MRI-guided Radiotherapy Research Articles

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

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

Electron stream effect in 0.35 Tesla magnetic resonance image guided radiotherapy for breast cancer.

This research aimed to analyze electron stream effect (ESE) during magnetic resonance image guided radiotherapy (MRgRT) for breast cancer patients on a MR-Linac (0.35 Tesla, 6MV), with a focus on the prevention of redundant radiation exposure. RANDO phantom was used with and without the breast attachment in order to represent the patients after breast conserving surgery (BCS) and those received modified radical mastectomy (MRM). The prescription dose is 40.05 Gy in fifteen fractions for whole breast irradiation (WBI) or 20 Gy single shot for partial breast irradiation (PBI). Thirteen different portals of intensity-modulated radiation therapy were created. And then we evaluated dose distribution in five areas (on the skin of the tip of the nose, the chin, the neck, the abdomen and the thyroid.) outside of the irradiated field with and without 0.35 Tesla. In addition, we added a piece of bolus with the thickness of 1cm on the skin in order to compare the ESE difference with and without a bolus. Lastly, we loaded two patients' images for PBI comparison. We found that 0.35 Tesla caused redundant doses to the skin of the chin and the neck as high as 9.79% and 5.59% of the prescription dose in the BCS RANDO model, respectively. For RANDO phantom without the breast accessory (simulating MRM), the maximal dose increase were 8.71% and 4.67% of the prescription dose to the skin of the chin and the neck, respectively. Furthermore, the bolus we added efficiently decrease the unnecessary dose caused by ESE up to 59.8%. We report the first physical investigation on successful avoidance of superfluous doses on a 0.35T MR-Linac for breast cancer patients. Future studies of MRgRT on the individual body shape and its association with ESE influence is warranted.

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation

Background and purposePrevious studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. Materials and methodsThe proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network’s outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. ResultsThe proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. ConclusionTo the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method

PurposeThis study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.MethodsSixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration.ResultsCompared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ.ConclusionThe proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

MRI-guided stereotactic ablative radiation therapy for metachronously recurrent pulmonary oligometastases from hepatocellular carcinoma after failure of systemic therapy

Objectives. This article aims to evaluate the feasibility and effectiveness of MRI-guided stereotactic ablative radiotherapy (SABR) for metachronously recurrent pulmonary oligometastases from hepatocellular carcinoma (HCC) when systemic therapies prove ineffective. Case Presentation. A 47-year-old male with metastatic HCC underwent unsuccessful chemotherapy, targeted therapy, and immunotherapy. The patient had a history of 14 lung metastases, previously treated with respiratory non-gated SABR. However, five new lung metastases emerged, raising concerns about potential pulmonary toxicity. After administering oral capecitabine, tumor growth persisted. To address this, the patient received MRI-guided respiratory-gated SABR using a recently installed system. Outcome. MRI-guided respiratory-gated SABR was well-tolerated, with no radiation-induced adverse events reported during treatment and a six-month follow-up period. Imaging follow-up demonstrated complete tumor regression, and no new recurrences were observed. Conclusions. This case report suggests that MRI-guided SABR could be a viable option for managing metachronously recurrent pulmonary oligometastases from HCC, particularly after failed systemic therapies. The precise targeting achieved with minimal toxicity, utilizing a 1 mm margin without an internal target volume, showcases the potential of this approach. However, further scientific investigation is warranted to validate these findings, given the limited scope of this single case report. Nonetheless, MRI-guided SABR holds promise as a safe and effective local treatment modality for lung metastases in HCC patients.

