Emerging role of MRI in radiation therapy

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach

PURPOSE

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning.

METHODS

The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors.

RESULTS

In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm).

CONCLUSION

The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

Magnetic Resonance Imaging Techniques for Post-Treatment Evaluation After External Beam Radiation Therapy of Prostate Cancer: Narrative Review

Background/Objectives: This study aimed to investigate the prognostic value of advanced techniques of magnetic resonance imaging (MRI) biochemical recurrence (BCR) after radiotherapy in patients with prostate cancer (PCa). Methods: A comprehensive literature review was conducted to evaluate the role of MRI in detecting BCR of PCa patients after external beam radiation therapy. Results: National guidelines do not recommend imaging techniques in clinical follow-up PCa. However, in 2021, the European Association of Urogenital Radiology (ESUR), the European Association of Urological Imaging (ESUI), and the PI-RADS Steering Committee introduced the Prostate Imaging for Recurrence Reporting (PI-RR) system. PI-RR incorporates the MRI biomarkers in the post-treatment process. In the last decade, a growing number of clinical researchers have investigated the role of various MRI techniques in BCR. Conclusions: The integration of advanced MRI technologies into clinical routine marks the beginning of a new era of BCR with accuracy.

Validation of a diode-based phantom for high temporal and spatial measurements in a 1.5 T MR-linac

BACKGROUND

For the development and validation of dynamic treatment modalities and processes on the MR-linac, independent measurements should be performed that validate dose delivery and linac behavior at a high temporal resolution. To achieve this, a detector with both high temporal and spatial resolution is necessary.

PURPOSE

This study investigates the suitability of a Delta4 Phantom+ MR (Delta4) detector array for time-resolved dosimetry in the 1.5 T MR-linac and characterizes the Delta4's performance with dynamic dose delivery, such as beam gating and field modulation during radiation.

METHODS

A Delta4 detector was used, including software for time-resolved dosimetry. First validation experiments were performed and compared to reference measurements. Subsequently, demonstrator measurements were performed to show use cases of the Delta4's time-resolved dose readouts. An example of such an experiment is the determination of the field speed during a sliding window experiment, traveling between 0.7 and 6.8 cm/s in the cranial-caudal direction.

RESULTS

Validation experiments of the dose reproducibility and dose rate dependency showed no difference relative to the standard static delivery. The field speed measured by the Delta4 showed an average field speed difference of -0.3% relative to MR-linac log files. The Delta4 was capable of measuring the dose with high accuracy and temporal resolution during dynamic radiation delivery.

CONCLUSION

The Delta4 can be used for time-resolved dosimetry in a 1.5 T MR-linac.

Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review

The promise of novel technologies to increase access to radiotherapy in low- and middle-income countries (LMICs) is crucial, given that the cost of equipping new radiotherapy centres or upgrading existing machinery remains a major obstacle to expanding access to cancer treatment. The study aims to provide a thorough analysis overview of how technological advancement may revolutionize radiotherapy (RT) to improve level of care provided to cancer patients. A comprehensive literature review following some steps of systematic review (SLR) was performed using the Web of Science (WoS), PubMed, and Scopus databases. The study findings are classified into different technologies. Artificial intelligence (AI), knowledge-based planning, remote planning, radiotherapy, and scripting are all ways to increase patient flow across radiation oncology, including initial consultation, treatment planning, delivery, verification, and patient follow-up. This review found that these technologies improve delineation of organ at risks (OARs) and considerably reduce waiting times when compared with conventional treatment planning in RT. In this review, AI, knowledge-based planning, remote radiotherapy treatment planning, and scripting reduced waiting times and improved organ at-risk delineation compared with conventional RT treatment planning. A combination of these technologies may lower cancer patients' risk of disease progression due to reduced workload, quality of therapy, and individualized treatment. Efficiency tools, such as the application of AI, knowledge-based planning, remote radiotherapy planning, and scripting, are urgently needed to reduce waiting times and improve OAR delineation accuracy in cancer treatment compared with traditional treatment planning methods. The study's contribution is to present the potential of technological advancement to optimize RT planning process, thereby improving patient care and resource utilization. The study may be extended in the future to include digital integration and technology's impact on patient safety, outcomes, and risk. Therefore, in radiotherapy, research on more efficient tools pioneers the development and implementation of high-precision radiotherapy for cancer patients.

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

Comparative Analysis of Dosimetry: IMRT versus 3DCRT in Left-Sided Breast Cancer Patients with Considering Some Organs in Out - of - Field Borders

Purpose

The local management approach for node-positive breast cancer has undergone substantial evolution. Consequently, there exists a pressing need to enhance our treatment strategies by placing greater emphasis on planning and dosimetric factors, given the availability of more conformal techniques and delineation criteria, achieving optimal goals of radiotherapy treatment. The primary aim of this article is to discuss how the extent of regional nodal coverage influences the choice between IMRT and 3D radiation therapy for patients.

Patients and Methods

A total of 15 patients diagnosed with left breast cancer with disease involved lymph nodes were included in this study. Delivering the recommended dose required the use of a linear accelerator (LINAC) with photon beams energy of 6 mega voltage (6MV). Each patient had full breast radiation using two planning procedures: intensity-modulated radiotherapy (IMRT) and three-dimensional radiotherapy (3D conformal). Following the guidelines set forth by the Radiation Therapy Oncology Group (RTOG), the planned treatment coverage was carefully designed to fall between 95% and 107% of the recommended dose. Additionally, Dose Volume Histograms (DVHs) were generated the dose distribution within these anatomical contours.

Results and Conclusion

The DVH parameters were subjected to a comparative analysis, focusing on the doses absorbed by both Organs at Risk (OARs) and the Planning Target Volume (PTV). The findings suggest that low doses in IMRT plan might raise the risk of adverse oncological outcomes or potentially result in an increased incidence of subsequent malignancies. Consequently, the adoption of inverse IMRT remains limited, and the decision to opt for this therapy should be reserved for situations where it is genuinely necessary to uphold a satisfactory quality of life. Additionally, this approach helps in reducing the likelihood of developing thyroid problems and mitigates the risk of injuries to the supraclavicular area and the proximal head of the humerus bone.

