidance, in order to promote MRI practice and research for PCa MRgRT. In particular, the major differences in the MRI acquisition of PCa MRgRT from that of diagnostic prostate MRI are demonstrated and explained. Limitations in the current MRI acquisition for PCa MRgRT are analyzed. The future developments of MRI in the PCa MRgRT are also discussed.

MR-guided SBRT boost for patients with locally advanced or recurrent gynecological cancers ineligible for brachytherapy: feasibility and early clinical experience

Background and purpose

Chemoradiotherapy (CRT) followed by a brachytherapy (BT) boost is the standard of care for patients with locally advanced or recurrent gynecological cancer (LARGC). However, not every patient is suitable for BT. Therefore, we investigated the feasibility of an MR-guided SBRT boost (MRg-SBRT boost) following CRT of the pelvis.

Material and methods

Ten patients with LARGC were analyzed retrospectively. The patients were not suitable for BT due to extensive infiltration of the pelvic wall (10%), other adjacent organs (30%), or both (50%), or ineligibility for anesthesia (10%). Online-adaptive treatment planning was performed to control for interfractional anatomical changes. Treatment parameters and toxicity were evaluated to assess the feasibility of MRg-SBRT boost.

Results

MRg-SBRT boost was delivered to a median total dose of 21.0 Gy in 4 fractions. The median optimized PTV (PTV_opt) size was 43.5ccm. The median cumulative dose of 73.6Gy₁₀ was delivered to PTV_opt. The cumulative median D2ccm of the rectum was 63.7 Gy; bladder 72.2 Gy; sigmoid 65.8 Gy; bowel 59.9 Gy (EQD2₃). The median overall treatment time/fraction was 77 min, including the adaptive workflow in 100% of fractions. The median duration of the entire treatment was 50 days. After a median follow-up of 9 months, we observed no CTCAE ≥ °II toxicities.

Conclusion

These early results report the feasibility of an MRg-SBRT boost approach in patients with LARGC, who were not candidates for BT. When classical BT-OAR constraints are followed, the therapy was well tolerated. Long-term follow-up is needed to validate the results.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Simple Summary

MRI-only simulation in radiation therapy (RT) planning has received attention because the CT scan can be omitted. For MRI-only simulation, synthetic CT (sCT) is necessary for the dose calculation. Various methodologies have been suggested for the generation of sCT and, recently, methods using the deep learning approaches are actively investigated. GAN and cycle-consistent GAN (CycGAN) have been mainly tested, however, very limited studies compared the qualities of sCTs generated from these methods or suggested other models for sCT generation. We have compared GAN, CycGAN, and, reference-guided GAN (RgGAN), a new model of deep learning method. We found that the performance in the HU conservation for soft tissue was poorest for GAN. All methods could generate sCTs feasible for VMAT planning with the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D_98% and D_95% than sCTs from other methodologies.

Abstract

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D_98% and D_95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D_98% and D_95% than sCTs from other methodologies.

Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks

Purpose: To enable real‐time adaptive magnetic resonance imaging–guided radiotherapy (MRIgRT) by obtaining time‐resolved three‐dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency (<500 ms).

Theory and Methods: Respiratory‐resolved T1‐weighted 4D‐MRI of 27 patients with lung cancer were acquired using a golden‐angle radial stack‐of‐stars readout. A multiresolution convolutional neural network (CNN) called TEMPEST was trained on up to 32× retrospectively undersampled MRI of 17 patients, reconstructed with a nonuniform fast Fourier transform, to learn optical flow DVFs. TEMPEST was validated using 4D respiratory‐resolved MRI, a digital phantom, and a physical motion phantom. The time‐resolved motion estimation was evaluated in‐vivo using two volunteer scans, acquired on a hybrid MR‐scanner with integrated linear accelerator. Finally, we evaluated the model robustness on a publicly‐available four‐dimensional computed tomography (4D‐CT) dataset.