Comparing adaptation strategies in MRI-guided online adaptive radiotherapy for prostate cancer: Implications for treatment margins

PurposeTo quantify the difference in accuracy of adapt-to-position (ATP), adapt-to-rotation (ATR) and adapt-to-shape (ATS) workflows used in MRI-guided online adaptive radiotherapy for prostate carcinoma (PCa) by evaluating the margins required to accommodate intra-fraction motion of the clinical target volumes for prostate (CTVpros), prostate including seminal vesicles (CTVpros + sv) and gross tumor volume (GTV). Materials and methodsClinical delineations of the CTVpros, CTVpros + sv and GTV of 24 patients with intermediate- and high-risk PCa, treated using ATS on a 1.5 T MR-Linac, were used for analysis. Delineations were available pre- and during beam-on. To simulate ATP and ATR workflows, we automatically generated the structures associated with these workflows using rigid transformations from the planning-MRI to the daily online MRIs. Clinical GTVs were analyzed as ATR GTVs and only ATP GTVs were simulated. Planning target volumes (PTVs) were generated with isotropic margins ranging 0.0–5.0 mm. The volumetric overlap was calculated between these PTVs and their corresponding clinical delineation on the MRI acquired during beam-on and averaged over all treatment fractions. ResultsThe PTV margin required to cover > 95% of the CTVpros was equal (2.5 mm) for all workflows. For the CTVpros + sv, this margin increased to 5.0, 4.0 and 3.5 mm in the ATP, ATR and ATS workflow, respectively. GTV coverage improved from ATP to ATR for margins up to 4.0 mm. ConclusionATP, ATR and ATS workflows ensure equal coverage of the CTVpros for the current clinical margins. For the CTVpros + sv, ATS showed optimal performance. GTV coverage improves by additional adaptations to prostate rotations.

Dosimetry impact of distinct gating strategies in cine MR image-guided breath-hold pancreatic cancer radiotherapy.

To investigate the dosimetry effects of different gating strategies in cine magnetic resonance imaging (MRI)-guided breath-hold pancreatic cancer radiotherapy. Two cine MRI-based gating strategies were investigated： a tumor contour-based gating strategy at a gating threshold of 0-5% and a tumor displacement-based gating strategy at a gating threshold of 3-5 mm. The cine MRI videos were obtained from 17 pancreatic cancer patients who received MRI-guided radiation therapy. We calculated the tumor displacement in each cine MR frame that satisfied the gating threshold and obtained the proportion of frames with different displacements. We generated IMRT and VMAT plans using a 33 Gy prescription, and motion plans were generated by adding up all isocenter-shift plans corresponding to different tumor displacements. The dose parameters of GTV, PTV, and organs at risk (OAR) were compared between the original and motion plans. In both gating strategies, the difference was significant in PTV coverage but not in GTV coverage between the original and motion plans. OAR dose parameters deteriorate with increasing gating threshold. The beam duty cycle increased from 19.5±14.3% (median 18.0%) to 60.8±15.6% (61.1%) for gating thresholds from 0% to 5% in tumor contour-based gating and from 51.7±11.5% (49.7%) to 67.3±12.4% (67.1%) for gating thresholds from 3 to 5 mm in tumor displacement-based gating. In tumor contour-based gating strategy, the dose delivery accuracy deteriorates while the dose delivery efficiency improves with increasing gating thresholds. To ensure treatment efficiency, the gating threshold might be no less than 3%. A threshold up to 5% may be acceptable in terms of the GTV coverage. The displacement-based gating strategy may serve as a potential alternative to the tumor contour based gating strategy, in which the gating threshold of approximately 4 mm might be a good choice for reasonably balancing the dose delivery accuracy and efficiency.

What do we want? Training! When do we want it? Now? A training needs analysis for adaptive radiotherapy for therapeutic radiographers