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN

BACKGROUND

In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in the availability of tools tailored for the automatic segmentation of the GTV based on CT simulation localization images.

PURPOSE

This study aims to fill this gap by devising an effective tool specifically for the automatic segmentation of the GTV using CT simulation localization images.

METHODS

A dual-network generative adversarial network (GAN) architecture was developed, wherein the generator focused on refining CT images for more precise delineation, and the discriminator differentiated between real and augmented images. This architecture was coupled with the Mask R-CNN model to achieve meticulous GTV segmentation. An end-to-end training process facilitated the integration between the GAN and Mask R-CNN functionalities. Furthermore, a conditional random field (CRF) was incorporated to refine the initial masks generated by the Mask R-CNN model to ensure optimal segmentation accuracy. The performance was assessed using key metrics, namely, the Dice coefficient (DSC), intersection over union (IoU), accuracy, specificity, and sensitivity.

RESULTS

The GAN+Mask R-CNN+CRF integration method in this study performs well in GTV segmentation. In particular, the model has an overall average DSC of 0.819 ± 0.102 and an IoU of 0.712 ± 0.111 in the internal validation. The overall average DSC in the external validation data is 0.726 ± 0.128 and the IoU is 0.640 ± 0.136. It demonstrates favorable generalization ability.

CONCLUSION

The integration of the GAN, Mask R-CNN, and CRF optimization provides a pioneering tool for the sophisticated segmentation of the GTV in brain metastases using CT simulation localization images. The method proposed in this study can provide a robust automatic segmentation approach for brain metastases in the absence of MRI.

Optimization of treatment workflow for 0.35T MR-Linac system

PURPOSE

This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow.

METHODS

For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows.

RESULTS

A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT.

CONCLUSION

In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.

A Comprehensive Review on the Role of MRI in the Assessment of Supratentorial Neoplasms: Comparative Insights Into Adult and Pediatric Cases

Magnetic resonance imaging (MRI) is a critical diagnostic tool in assessing supratentorial neoplasms, offering unparalleled detail and specificity in brain imaging. Supratentorial neoplasms in the cerebral hemispheres, basal ganglia, thalamus, and other structures above the tentorium cerebelli present significant diagnostic and therapeutic challenges. These challenges vary notably between adult and pediatric populations due to differences in tumor types, biological behavior, and patient management strategies. This comprehensive review explores the role of MRI in diagnosing, planning treatment, monitoring response, and detecting recurrence in supratentorial neoplasms, providing comparative insights into adult and pediatric cases. The review begins with an overview of the epidemiology and pathophysiology of these tumors in different age groups, followed by a detailed examination of standard and advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS). We discuss the specific imaging characteristics of various neoplasms and the importance of tailored approaches to optimize diagnostic accuracy and therapeutic efficacy. The review also addresses the technical and interpretative challenges unique to pediatric imaging and the implications for long-term patient outcomes. By highlighting the comparative utility of MRI in adult and pediatric cases, this review aims to enhance the understanding of its pivotal role in managing supratentorial neoplasms. It underscores the necessity of age-specific diagnostic and therapeutic strategies. Emerging MRI technologies and future research directions are also discussed, emphasizing the potential for advancements in personalized imaging approaches and improved patient care across all age groups.

Impact Assessment of Systemic Geometric Distortion in 1.5T Magnetic Resonance Imaging Simulation through Three-dimensional Geometric Distortion Phantom on Dosimetric Accuracy for Magnetic Resonance Imaging-only Prostate Treatment Planning

Aims

Magnetic resonance imaging (MRI)-only radiotherapy has emerged as a solution to address registration errors that can lead to missed dose delivery. However, the presence of systemic geometric distortion (SGD) stemming from gradient nonlinearity (GNL) and inhomogeneity of the main magnetic field (B0) necessitates consideration. This study aimed to quantitatively assess residual SGD in 1.5T MRI simulation using a three-dimensional (3D) geometric distortion phantom and evaluate its impact on dosimetric accuracy for retrospective prostate cancer patients.

Materials and Methods

Ten retrospective cases of prostate cancer patients treated with volumetric modulated arc radiotherapy (VMAT) were randomly selected. A geometric distortion phantom was scanned on a 1.5T MRI simulation using a 3D T1 volumetric interpolated breath-hold examination sequence, varying bandwidth (BW), and two-phase-encoding directions. Distortion maps were generated and applied to the original computed tomography (oriCT) plan to create a distorted computed tomography plan (dCT), and a dice similarity coefficient (DSC) was observed. Dosimetric accuracy was evaluated by recalculating radiation dose for dCT plans using identical beam parameters as oriCT.

Results

The SGD increased with distance from the isocenter in all series. DSC exceeded 0.95 for all plans except the rectum. Regarding GNL's impact on dosimetric accuracy, most mean percentage errors for clinical target volume, planning target volume, and both femurs were under 2% in all plans, except for the bladder and rectum.

Conclusion

SGD pre-evaluation is crucial and should be incorporated into a quality assurance program to ensure effective MRI-simulation performance before MRI-only treatment planning for prostate cancer.

Key clinical predictors in the diagnosis of ovarian torsion in children

OBJECTIVE

Ovarian torsion (OT) represents a severe gynecological emergency in female pediatric patients, necessitating immediate surgical intervention to prevent ovarian ischemia and preserve fertility. Prompt diagnosis is, therefore, paramount. This retrospective study set out to assess the utility of combined clinical, ultrasound, and laboratory features in diagnosing OT.

METHODS

The authors included 326 female pediatric patients aged under 14 years who underwent surgical confirmation of OT over a five-year period. Logistic regression analysis was employed to pinpoint factors linked with OT, and the authors compared clinical presentation, laboratory results, and ultrasound characteristics between patients with OT (OT group) and without OT (N-OT group). The authors conducted receiver operating characteristic (ROC) curve analysis to gauge the predictive capacity of the combined features.