Results: TEMPEST produced accurate DVFs on respiratory‐resolved MRI at 20‐fold acceleration, with the average end‐point‐error <2 mm, both on respiratory‐sorted MRI and on a digital phantom. TEMPEST estimated accurate time‐resolved DVFs on MRI of a motion phantom, with an error <2 mm at 28× undersampling. On two volunteer scans, TEMPEST accurately estimated motion compared to the self‐navigation signal using 50 spokes per dynamic (366× undersampling). At this undersampling factor, DVFs were estimated within 200 ms, including MRI acquisition. On fully sampled CT data, we achieved a target registration error of 1.87±1.65 mm without retraining the model.

Conclusion: A CNN trained on undersampled MRI produced accurate 3D DVFs with high spatiotemporal resolution for MRIgRT.

FDG-PET/CT and MR imaging for target volume delineation in rectal cancer radiotherapy treatment planning: a systematic review

Aim:

The aim of this systematic review was to synthesise and summarise evidence surrounding the clinical use of fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) for target volume delineation (TVD) in rectal cancer radiotherapy planning.

Methods:

PubMed, EMBASE, Cochrane library, CINAHL, Web of Science and Scopus databases and other sources were systematically queried using keywords and relevant synonyms. Eligible full-text studies were assessed for methodological quality using the QUADAS-2 tool.

Results:

Eight of the 1448 studies identified met the inclusion criteria. Findings showed that MRI significantly delineate larger tumour volumes (TVs) than FDG-PET/CT while diffusion-weighted magnetic resonance imaging (DW-MRI) defined smaller gross tumour volumes (GTVs) compared to T2 weighted-Magnetic Resonance Image. CT-based GTVs were found to be larger compared to FDG-PET/CT. FDG-PET/CT also identified new lesions in 15–17% patients and TVs extending outside the routinely used clinical standard CT TV in 29–83% patients. Between observers, delineated volumes were similar and consistent between MRI sequences, whereas interobserver agreement was significantly improved with FDG-PET/CT than CT.

Conclusion:

FDG-PET/CT and DW-MRI appear to delineate smaller rectal TVs and show improved interobserver variability. Overall, this study provides valuable insights into the amount of attention in the research literature that has been paid to imaging for TVD in rectal cancer.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

PURPOSE

In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated.

METHODS

41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( C B C T RS ) and a population-based dose calculation method ( C B C T Pop ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs.

RESULTS

The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2 mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0  ±  0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0  ±  0.8% and 99.1  ±  0.8% for the C B C T RS and C B C T Pop , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4  ±  1.6% and 99.2  ±  0.6% for C B C T RS and C B C T Pop , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%.

CONCLUSION

The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the C B C T RS approach in an MR only workflow.

Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets

In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow. In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality. A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.

3D-printed saw guides for lower arm osteotomy, a comparison between a synthetic CT and CT-based workflow

BACKGROUND

Three-dimensional (3D)-printed saw guides are frequently used to optimize osteotomy results and are usually designed based on computed tomography (CT), despite the radiation burden, as radiation-less alternatives like magnetic resonance imaging (MRI) have inferior bone visualization capabilities. This study investigated the usability of MR-based synthetic-CT (sCT), a novel radiation-less bone visualization technique for 3D planning and design of patient-specific saw guides.

METHODS

Eight human cadaveric lower arms (mean age: 78y) received MRI and CT scans as well as high-resolution micro-CT. From the MRI scans, sCT were generated using a conditional generative adversarial network. Digital 3D bone surface models based on the sCT and general CT were compared to the surface model from the micro-CT that was used as ground truth for image resolution. From both the sCT and CT digital bone models saw guides were designed and 3D-printed in nylon for one proximal and one distal bone position for each radius and ulna. Six blinded observers placed these saw guides as accurately as possible on dissected bones. The position of each guide was assessed by optical 3D-scanning of each bone with positioned saw guide and compared to the preplanning. Eight placement errors were evaluated: three translational errors (along each axis), three rotational errors (around each axis), a total translation (∆T) and a total rotation error (∆R).