IntroductionTherapeutic radiographers (TRs) have adapted to the changing requirements and demands of the oncology service and in response to advanced techniques such as on-line adaptive MRI-guided radiotherapy (MRIgRT). The skills required for MRIgRT would benefit many TRs not just those involved in this technique. This study presents the results of a training needs analysis (TNA) for the required MRIgRT skills in readiness for training TRs for current and future practice. MethodsA UK-based TNA was used to ask TRs about their knowledge and experience with essential skills required for MRIgRT based on previous investigations into the topic. A five-point Likert scale was used for each of the skills and the difference in values were used to calculate the training need for current and future practice. Results261 responses were received (n = 261). The skill rated the most important to current practice was CBCT/CT matching and/or fusion. The current highest priority needs were radiotherapy planning and radiotherapy dosimetry. The skill rated the most important to future practice was CBCT/CT matching and/or fusion. The future highest priority needs were MRI acquisition and MRI Contouring. Over 50% of participants wanted training or additional training in all skills. There was an increase in all values for skills investigated from current to future roles. ConclusionAlthough the examined skills were viewed as important to current roles, the future training needs, both overall and high priority, were different compared to current roles. As the ‘future’ of radiotherapy can arrive rapidly, it is essential that training is delivered appropriately and timely. Before this can occur, there must be investigations into the method and delivery of this training. Implications for practiceRole development. Education changes for therapeutic radiographers.

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors

Objective. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike computed tomography, they do not occupy all dark regions on the image. The purpose of this study is to develop an automated method to segment the air regions on MRI-guided adaptive radiation therapy (MRgART) of abdominal tumors. Approach. A modified ResUNet3D deep learning (DL)-based auto air delineation model was trained using 102 patients’ MR images. The MR images were acquired by a dedicated in-house sequence named ‘Air-Scan’, which is designed to generate air regions that are especially dark and accentuated. The air volumes generated by the newly developed DL model were compared with the manual air contours using geometric similarity (Dice Similarity Coefficient (DSC)), and dosimetric equivalence using Gamma index and dose-volume parameters. Main results. The average DSC agreement between the DL generated and manual air contours is 99% ± 1%. The gamma index between the dose calculations with overriding the DL versus manual air volumes with density of 0.01 is 97% ± 2% for a local gamma calculation with a tolerance of 2% and 2 mm. The dosimetric parameters from planning target volume—PTV and organs at risk—OARs were all within 1% between when DL versus manual contours were overridden by air density. The model runs in less than five seconds on a PC with 28 Core processor and NVIDIA Quadro® P2000 GPU. Significance: a DL based automated segmentation method was developed to generate air volumes on specialized abdominal MR images and generate results that are practically equivalent to the manual contouring of air volumes.

Hypofractionated Radiotherapy in Localized, Low-Intermediate-Risk Prostate Cancer: Current and Future Prospectives.

At the time of diagnosis, the vast majority of prostate carcinoma patients have a clinically localized form of the disease, with most of them presenting with low- or intermediate-risk prostate cancer. In this setting, various curative-intent alternatives are available, including surgery, external beam radiotherapy and brachytherapy. Randomized clinical trials have demonstrated that moderate hypofractionated radiotherapy can be considered as a valid alternative strategy for localized prostate cancer. High-dose-rate brachytherapy can be administered according to different schedules. Proton beam radiotherapy represents a promising strategy, but further studies are needed to make it more affordable and accessible. At the moment, new technologies such as MRI-guided radiotherapy remain in early stages, but their potential abilities are very promising.

P-374 Total neoadjuvant treatment of rectal cancer by MRI-guided radiotherapy

P-374 Total neoadjuvant treatment of rectal cancer by MRI-guided radiotherapy

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

Trimodal Therapy Using an MR–guided Radiation Therapy Partial Bladder Tumor Boost in Muscle Invasive Bladder Cancer