RESULTS

Among 326, OT was confirmed in 24.23 % (79 cases) of the patients. The OT group had a higher incidence of prenatal ovarian masses than the N-OT (22 cases versus 7 cases) (p < 0.0001). Similarly, the authors observed significant differences in the presence of lower abdominal pain, suspected torsion on transabdominal ultrasound, and a high neutrophil-lymphocyte ratio (NLR > 3) between the OT and non-OT groups (p ˂ 0.05). Furthermore, when these parameters were combined, the resulting area under the curve (AUC) was 0.868, demonstrating their potential utility in OT diagnosis.

CONCLUSION

This study demonstrates a prediction model integrating clinical, laboratory, and ultrasound findings that can support the preoperative diagnosis of ovarian torsion, thereby enhancing diagnostic precision and improving patient management. Future prospective studies should concentrate on developing clinical predictive models for OT in pediatric patients.

Biocompatible and bioactivable terpolymer-lipid-MnO2 Nanoparticle-based MRI contrast agent for improving tumor detection and delineation

Early and precise detection of solid tumor cancers is critical for improving therapeutic outcomes. In this regard, magnetic resonance imaging (MRI) has become a useful tool for tumor diagnosis and image-guided therapy. However, its effectiveness is limited by the shortcomings of clinically available gadolinium-based contrast agents (GBCAs), i.e. poor tumor penetration and retention, and safety concerns. Thus, we have developed a novel nanoparticulate contrast agent using a biocompatible terpolymer and lipids to encapsulate manganese dioxide nanoparticles (TPL-MDNP). The TPL-MDNP accumulated in tumor tissue and produced paramagnetic Mn2+ ions, enhancing T1-weight MRI contrast via the reaction with H2O2 rich in the acidic tumor microenvironment. Compared to the clinically used GBCA, Gadovist®1.0, TPL-MDNP generated stronger T1-weighted MR signals by over 2.0-fold at 30 % less of the recommended clinical dose with well-defined tumor delineation in preclinical orthotopic tumor models of brain, breast, prostate, and pancreas. Importantly, the MRI signals were retained for 60 min by TPL-MDNP, much longer than Gadovist®1.0. Biocompatibility of TPL-MDNP was evaluated and found to be safe up to 4-fold of the dose used for MRI. A robust large-scale manufacturing process was developed with batch-to-batch consistency. A lyophilization formulation was designed to maintain the nanostructure and storage stability of the new contrast agent.

Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma

Background and Purpose

Magnetic resonance (MR)-only radiotherapy (RT) workflow eliminates uncertainties due to computed tomography (CT)-MR image registration, by using synthetic CT (sCT) images generated from MR. This study describes the clinical implementation process, from retrospective commissioning to prospective validation stage of a commercial artificial intelligence (AI)-based sCT product. Evaluation of the dosimetric performance of the sCT is presented, with emphasis on the impact of voxel size differences between image modalities.

Materials and methods

sCT performance was assessed in glioblastoma RT planning. Dose differences for 30 patients in both commissioning and validation cohorts were calculated at various dose-volume-histogram (DVH) points for target and organs-at-risk (OAR). A gamma analysis was conducted on regridded image plans. Quality assurance (QA) guidelines were established based on commissioning phase results.

Results

Mean dose difference to target structures was found to be within ± 0.7 % regardless of image resolution and cohort. OARs' mean dose differences were within ± 1.3 % for plans calculated on regridded images for both cohorts, while differences were higher for plans with original voxel size, reaching up to -4.2 % for chiasma D2% in the commissioning cohort. Gamma passing rates for the brain structure using the criteria 1 %/1mm, 2 %/2mm and 3 %/3mm were 93.6 %/99.8 %/100 % and 96.6 %/99.9 %/100 % for commissioning and validation cohorts, respectively.

Conclusions

Dosimetric outcomes in both commissioning and validation stages confirmed sCT's equivalence to CT. The large patient cohort in this study aided in establishing a robust QA program for the MR-only workflow, now applied in glioblastoma RT at our center.

Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning.

METHODS

MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate noise-free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Multi-scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%.

RESULTS

MC-IDDPM generated brain sCTs with state-of-the-art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC-IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%.

CONCLUSIONS

We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery.

Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique

INTRODUCTION

This paper presents a novel approach to automate the segmentation of Organ-at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined with Ensemble Learning techniques. The study aims to improve the accuracy and efficiency of OAR segmentation, essential for radiotherapy treatment planning.

METHODS

The dataset comprised computed tomography (CT) scans of 182 patients in DICOM format, obtained from an institutional image bank. Experienced Radiation Oncologists manually segmented seven OARs for each scan. Two models, 3D U-Net and 3D DenseNet-FCN, were trained on reduced CT scans (192 × 192 x 128) due to memory limitations. Ensemble Learning techniques were employed to enhance accuracy and segmentation metrics. Testing was conducted on 78 patients from the institutional dataset and an open-source dataset (TCGA-HNSC and Head-Neck Cetuximab) consisting of 31 patient scans.

RESULTS

Using the Ensemble Learning technique, the average dice similarity coefficient for OARs ranged from 0.990 to 0.994, indicating high segmentation accuracy. The 95% Hausdorff distance (mm) ranged from 1.3 to 2.1, demonstrating precise segmentation boundaries.

CONCLUSION

The proposed automated segmentation method achieved efficient and accurate OAR segmentation, surpassing human expert performance in terms of time and accuracy.

IMPLICATIONS FOR PRACTICE

This approach has implications for improving treatment planning and patient care in radiotherapy. By reducing manual segmentation reliance, the proposed method offers significant time savings and potential improvements in treatment planning efficiency and precision for head and neck cancer patients.

Contouring practices and artefact management within a synthetic CT-based radiotherapy workflow for the central nervous system

BACKGROUND

The incorporation of magnetic resonance (MR) imaging in radiotherapy (RT) workflows improves contouring precision, yet it introduces geometrical uncertainties when registered with computed tomography (CT) scans. Synthetic CT (sCT) images could minimize these uncertainties and streamline the RT workflow. This study aims to compare the contouring capabilities of sCT images with conventional CT-based/MR-assisted RT workflows, with an emphasis on managing artefacts caused by surgical fixation devices (SFDs).