RESULTS

Surface models derived from micro-CT were on average smaller than sCT and CT-based models with average differences of 0.27 ± 0.30 mm for sCT and 0.24 ± 0.12 mm for CT. No statistically significant positioning differences on the bones were found between sCT- and CT-based saw guides for any axis specific translational or rotational errors nor between the ∆T (p = .284) and ∆R (p = .216). On Bland-Altman plots, the ∆T and ∆R limits of agreement (LoA) were within the inter-observer variability LoA.

CONCLUSIONS

This research showed a similar error for sCT and CT digital surface models when comparing to ground truth micro-CT models. Additionally, the saw guide study showed equivalent CT- and sCT-based saw guide placement errors. Therefore, MRI-based synthetic CT is a promising radiation-less alternative to CT for the creation of patient-specific osteotomy surgical saw guides.

Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images

Cross-modality medical image synthesis between magnetic resonance (MR) images and computed tomography (CT) images has attracted increasing attention in many medical imaging area. Many deep learning methods have been used to generate pseudo-MR/CT images from counterpart modality images. In this study, we used U-Net and Cycle-Consistent Adversarial Networks (CycleGAN), which were typical networks of supervised and unsupervised deep learning methods, respectively, to transform MR/CT images to their counterpart modality. Experimental results show that synthetic images predicted by the proposed U-Net method got lower mean absolute error (MAE), higher structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) in both directions of CT/MR synthesis, especially in synthetic CT image generation. Though synthetic images by the U-Net method has less contrast information than those by the CycleGAN method, the pixel value profile tendency of the synthetic images by the U-Net method is closer to the ground truth images. This work demonstrated that supervised deep learning method outperforms unsupervised deep learning method in accuracy for medical tasks of MR/CT synthesis.

Cross-modality deep learning: Contouring of MRI data from annotated CT data only

PURPOSE

Online adaptive radiotherapy would greatly benefit from the development of reliable auto-segmentation algorithms for organs-at-risk and radiation targets. Current practice of manual segmentation is subjective and time-consuming. While deep learning-based algorithms offer ample opportunities to solve this problem, they typically require large datasets. However, medical imaging data are generally sparse, in particular annotated MR images for radiotherapy. In this study, we developed a method to exploit the wealth of publicly available, annotated CT images to generate synthetic MR images, which could then be used to train a convolutional neural network (CNN) to segment the parotid glands on MR images of head and neck cancer patients.

METHODS

Imaging data comprised 202 annotated CT and 27 annotated MR images. The unpaired CT and MR images were fed into a 2D CycleGAN network to generate synthetic MR images from the CT images. Annotations of axial slices of the synthetic images were generated by propagating the CT contours. These were then used to train a 2D CNN. We assessed the segmentation accuracy using the real MR images as test dataset. The accuracy was quantified with the 3D Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) between manual and auto-generated contours. We benchmarked the approach by a comparison to the interobserver variation determined for the real MR images, as well as to the accuracy when training the 2D CNN to segment the CT images.

RESULTS

The determined accuracy (DSC: 0.77±0.07, HD: 18.04±12.59mm, MSD: 2.51±1.47mm) was close to the interobserver variation (DSC: 0.84±0.06, HD: 10.85±5.74mm, MSD: 1.50±0.77mm), as well as to the accuracy when training the 2D CNN to segment the CT images (DSC: 0.81±0.07, HD: 13.00±7.61mm, MSD: 1.87±0.84mm).

CONCLUSIONS

The introduced cross-modality learning technique can be of great value for segmentation problems with sparse training data. We anticipate using this method with any nonannotated MRI dataset to generate annotated synthetic MR images of the same type via image style transfer from annotated CT images. Furthermore, as this technique allows for fast adaptation of annotated datasets from one imaging modality to another, it could prove useful for translating between large varieties of MRI contrasts due to differences in imaging protocols within and between institutions.

Visualizing the urethra by magnetic resonance imaging without usage of a catheter for radiotherapy of prostate cancer

•

Post urination MRI is useful for urethra-sparing radiotherapy treatment planning.