Bladder preservation with trimodal therapy (TMT; maximal tumor resection followed by chemoradiation) is an effective paradigm for select patients with muscle invasive bladder cancer. We report our institutional experience of a TMT protocol using nonadaptive magnetic resonance imaging-guided radiation therapy (MRgRT) for partial bladder boost (PBB). A retrospective analysis was performed on consecutive patients with nonmetastatic muscle invasive bladder cancer who were treated with TMT using MRgRT between 2019 and 2022. Patients underwent intensity modulated RT-based nonadaptive MRgRT PBB contoured on True fast imaging with steady state precession (FISP) images (full bladder) followed sequentially by computed tomography-based RT to the whole empty bladder and pelvic lymph nodes with concurrent chemotherapy. MRgRT treatment time, table shifts, and dosimetric parameters of target coverage and normal tissue exposure were described. Prospectively assessed acute and late genitourinary and gastrointestinal (GI) toxicity were reported. Two-year local control was assessed with Kaplan-Meier methods. Seventeen patients were identified for analysis. PBB planning target volume margins were ≤8 mm in 94% (n=16) of cases. Dosimetric target coverage parameters were favorable and all normal tissue dose constraints were met. For MRgRT PBB fractions, median table shifts were 0.4 cm (range, 0-3.15), 0.45 cm (0-2.65), and 0.75 cm (0-4.8) in the X, Y, and Z planes, respectively. Median treatment time for MRgRT PBB fractions was 9 minutes (range, 6.9-17.4). We identified 32 out of 100 total MRgRT fractions that may have benefitted from online adaptation based on changes in organ position relative to planning target volume, predominantly because of small bowel (13/32, 41%) or rectum (8/32, 25%). Two patients discontinued RT prematurely. The incidence of highest-grade acute genitourinary toxicity was 1 to 2 (69%) and 3 (6%), whereas the incidence of acute GI toxicity was 1 to 2 (81%) and 3 (6%). There were no late grade 3 events; 17.6% had late grade 2 cystitis and none had late GI toxicity. With median follow-up of 18.2 months (95% CI, 12.4-22.5), the local control rate was 92%, and no patient has required salvage cystectomy. Nonadaptive MRgRT PBB is feasible with favorable dosimetry and low resource utilization. Larger studies are needed to evaluate for potential benefits in toxicity and local control associated with this approach in comparison to standard treatment techniques.

Local Therapies for Hepatocellular Carcinoma and Role of MRI-Guided Adaptive Radiation Therapy.

Hepatocellular carcinoma (HCC) is the most common liver tumor, with a continually rising incidence. The curative treatment for HCC is surgical resection or liver transplantation; however, only a small portion of patients are eligible due to local tumor burden or underlying liver dysfunction. Most HCC patients receive nonsurgical liver-directed therapies (LDTs), including thermal ablation, transarterial chemoembolization (TACE), transarterial radioembolization (TARE), and external beam radiation therapy (EBRT). Stereotactic ablative body radiation (SABR) is a specific type of EBRT that can precisely deliver a high dose of radiation to ablate tumor cells using a small number of treatments (or fractions, typically 5 or less). With onboard MRI imaging, MRI-guided SABR can improve therapeutic dose while minimizing normal tissue exposure. In the current review, we discuss different LDTs and compare them with EBRT, specifically SABR. The emerging MRI-guided adaptive radiation therapy has been reviewed, highlighting its advantages and potential role in HCC management.

Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system.

MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio. We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac. Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n=12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time=5-7 min) and the first 200 spokes (acquisition time=35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review. Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p<0.0001) and smaller RMSEs (p<0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring>8 out of 10), followed by P2P200 (scoring>5 out of 10), and then motion-uncorrected (scoring<3 out of 10) in sharpness, contrast, and artifact-freeness. We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.

SP-0995 An ESTRO-ACROP guideline on quality assurance and medical physics commissioning of online MRI-guided radiotherapy systems based on a consensus expert opinion

SP-0995 An ESTRO-ACROP guideline on quality assurance and medical physics commissioning of online MRI-guided radiotherapy systems based on a consensus expert opinion

Distortion-corrected image reconstruction with deep learning on an MRI-Linac.

MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications. We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported. Imaging results demonstrated that DCReconNet better preserves image structures compared to CS- and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods. DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications.