METHODS

The study comprised a commissioning cohort of 100 patients with cranial tumors treated using a conventional CT-based/MR-assisted RT workflow and a validation cohort of 30 patients with grade IV glioblastomas treated using an MR-only workflow. A CE-marked artificial-intelligence-based sCT product was utilized. The delineation accuracy comparison was performed using dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Artefacts within the commissioning cohort were visually inspected, classified and an estimation of thickness was derived using Hausdorff distance (HD). For the validation cohort, boolean operators were used to extract artefact volumes adjacent to the target and contrasted to the planning treatment volume.

RESULTS

The combination of high DSC (0.94) and low AHD (0.04 mm) indicates equal target delineation capacity between sCT images and conventional CT scans. However, the results for organs at risk delineation were less consistent, likely because of voxel size differences between sCT images and CT scans and absence of standardized delineation routines. Artefacts observed in sCT images appeared as enhancements of cranial bone. When close to the target, they could affect its definition. Therefore, in the validation cohort the clinical target volume (CTV) was expanded towards the bone by 3.5 mm, as estimated by HD analysis. Subsequent analysis on cone-beam CT scans showed that the CTV adjustment was enough to provide acceptable target coverage.

CONCLUSION

The tested sCT product performed on par with conventional CT in terms of contouring capability. Additionally, this study provides both the first comprehensive classification of metal artefacts on a sCT product and a novel method to assess the clinical impact of artefacts caused by SFDs on target delineation. This methodology encourages similar analysis for other sCT products.

The Impact of Nanomedicine on Soft Tissue Sarcoma Treated by Radiotherapy and/or Hyperthermia: A Review

This article presents a comprehensive review of nanoparticle-assisted treatment approaches for soft tissue sarcoma (STS). STS, a heterogeneous group of mesenchymal-origin tumors with aggressive behavior and low overall survival rates, necessitates the exploration of innovative therapeutic interventions. In contrast to conventional treatments like surgery, radiotherapy (RT), hyperthermia (HT), and chemotherapy, nanomedicine offers promising advancements in STS management. This review focuses on recent research in nanoparticle applications, including their role in enhancing RT and HT efficacy through improved drug delivery systems, novel radiosensitizers, and imaging agents. Reviewing the current state of nanoparticle-assisted therapies, this paper sheds light on their potential to revolutionize soft tissue sarcoma treatment and improve patient therapy outcomes.

Influence of magnetic field on a novel scintillation dosimeter in a 1.5 T MR-linac

For commissioning and quality assurance for adaptive workflows on the MR-linac, a dosimeter which can measure time-resolved dose during MR image acquisition is desired. The Blue Physics model 10 scintillation dosimeter is potentially an ideal detector for such measurements. However, some detectors can be influenced by the magnetic field of the MR-linac. To assess the calibration methods and magnetic field dependency of the Blue Physics scintillator in the 1.5 T MR-linac. Several calibration methods were assessed for robustness. Detector characteristics and the influence of the calibration methods were assessed based on dose reproducibility, dose linearity, dose rate dependency, relative output factor (ROF), percentage depth dose profile, axial rotation and the radial detector orientation with respect to the magnetic field. The potential application of time-resolved dynamic dose measurements during MRI acquisition was assessed. A variation of calibration factors was observed for different calibration methods. Dose reproducibility, dose linearity and dose rate stability were all found to be within tolerance and were not significantly affected by different calibration methods. Measurements with the detector showed good correspondence with reference chambers. The ROF and radial orientation dependence measurements were influenced by the calibration method used. Axial detector dependence was assessed and relative readout differences of up to 2.5% were observed. A maximum readout difference of 10.8% was obtained when rotating the detector with respect to the magnetic field. Importantly, measurements with and without MR image acquisition were consistent for both static and dynamic situations. The Blue Physics scintillation detector is suitable for relative dosimetry in the 1.5 T MR-linac when measurements are within or close to calibration conditions.

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.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images

Objectives

Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients.

Methods

This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant.

Results

No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively.

Conclusions

The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC.

Advances in knowledge

First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

The contest between internal and external-beam dosimetry: The Zeno's paradox of Achilles and the tortoise

Radionuclide therapy, also called molecular radiotherapy (MRT), has come of age, with several novel radiopharmaceuticals being approved for clinical use or under development in the last decade. External beam radiotherapy (EBRT) is a well-established treatment modality, with about half of all oncologic patients expected to receive at least one external radiation treatment over their disease course. The efficacy and the toxicity of both types of treatment rely on the interaction of radiation with biological tissues. Dosimetry played a fundamental role in the scientific and technological evolution of EBRT, and absorbed doses to the target and to the organs at risk are calculated on a routine basis. In contrast, in MRT the usefulness of internal dosimetry has long been questioned, and a structured path to include absorbed dose calculation is missing. However, following a similar route of development as EBRT, MRT treatments could probably be optimized in a significant proportion of patients, likely based on dosimetry and radiobiology. In the present paper we describe the differences and the similarities between internal and external-beam dosimetry in the context of radiation treatments, and we retrace the main stages of their development over the last decades.

Assessing the Impact of a 1.5 T Transverse Magnetic Field in Radiotherapy for Esophageal Cancer Patients

Purpose: Magnetic resonance (MR)-guided radiotherapy enables visualization of static anatomy, capturing tumor motion, and extracting quantitative image features for treatment verification and outcome monitoring. However, magnetic fields in online MR imaging (MRI) require efforts to ensure accurate dose measurements. This study aimed to assess the dosimetric impact of a 1.5 T magnetic field in esophageal cancer radiotherapy using MR-linac, exploring treatment adaptation potential and personalized medicine benefits. Methods: A prospective cohort study enrolled 100 esophageal squamous cell carcinoma patients undergoing 4DCT and 3DCT scans before radiotherapy. The heart was contoured on 3DCT, 4DCT end expiration (EE), and 4DCT end inhalation (EI) images by the same radiation oncologist. Reference RT plans were designed on 3DCT, with adjustments for different phases generating 5 plan types per patient. Variations in dose-volume parameters for organs at risk and the target area among different plans were compared using Monaco 5.40.04. Results: Slight dose distortions at air-tissue interfaces were observed in the magnetic field's presence. Dose at air-tissue interfaces (chest wall and heart wall) was slightly higher in some patients (3.0% tissue increased by 4.3 Gy on average) compared to nonmagnetic conditions. Average clinical target volume coverage V100 dropped from 99% to 95% compared to reference plans (planEI and planEE). Dose-volume histogram variation between the original plan and reference plans was within 2.3%. Superior-inferior (SI) direction displacement was significantly larger than lateral and anterior-posterior directions (P < .05). Conclusion: Significant SI direction shift in lower esophageal cancerous regions during RT indicates the magnetic field's dosimetric impact, including the electron return effect at tissue-air boundaries. Changes in OAR dose could serve as valuable indicators of organ impairment and target dose alterations, especially for cardiac tissue when using the 1.5 T linac method. Reoptimizing the plan with the magnetic field enhances the feasibility of achieving a clinically acceptable treatment plan for esophageal cancer patients.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models