•

This prospective clinical trial included 11 prostate cancer patients.

•

Post urination MRI is the identification method of prostatic urinary tract in non-invasive manner.

The urethra position may shift due to the presence/absence of the catheter. Our proposed post-urination-magnetic resonance imaging (PU-MRI) technique is possible to identify the urethra without catheter. We aimed to verify the inter-operator difference in contouring the urethra by PU-MRI. The mean values of the evaluation indices of dice similarity coefficient, mean slice-wise Hausdorff distance, and center coordinates were 0.93, 0.17 mm, and 0.36 mm for computed tomography, and 0.75, 0.44 mm, and 1.00 mm for PU-MRI. Therefore, PU-MRI might be useful for identifying the prostatic urinary tract without using a urethral catheter.

Clinical trial registration: Hokkaido University Hospital for Clinical Research (018-0221).

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept

OBJECTIVE

Computed tomography scanning of the lumbar spine incurs a radiation dose ranging from 3.5 mSv to 19.5 mSv as well as relevant costs and is commonly necessary for spinal neuronavigation. Mitigation of the need for treatment-planning CT scans in the presence of MRI facilitated by MRI-based synthetic CT (sCT) would revolutionize navigated lumbar spine surgery. The authors aim to demonstrate, as a proof of concept, the capability of deep learning-based generation of sCT scans from MRI of the lumbar spine in 3 cases and to evaluate the potential of sCT for surgical planning.

METHODS

Synthetic CT reconstructions were made using a prototype version of the "BoneMRI" software. This deep learning-based image synthesis method relies on a convolutional neural network trained on paired MRI-CT data. A specific but generally available 4-minute 3D radiofrequency-spoiled T1-weighted multiple gradient echo MRI sequence was supplemented to a 1.5T lumbar spine MRI acquisition protocol.

RESULTS

In the 3 presented cases, the prototype sCT method allowed voxel-wise radiodensity estimation from MRI, resulting in qualitatively adequate CT images of the lumbar spine based on visual inspection. Normal as well as pathological structures were reliably visualized. In the first case, in which a spiral CT scan was available as a control, a volume CT dose index (CTDIvol) of 12.9 mGy could thus have been avoided. Pedicle screw trajectories and screw thickness were estimable based on sCT findings.

CONCLUSIONS

The evaluated prototype BoneMRI method enables generation of sCT scans from MRI images with only minor changes in the acquisition protocol, with a potential to reduce workflow complexity, radiation exposure, and costs. The quality of the generated CT scans was adequate based on visual inspection and could potentially be used for surgical planning, intraoperative neuronavigation, or for diagnostic purposes in an adjunctive manner.

MRI-based Synthetic CT in the Detection of Structural Lesions in Patients with Suspected Sacroiliitis: Comparison with MRI

Background Evaluation of structural lesions in the sacroiliac (SI) joints can improve the accuracy for diagnosis of spondyloarthritis. However, structural lesions, such as erosions, are difficult to assess on routine T1-weighted MRI scans. Purpose To determine the diagnostic performance of MRI-based synthetic CT (sCT) in the depiction of erosions, sclerosis, and ankylosis of the SI joints compared with T1-weighted MRI, with CT as the reference standard. Materials and Methods A prospective study (clinical trial registration no. B670201837885) was performed from February 2019 to November 2019. Adults were referred from a tertiary hospital rheumatology outpatient clinic with clinical suspicion of inflammatory sacroiliitis. MRI and CT of the SI joints were performed on the same day. SCT images were generated from MRI scans using a commercially available deep learning-based image synthesis method. Two readers independently recorded if structural lesions (erosions, sclerosis, and ankylosis) were present on T1-weighted MRI, sCT, and CT scans in different reading sessions, with readers blinded to clinical information and other images. Diagnostic performance of sCT and T1-weighted MRI scans were analyzed using generalized estimating equation models, with consensus results of CT as the reference standard. Results Thirty participants were included (16 men, 14 women; mean age, 40 years ± 10 [standard deviation]). Diagnostic accuracy of sCT was higher than that of T1-weighted MRI for erosion (94% vs 86%, P = .003), sclerosis (97% vs 81%, P < .001), and ankylosis (92% vs 84%, P = .04). With sCT, specificity for erosion detection (96% [95% CI: 90, 98] vs 89% [95% CI: 81, 94], P = .01] and sensitivity for detection of sclerosis [94% [95% CI: 87, 97] vs 20% [95% CI: 10, 35], P < .001] and ankylosis (93% [95% CI: 78, 98] vs 70% [95% CI: 47, 87], P = .001) were improved. Conclusion With CT as the reference standard, synthetic CT of the sacroiliac joints has better diagnostic performance in the detection of structural lesions in individuals suspected of having sacroiliitis compared with routine T1-weighted MRI. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Fritz in this issue.

Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas

In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD_2%, ΔD_98%, and ΔD_mean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD_2%≥0.3%, ΔD_98%≥15.9%, ΔD_mean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD_2%≤0.4%, ΔD_98%≤5.6%, ΔD_mean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.

Using magnetic resonance imaging to measure the depth of acupotomy points in the lumbar spine: A retrospective study

Background

The acupotomy is an acupuncture device recently used to stimulate lumbar vertebrae such as transverse processes (TPs) and facet joints (FJs). However, there are many organs, nerves, and blood vessels, which can lead to side effects if the needle misses the treatment target. Therefore, information regarding appropriate insertion depths, which is currently lacking, could facilitate its safe use. We retrospectively investigated the depth from the skin to the TP and FJ of the lumbar vertebrae, using magnetic resonance imaging (MRI).

Methods

This retrospective chart review was conducted at a single medical centre in Korea. From 55,129 patient records, 158 subjects were selected. Perpendicular depth from the skin to the left and right TPs and FJs was measured using T1-weighted sagittal plane MRI. Depth differences between the left and right sides were evaluated using the paired t-test and analysis of covariance (body mass index [BMI] as a covariate). The influence of BMI on depth at each location was evaluated by simple linear regression analysis.

Results

The mean age was 43.2 years and mean BMI was 23.6 kg/m². The depth from skin to the TPs or FJs was unaffected by age, sex, or side. Mean depths (cm) were as follows: (TPs) L1 = 4.5, L2 = 4.9, L3 = 5.3, L4 = 5.7, L5 = 5.9; (FJs) L12 = 3.8, L23 = 4.0, L34 = 4.4, L45 = 4.6, L5S1 = 4.6. Depth was highly correlated with BMI at each location.

Conclusion

The depth of TPs and FJs adjusted for BMI can safely and effectively be used for treatment via various invasive interventions, including acupotomy treatment, in the lumbar region.

Patient-Reported Tolerance of Magnetic Resonance-Guided Radiation Therapy

Purpose

Magnetic resonance-guided radiation therapy (MRgRT) has been incorporated into a growing number of clinical practices world-wide, however, there is limited data on patient experiences with MRgRT. The purpose of this study was to prospectively evaluate patient tolerance of MRgRT using patient reported outcome questionnaires (PRO-Q).

Methods

Ninety patients were enrolled in this prospective observational study and treated with MRgRT (MRIdian Linac System, ViewRay Inc. Oakwood Village, OH, United States) between September 2018 and September 2019. Breath-hold-gated dose delivery with audiovisual feedback was completed as needed. Patients completed an in-house developed PRO-Q after the first and last fraction of MRgRT.

Results

The most commonly treated anatomic sites were the abdomen (47%) and pelvis (33%). Respiratory gating was utilized in 62% of the patients. Patients rated their experience as positive or at least tolerable with mean scores of 1.0–2.8. The most common complaint was the temperature in the room (61%) followed by paresthesias (57%). The degree of anxiety reported by 45% of the patients significantly decreased at the completion of treatment (mean score 1.54 vs. 1.36, p = 0.01). Forty-three percent of the patients reported some degree of disturbing noise which was improved considerably by use of music. All patients appreciated their active role during the treatment.