Consolidation of Gold and Gadolinium Nanoparticles: An Extra Step towards Improving Cancer Imaging and Therapy

The multifactorial nature of cancer still classifies the disease as one of the leading causes of death worldwide. Modern medical sciences are following an interdisciplinary approach that has been fueled by the nanoscale revolution of the past years. The exploitation of high-Z materials, in combination with ionizing or non-ionizing radiation, promises to overcome restrictions in medical imaging and to augment the efficacy of current therapeutic modalities. Gold nanoparticles (AuNPs) have proven their value among the scientific community in various therapeutic and diagnostic techniques. However, the high level of multiparametric demands of AuNP experiments in combination with their biocompatibility and cytotoxicity levels remain crucial issues. Gadolinium NPs (GdNPs), have presented high biocompatibility, low cytotoxicity, and excellent hemocompatibility, and have been utilized in MRI-guided radiotherapy, photodynamic and photothermal therapy, etc. Τhe utilization of gadolinium bound to AuNPs may be a promising alternative that would reduce phenomena, such as toxicity, aggregation, etc., and could create a multimodal in vivo contrast and therapeutic agent. This review highlights multi-functionalization strategies against cancer where gold and gadolinium NPs are implicated. Their experimental applications and limitations of the past 5 years will be analyzed in the hope of enlightening the benefits and drawbacks of their proper combination.

Real-Time MRI-guided Radiation Therapy for Locally Advanced Pancreatic Tail Cancer in Contact with The Colon

Introduction: Curative radiation therapy for locally advanced pancreatic ductal adenocarcinoma (PDAC) is difficult when the tumor is in contact with the colon. In this regard, MRI-guided radiation therapy (MRgRT) provides real-time MRI during radiation therapy, resulting in highdose radiation delivery to the target while avoiding surrounding organs at risk (OARs), such as the intestinal tract. Case Presentation: A 94-year-old woman with locally advanced PDAC in the pancreatic tail underwent high-dose (72 Gy/30 fractions) real-time hypo-fractionated MRgRT to avoid irradiation of the colon. Three months after MRgRT, her serum CA 19-9 level normalized, and the tumor regressed completely. No adverse event or recurrence was noted during the six-month follow-up period.Conclusions: High-dose hypo-fractionated MRgRT may be a treatment option for a patient with locally advanced PDAC of the pancreatic tail, which is in contact with the colon.

The role of adaptive planning in margin-reduced, MRI-guided stereotactic body radiotherapy to the prostate bed following radical prostatectomy: Post-hoc analysis of a phase II clinical trial

Background and purposeWe examined the interfractional variations of clinical target volumes (CTVs), planning target volumes (PTVs), and organs-at-risk (OARs) in patients receiving MRI-guided stereotactic body radiotherapy (SBRT) to the prostate bed and evaluated the potential role of adaptive planning. Materials and Methods31 patients received 30–34 Gy in five fractions to the prostate bed on a phase II clinical trial. OARs, CTVs, and PTVs were retrospectively contoured on daily pretreatment MRIs (n = 155). Geometric comparisons were made between initial planning contours and daily pretreatment contours. Predicted treatment plans for each fraction were evaluated using the following constraints: CTV V95%>93%, PTV V95%>90%, bladder Dmax < 36.7 Gy, bladder V32.5 Gy < 35%, rectum Dmax < 36.7 Gy, rectum V27.5 Gy < 45%, rectum 32.5 Gy < 30%, and rectal wall V24Gy < 50%. Adaptive planning was simulated for all fractions that failed to meet these criteria. Plans were then re-evaluated. ResultsMedian change in volume was 0.48% for CTV, −24.5% for bladder, and 6.95% for rectum. Median DSC was 0.89 for CTV, 0.79 for bladder, and 0.76 for rectum. 145/155 fractions (93.5%) met CTV V95%>93%. 75/155 fractions (48.4%) failed at least one OAR dose constraint. Overall, 83/155 fractions (53.5%) met criteria for adapting planning. This affected 24/31 patients (77.4%). Following adaptive planning, all fractions met CTV V95%>93% and PTV V95%>90% and 120/155 fractions (77.4%) met all OAR constraints. ConclusionDue to significant interfractional variations in anatomy, a majority of fractions failed to meet both target volume and OAR constraints. However, adaptive planning was effective in overcoming these anatomic changes. Adaptive planning should be routinely considered in prostate bed SBRT.