The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated.

Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain

BACKGROUND

Advancements in deep-learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)-only based radiotherapy treatment planning (RTP) of the brain.

PURPOSE

This study evaluates the clinical feasibility of a commercial, deep-learning based MRI-only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain.

METHODS

Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI-only RTP method was evaluated by a direct comparison of the sCT-based and computed tomography (CT)-based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone-beam computed tomography (CBCT) imaging.

RESULTS

An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean =1.0% for NTs. Global three-dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance-to-agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT-based and sCT-based CBCT patient positioning in both subgroups.

CONCLUSIONS

In terms of dose calculation and patient positioning accuracy, the studied MRI-only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow.

Comparison of magnetic resonance imaging and CT scan-based delineation of target volumes and organs at risk in the radiation treatment planning of head and neck malignancies

INTRODUCTION

Accuracy of target definition is paramount in radiation treatment planning. The optimal choice of imaging modality to define the tumor volume in head and neck tumors is debatable. The study compared MRI and CT scan-based delineation of target volume and Organs At Risk in head and neck cancers.

MATERIALS AND METHODS

54 head and neck carcinoma patients underwent rigid image registration of planning CT images with MRI images. The gross tumor volume of the primary tumor, node, and organs at risk were delineated on both CT and MRI images. A volumetric evaluation was done for gross tumors, nodes, and organs at risk. Dice Similarity coefficient (DSC), Conformity index(CI), Sensitivity index(SI), and Inclusion index(II) were calculated for gross tumor, node, brainstem, and bilateral parotids.

RESULTS

The mean volume of the tumor in CT and MRI obtained were 41 .94 cc and 34.76 ccs, mean DSC, CI, SI, and II of the tumor were 0.71, 0.56, 67.37, and 79.80. The mean volume of the node in CT and MRI were 12.16 cc and 10.24 cc, mean DSC, CI, SI, and II of the node were 0.61, 0.45, 62.47, and 64. The mean volume of the brainstem in CT and MRI was 24.13 cc and 21.21 cc. The mean volume of the right parotid in CT and MRI was 24.39 cc, 26.04 ccs. The mean volume of left parotid in CT and MRI, respectively, were 23.95 ccs and 25.04 ccs.

CONCLUSIONS

The study shows that MRI may be used in combination with CT for better delineation of target volume and organs at risk for head and neck malignancies.

Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency

PURPOSE

To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI.

METHODS

Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed.

RESULTS

Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages.

CONCLUSION

Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.

Application of Medical Image Navigation Technology in Minimally Invasive Puncture Robot

Microneedle puncture is a standard minimally invasive treatment and surgical method, which is widely used in extracting blood, tissues, and their secretions for pathological examination, needle-puncture-directed drug therapy, local anaesthesia, microwave ablation needle therapy, radiotherapy, and other procedures. The use of robots for microneedle puncture has become a worldwide research hotspot, and medical imaging navigation technology plays an essential role in preoperative robotic puncture path planning, intraoperative assisted puncture, and surgical efficacy detection. This paper introduces medical imaging technology and minimally invasive puncture robots, reviews the current status of research on the application of medical imaging navigation technology in minimally invasive puncture robots, and points out its future development trends and challenges.

Multi-parametric MRI for radiotherapy simulation

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen

Background and purpose

The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.

Materials and methods

A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.

Results

The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.

Conclusion

The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Application of iron oxide nanoparticles in the diagnosis and treatment of leukemia

Leukemia is a malignancy initiated by uncontrolled proliferation of hematopoietic stem cell from the B and T lineages, resulting in destruction of hematopoietic system. The conventional leukemia treatments induce severe toxic and a long series of unwanted side-effects which are caused by lack of specificity of anti-leukemic drugs. Recently, nanotechnology have shown tremendous application and clinical impact with respect to diagnosis and treatment of leukemia. According to considerable researches in the context of finding new nanotechnological platform, iron oxide nanoparticles have been gained increasing attention for the leukemia patients use. In this review, a short introduction of leukemia is described followed by the evaluation of the current approaches of iron oxide nanoparticles applied in the leukemia detection and treatment. The enormous advantages of iron oxide nanoparticles for leukemia have been discussed, which consist of the detection of magnetic resonance imaging (MRI) as efficient contrast agents, magnetic biosensors and targeted delivery of anti-leukemia drugs by coating different targeting moieties. In addition, this paper will briefly describe the application of iron oxide nanoparticles in the combined treatment of leukemia. Finally, the shortcomings of the current applications of iron-based nanoparticles in leukemia diagnosis and treatment will be discussed in particular.

Artificial intelligence-supported applications in head and neck cancer radiotherapy treatment planning and dose optimisation

INTRODUCTION

The aim of this review is to describe how various AI-supported applications are used in head and neck cancer radiotherapy treatment planning, and the impact on dose management in regards to target volume and nearby organs at risk (OARs).

METHODS

Literature searches were conducted in databases and publisher portals Pubmed, Science Direct, CINAHL, Ovid, and ProQuest to peer reviewed studies published between 2015 and 2021.