Conclusion

This evaluation of PROs indicates that MRgRT was well-tolerated by our patients. Patients’ experience may further improve with adjustment of room temperature and noise reduction.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Gross tumour volume delineation in anal cancer on T2-weighted and diffusion-weighted MRI - Reproducibility between radiologists and radiation oncologists and impact of reader experience level and DWI image quality

PURPOSE

To assess how gross tumour volume (GTV) delineation in anal cancer is affected by interobserver variations between radiologists and radiation oncologists, expertise level, and use of T2-weighted MRI (T2W-MRI) vs. diffusion-weighted imaging (DWI), and to explore effects of DWI quality.

METHODS AND MATERIALS

We retrospectively analyzed the MRIs (T2W-MRI and b800-DWI) of 25 anal cancer patients. Four readers (Senior and Junior Radiologist; Senior and Junior Radiation Oncologist) independently delineated GTVs, first on T2W-MRI only and then on DWI (with reference to T2W-MRI). Maximum Tumour Diameter (MTD) was calculated from each GTV. Mean GTVs/MTDs were compared between readers and between T2W-MRI vs. DWI. Interobserver agreement was calculated as Intraclass Correlation Coefficient (ICC), Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). DWI image quality was assessed using a 5-point artefact scale.

RESULTS

Interobserver agreement between radiologists vs. radiation oncologists and between junior vs. senior readers was good-excellent, with similar agreement for T2W-MRI and DWI (e.g. ICCs 0.72-0.94 for T2W-MRI and 0.68-0.89 for DWI). There was a trend towards smaller GTVs on DWI, but only for the radiologists (P = 0.03-0.07). Moderate-severe DWI-artefacts were observed in 11/25 (44%) cases. Agreement tended to be lower in these cases.

CONCLUSION

Overall interobserver agreement for anal cancer GTV delineation on MRI is good for both radiologists and radiation oncologists, regardless of experience level. Use of DWI did not improve agreement. DWI artefacts affecting GTV delineation occurred in almost half of the patients, which may severely limit the use of DWI for radiotherapy planning if no steps are undertaken to avoid them.

J. Kotte AN, Bol GH, T. van den Berg CA. Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy

BACKGROUND

Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT).

PURPOSE

In this study, we investigate the feasibility of clinical use of deep learning-based automatic OARs delineation on MRI.

MATERIALS AND METHODS

We included 150 patients diagnosed with prostate cancer who underwent MR-only radiotherapy. A three-dimensional (3D) T1-weighted dual spoiled gradient-recalled echo sequence was acquired with 3T MRI for the generation of the synthetic-CT. The first 48 patients were included in a feasibility study training two 3D convolutional networks called DeepMedic and dense V-net (dV-net) to segment bladder, rectum and femurs. A research version of an atlas-based software was considered for comparison. Dice similarity coefficient, 95% Hausdorff distances (HD95), and mean distances were calculated against clinical delineations. For eight patients, an expert RTT scored the quality of the contouring for all the three methods. A choice among the three approaches was made, and the chosen approach was retrained on 97 patients and implemented for automatic use in the clinical workflow. For the successive 53 patients, Dice, HD95 and mean distances were calculated against the clinically used delineations.

RESULTS

DeepMedic, dV-net and the atlas-based software generated contours in 60 s, 4 s and 10-15 min, respectively. Performances were higher for both the networks compared to the atlas-based software. The qualitative analysis demonstrated that delineation from DeepMedic required fewer adaptations, followed by dV-net and the atlas-based software. DeepMedic was clinically implemented. After retraining DeepMedic and testing on the successive patients, the performances slightly improved.

CONCLUSION

High conformality for OARs delineation was achieved with two in-house trained networks, obtaining a significant speed-up of the delineation procedure. Comparison of different approaches has been performed leading to the succesful adoption of one of the neural networks, DeepMedic, in the clinical workflow. DeepMedic maintained in a clinical setting the accuracy obtained in the feasibility study.