RESULTS

Out of 464 potential ones, ten articles covering the topic were selected. The benefit of using deep learning-based methods to automatically segment OARs is that it makes the process more efficient producing clinically acceptable OAR doses. In some cases automated treatment planning systems can outperform traditional systems in dose prediction.

CONCLUSIONS

Based on the selected articles, in general AI-based systems produced time savings. Also, AI-based solutions perform at the same level or better than traditional planning systems considering auto-segmentation, treatment planning and dose prediction. However, their clinical implementation into routine standard of care should be carefully validated IMPLICATIONS TO PRACTICE: AI has a primary benefit in reducing treatment planning time and improving plan quality allowing dose reduction to the OARs thereby enhancing patients' quality of life. It has a secondary benefit of reducing radiation therapists' time spent annotating thereby saving their time for e.g. patient encounters.

Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac

PURPOSE

A hybrid magnetic resonance linear accelerator (MRL) can perform magnetic resonance imaging (MRI) with high soft-tissue contrast to be used for online adaptive radiotherapy (oART). To obtain electron densities needed for the oART dose calculation, a computed tomography (CT) is often deformably registered to MRI. Our aim was to evaluate an MRI-only based synthetic CT (sCT) generation as an alternative to the deformed CT (dCT)-based oART in the abdominal region.

METHODS

The study data consisted of 57 patients who were treated on a 0.35 T MRL system mainly for abdominal tumors. Simulation MRI-CT pairs of 43 patients were used for training and validation of a prototype convolutional neural network sCT-generation algorithm, based on HighRes3DNet, for the abdominal region. For remaining test patients, sCT images were produced from simulation MRIs and daily MRIs. The dCT-based plans were re-calculated on sCT with identical calculation parameters. The sCT and dCT were compared in terms of geometric agreement and calculated dose.

RESULTS

The mean and one standard deviation of the geometric agreement metrics over dCT-sCT-pairs were: mean error of 8 ± 10 HU, mean absolute error of 49 ± 10 HU, and Dice similarity coefficient of 55 ± 12%, 60 ± 5%, and 82 ± 15% for bone, fat, and lung tissues, respectively. The dose differences between the sCT and dCT-based dose for planning target volumes were 0.5 ± 0.9%, 0.6 ± 0.8%, and 0.5 ± 0.8% at D_2% , D_50% , and D_98% in physical dose and 0.8 ± 1.4%, 0.8 ± 1.2%, and 0.6 ± 1.1% in biologically effective dose (BED). For organs-at-risk, the dose differences of all evaluated dose-volume histogram points were within [-4.5%, 7.8%] and [-1.1 Gy, 3.5 Gy] in both physical dose and BED.

CONCLUSIONS

The geometric agreement metrics were within typically reported values and most average relative dose differences were within 1%. Thus, an MRI-only sCT-based approach is a promising alternative to the current clinical practice of the abdominal oART on MRL.

Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction

BACKGROUND

The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting.

PURPOSE

We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting.

METHODS

Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random.

RESULTS

In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant.

CONCLUSIONS

We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion

Purpose

Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients.

Methods

An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion.

Results

The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively.

Conclusion

A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.

Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon

The special issue of JCM on “Advances of MRI in Radiation Oncology” provides a unique forum for scientific literature related to MR imaging in radiation oncology. This issue covered many aspects, such as MR technology, motion management, economics, soft-tissue–air interface issues, and disease sites such as the pancreas, spine, sarcoma, prostate, head and neck, and rectum from both camps—the Unity and MRIdian systems. This paper provides additional information on the success and challenges of the two systems. A challenging aspect of this technology is low throughput and the monumental task of education and training that hinders its use for the majority of therapy centers. Additionally, the cost of this technology is too high for most institutions, and hence widespread use is still limited. This article highlights some of the difficulties and how to resolve them.

Time-course assessment of 3D-image distortion on the 1.5 T Marlin/Elekta Unity MR-LINAC

PURPOSE

The efficacy of MR-guided radiotherapy on a MR-LINAC (MR-L) is dependent on the geometric accuracy of its MR images over clinically relevant Fields-of-View (FOVs). Our objectives were to: evaluate gradient non-linearity (GNL) on the Elekta Unity MR-L across time via 76 weekly measurements of 3D-distortion over concentrically larger diameter spherical volumes (DSVs); quantify distortion measurement error; and assess the temporal stability of spatial distortion using statistical process control (SPC).

METHODS

MR-image distortion was assessed using a large-FOV 3D-phantom containing 1932 markers embedded in seven parallel plates, spaced 25 mm × 25 mm in- and 55 mm through-plane. Automatically analyzed T1 images yielded distortions in 200, 300, 400 and 500 mm concentric DSVs. Distortion measurement error was evaluated using median absolute difference analysis of imaging repeatability tests.

RESULTS

Over the measurement period absolute time-averaged distortion varied between: dr = 0.30 - 0.49 mm, 0.53 - 0.80 mm, 1.0 - 1.4 mm and 2.28 - 2.37 mm, for DSVs 200, 300, 400 and 500 mm at the 98^th percentile level. Repeatability tests showed that imaging/repositioning introduces negligible error: mean ≤ 0.02 mm (max ≤ 0.3 mm). SPC analysis showed image distortion was stable across all DSVs; however, noticeable changes in GNL were observed following servicing at the one-year mark.

CONCLUSIONS

Image distortion on the MR-L is in the sub-millimeter range for DSVs ≤ 300 mm and stable across time, with SPC analysis indicating all measurements remain within control for each DSV.

CT-MR Image Fusion for Post-Implant Dosimetry Analysis in Brain Tumor Seed Implantation- a Preliminary Study

Purpose

To calculate and evaluate postimplant dosimetry (PID) with CT-MR fusion technique after brain tumor brachytherapy and compare the result with CT-based PID.

Methods and Materials

16 brain tumor patients received MR-guided intervention with Iodine-125 (¹²⁵I) seed implantation entered this preliminary study for PID evaluation. Registration and fusion of CT and MR images of the same patients were performed one day after operation. Seeds identification and targets delineation were carried out on CT, MR, and CT-MR fusion images, each. The number and location of seeds on MR or CT- MR fusion images were compared with those of actually implanted seeds. Clinical target volume (CTV) and dosimetric parameters such as %D90, %V100 and external V100 were measured and calculated. In addition, the correlation of the fusion to CT CTV ratio and other factors were analyzed.

Results

The numbers of fusion seeds were not significantly different compared with reference seeds (t =1.76, p >0.05). The difference between reference seeds numbers and truly extracted MR seeds numbers was statistically significant (t =3.91, p <0.05). All dosimetric parameters showed significant differences between the two techniques (p <0.05). The mean CTV delineated on fusion images was 34.3 ± 33.6, smaller than that on CT images. The mean values of external V100, %V100 and %D90 on fusion images were larger than those on CT images. Correlation analysis showed that the fusion-CT V100 ratio was positively and significantly correlated with the fusion-CT volume ratio.

Conclusions

This preliminary study indicated that CT-MR fusion-based PID exhibited good accuracy for ¹²⁵I brain tumor brachytherapy dosimetry when compared to CT-based PID and merits further research to establish best-outcome protocols.

MRI Safety Practice Observations in MRI Facilities Within the Kingdom of Jordan, Compared to the 2020 Manual on MR Safety of the American College of Radiology

Purpose

The absence of ionizing radiation in MRI applications does not guarantee absolute safety. Implementing of safety guidelines can ensure high-quality practice in the clinical MRI with the minimum risk. For this purpose, this cross-section quantitative study conducted in Jordan Kingdom aimed to assess current MRI safety guidelines in comparison with those of 2020 Manual on MR Safety of the American College of Radiology (ACR).

Patients and Methods

A site observation study of 38 MRI units was undertaken in June 2021. A well-structured MRI safety questionnaire was the primary data collection method. Data were subjected to a descriptive statistics content analysis by the SPSS version 20. The results were analyzed to yield comprehensive discussions.

Results

A total of 38 MRI facilities in participated in this study with the responding rate of 44.7%. Patient screening areas and changing rooms were available in about 29% (11/38) of the MRI facilities. Most facilities (55%, 21/38) conducted verbal screening only whereas 21% implemented both written and verbal screening for their patients and companions in zone II, which was present in a percentage of 29% in the approached facilities. Meanwhile, only 13 (43.2%) of 38 facilities used handheld magnets for physical screening, 25 (65.8%) of MRI units did not use any kind of ferromagnetic metal detection systems. Three (7.9%) participating centers had MR-safe wheelchairs, ventilators, anesthesia machines, and stretchers. Most MRI facilities participating in this study (71%) had emergency preparedness plans for alternative power outages. Despite a relatively low number of participating centers having an emergency exit or code (26.3% and 10.5%, respectively), none of them performed practice drills for such scenarios.

Conclusion

Investing in new MR-safe equipment requires introducing ferromagnetic detecting systems. More research is needed to establish the degree of MRI professional’s safety-related education.

Pseudo computed tomography image generation from brain magnetic resonance image using integration of PCA & DCNN-UNET: A comparative analysis

MRI-Only Radiation (RT) now avoids some of the issues associated with employing Computed Tomography(CT) in RT chains, such as MRI registration to a separate CT, excess dosage administration, and the cost of recurrent imaging. The fact that MRI signal intensities are unrelated to the biological tissue’s attenuation coefficient poses a problem. This raises workloads, creates uncertainty as a result of the required inter-modality image registrations, and exposes patients to needless radiation. While using only MRI would be preferable, a method for estimating a pseudo-CT (pCT)or synthetic-CT(sCT) for producing electron density maps and patient positioning reference images is required. As Deep Learning(DL) is revolutionized in so many fields these days, an effective and accurate model is required for generating pCT from MRI. So, this paper depicts an efficient DL model in which the following are the stages: a) Data Acquisition where CT and MRI images are collected b) preprocessing these to avoid the anomalies and noises using techniques like outlier elimination, data smoothening and data normalizing c) feature extraction and selection using Principal Component Analysis (PCA) & regression method d) generating pCT from MRI using Deep Convolutional Neural Network and UNET (DCNN-UNET). We here compare both feature extraction (PCA) and classification model (DCNN-UNET) with other methods such as Discrete Wavelet Tranform(DWT), Independent Component Analysis(ICA), Fourier Transform and VGG16, ResNet, AlexNet, DenseNet, CNN (Convolutional Neural Network)respectively. The performance measures used to evaluate these models are Dice Coefficient(DC), Structured Similarity Index Measure(SSIM), Mean Absolute Error(MAE), Mean Squared Error(MSE), Accuracy, Computation Time in which our proposed system outperforms better with 0.94±0.02 over other state-of-art models.

Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network

Magnetic resonance imaging (MRI) allows accurate and reliable organ delineation for many disease sites in radiation therapy because MRI is able to offer superb soft-tissue contrast. Manual organ-at-risk delineation is labor-intensive and time-consuming. This study aims to develop a deep-learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy. A novel regional convolutional neural network (R-CNN) architecture, namely, mask scoring R-CNN, has been developed in this study. In the proposed model, a deep attention feature pyramid network is used as a backbone to extract the coarse features given by MRI, followed by feature refinement using R-CNN. The final segmentation is obtained through mask and mask scoring networks taking those refined feature maps as input. With the mask scoring mechanism incorporated into conventional mask supervision, the classification error can be highly minimized in conventional mask R-CNN architecture. A cohort of 60 HN cancer patients receiving external beam radiation therapy was used for experimental validation. Five-fold cross-validation was performed for the assessment of our proposed method. The Dice similarity coefficients of brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord were 0.89 ± 0.06, 0.68 ± 0.14/0.68 ± 0.18, 0.89 ± 0.07/0.89 ± 0.05, 0.90 ± 0.07, 0.67 ± 0.18/0.67 ± 0.10, 0.82 ± 0.10, 0.61 ± 0.14, 0.67 ± 0.11/0.68 ± 0.11, 0.92 ± 0.07, 0.85 ± 0.06/0.86 ± 0.05, 0.80 ± 0.13, and 0.77 ± 0.15, respectively. After the model training, all OARs can be segmented within 1 min.

MR-based truncation correction using an advanced HUGE method to improve attenuation correction in PET/MR imaging of obese patients

PURPOSE

Truncation artifacts in the periphery of the magnetic resonance (MR) field-of-view (FOV) and thus, in the MR-based attenuation correction (AC) map, may hamper accurate positron emission tomography (PET) quantification in whole-body PET/MR, which is especially problematic in patients with obesity with overall large body dimensions. Therefore, an advanced truncation correction (TC) method to extend the conventional MR FOV is needed.

METHODS

The extent of MR-based AC-map truncations in obese patients was determined in a data set including n  =  10 patients that underwent whole-body PET/MR exams. Patient inclusion criteria were defined as BMI > 30 kg/m² and body weight > 100 kg. Truncations in PET/MR patients with obesity were quantified comparing the MR-based AC-map volume to segmented non-AC PET data, serving as the reference body volume without truncations to demonstrate the need of improved TC. The new method implemented in this study, termed "advanced HUGE", was modified and extended from the original HUGE method by Blumhagen et al. in order to provide improved TC across the entire axial MR FOV and to unlock new clinical applications of PET/MR. Advanced HUGE was then systematically tested in PET/MR NEMA phantom measurements. Relative differences between computed tomography (CT) AC PET data of the phantom setup (reference) and MR-based Dixon AC, respectively Dixon + advanced HUGE AC, were calculated. The applicability of the method for advanced TC was then demonstrated in first MR-based measurements in healthy volunteers.

RESULTS

It was found that the MR-based AC maps of obese patients often reveal truncations in anterior-posterior direction. Especially the abdominal region could benefit from improved TC, where maximal relative differences in the AC-map volume up to -17 % were calculated. Applying advanced HUGE to improve the MR-based AC in PET/MR, PET quantification errors in the large-volume phantom setup could be considerably reduced from average -18.6 % (Dixon AC) to 4.6 % compared to the CT AC reference. Volunteer measurements demonstrate that formerly missing AC-map volume in the Dixon-VIBE AC-map could be added due to advanced HUGE in anterior-posterior direction and thus, potentially improves AC in PET/MR.

CONCLUSIONS

The advanced HUGE method for truncation correction considerably reduces truncations in anterior-posterior direction demonstrated in phantom measurements and healthy volunteers and thus, further improves MR-based AC in PET/MR imaging. This article is protected by copyright. All rights reserved.

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients

PURPOSE

To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients.

METHODS AND MATERIALS

The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume.

RESULTS

MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions.

CONCLUSION

We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging

Objective. MR SIGnature MAtching (MRSIGMA) is a real-time volumetric MRI technique to image tumor and organs at risk motion in real-time for radiotherapy applications, where a dictionary of high-resolution 3D motion states and associated motion signatures are computed first during offline training and real-time 3D imaging is performed afterwards using fast signature-only acquisition and signature matching. However, the lack of a reference image with similar spatial resolution and temporal resolution introduces significant challenges forin vivovalidation.Approach. This work proposes a retrospective self-validation for MRSIGMA, where the same data used for real-time imaging are used to create a non-real-time reference for comparison. MRSIGMA with self-validation is tested in patients with liver tumors using quantitative metrics defined on the tumor and nearby organs-at-risk structures. The dice coefficient between contours defined on the real-time MRSIGMA and non-real-time reference was used to assess motion imaging performance.Main Results. Total latency (including signature acquisition and signature matching) was between 250 and 314 ms, which is sufficient for organs affected by respiratory motion. Mean ± standard deviation dice coefficient over time was 0.74 ± 0.03 for patients imaged without contrast agent and 0.87 ± 0.03 for patients imaged with contrast agent, which demonstrated high-performance real-time motion imaging.Signficance. MRSIGMA with self-evaluation provides a means to perform real-time volumetric MRI for organ motion tracking with quantitative performance measures.

Integrating multiple MRI sequences for pelvic organs segmentation via the attention mechanism

PURPOSE

To create a network which fully utilizes multi-sequence MRI and compares favorably with manual human contouring.

METHODS

We retrospectively collected 89 MRI studies of the pelvic cavity from patients with prostate cancer and cervical cancer. The dataset contained 89 samples from 87 patients with a total of 84 valid samples. MRI was performed with T1-weighted (T1), T2-weighted (T2), and Enhanced Dixon T1-weighted (T1DIXONC) sequences. There were two cohorts. The training cohort contained 55 samples and the testing cohort contained 29 samples. The MRI images in the training cohort contained contouring data from radiotherapist α. The MRI images in the testing cohort contained contouring data from radiotherapist α and contouring data from another radiotherapist: radiotherapist β. The training cohort was used to optimize the convolution neural networks, which included the attention mechanism through the proposed activation module and the blended module into multiple MRI sequences, to perform autodelineation. The testing cohort was used to assess the networks' autodelineation performance. The contoured organs at risk (OAR) were the anal canal, bladder, rectum, femoral head (L), and femoral head (R).

RESULTS

We compared our proposed network with UNet and FuseUNet using our dataset. When T1 was the main sequence, we input three sequences to segment five organs and evaluated the results using four metrics: the DSC (Dice similarity coefficient), the JSC (Jaccard similarity coefficient), the ASD (average mean distance), and the 95% HD (robust Hausdorff distance). The proposed network achieved improved results compared with the baselines among all metrics. The DSC were 0.834±0.029, 0.818±0.037, and 0.808±0.050 for our proposed network, FuseUNet, and UNet, respectively. The 95% HD were 7.256±2.748 mm, 8.404±3.297 mm, and 8.951±4.798 mm for our proposed network, FuseUNet, and UNet, respectively. Our proposed network also had superior performance on the JSC and ASD coefficients.

CONCLUSION

Our proposed activation module and blended module significantly improved the performance of FuseUNet for multi-sequence MRI segmentation. Our proposed network integrated multiple MRI sequences efficiently and autosegmented OAR rapidly and accurately. We also discovered that three-sequence fusion (T1-T1DIXONC-T2) was superior to two-sequence fusion (T1-T2 and T1-T1DIXONC, respectively). We infer that the more MRI sequences fused, the better the automatic segmentation results.