On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy

Potential dosimetric error in the adaptive workflow of a 1.5 T MR-Linac from patient movement relative to immobilisation systems

In magnetic resonance- (MR-) based adaptive workflows for an MR-linac, the treatment plan is optimized and recalculated online using the daily MR images. The Unity MR-linac is supplied with a patient positioning device (ppd) using pelvic and abdomen thermoplastic masks attached to a board with high-density components. This study highlights the dosimetric effect of using this in such workflows when there are relative patient-ppd displacements, as these are not visualized on MR imaging and the treatment planning system assumes the patient is fixed relative to the ppd. The online adapted plans of two example rectum cancer patients treated at a Unity MR-linac were perturbed by introducing relative patient-ppd displacements, and the effect was evaluated on plan dosimetry. Forty-eight perturbed clinical adapted plans were recalculated, based on online MR-based synthetic computed tomography, and compared with the original plans, using dose-volume histogram parameters and gamma analysis. The target volume covered by the prescribed dose ( D pre ) and by at least 107% of D pre varied up to - 1.87% and + 3.67%, respectively for 0.5 cm displacements, and to - 3.18% and + 4.96% for 2 cm displacements; whilst 2%-2 mm gamma analysis showed a median value of 92.9%. The use of a patient positioning system with high-density components in a Unity MR-based online adaptive treatment workflow can introduce unrecognized errors in plan dosimetry and it is recommended not to use such a device for such treatments, without modifying the device and the workflow, followed by careful clinical evaluation, or alternatively to use other immobilization methods.

CT-based generation of synthetic-pseudo MR images with different weightings for human knee

Synthetic MR images are generated for their high soft-tissue contrast avoiding the discomfort by the long acquisition time and placing claustrophobic patients in the MR scanner's confined space. The aim of this study is to generate synthetic pseudo-MR images from a real CT image for the knee region in vivo. 19 healthy subjects were scanned for model training, while 13 other healthy subjects were imaged for testing. The approach used in this work is novel such that the registration was performed between the MR and CT images, and the femur bone, patella, and the surrounding soft tissue were segmented on the CT image. The tissue type was mapped to its corresponding mean and standard deviation values of the CT# of a window moving on each pixel in the reconstructed CT images, which enabled the remapping of the tissue to its MRI intrinsic parameters: T1, T2, and proton density (ρ). To generate the synthetic MR image of a knee slice, a classic spin-echo sequence was simulated using proper intrinsic and contrast parameters. Results showed that the synthetic MR images were comparable to the real images acquired with the same TE and TR values, and the average slope between them (for all knee segments) was 0.98, while the average percentage root mean square difference (PRD) was 25.7%. In conclusion, this study has shown the feasibility and validity of accurately generating synthetic MR images of the knee region in vivo with different weightings from a single real CT image.

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.

Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI

OBJECTIVES

This study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology.

MATERIALS AND METHODS

This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement.

RESULTS

The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals.

CONCLUSION

This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan.

CLINICAL RELEVANCE STATEMENT

Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia.

KEY POINTS

• Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

INTRODUCTION

The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered.

RESULTS

A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.

DISCUSSION

AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging

BACKGROUND

In proton therapy, it is disputed whether synthetic computed tomography (sCT), derived from magnetic resonance imaging (MRI), permits accurate dose calculations. On the one hand, an MRI-only workflow could eliminate errors caused by, e.g., MRI-CT registration. On the other hand, the extra error would be induced due to an sCT generation model. This work investigated the systematic and random model error induced by sCT generation of a widely discussed deep learning model, pix2pix.

MATERIAL AND METHODS

An open-source image dataset of 19 patients with cancer in the pelvis was employed and split into 10, 5, and 4 for training, testing, and validation of the model, respectively. Proton pencil beams (200 MeV) were simulated on the real CT and generated sCT using the tool for particle simulation (TOPAS). Monte Carlo (MC) dropout was used for error estimation (50 random sCT samples). Systematic and random model errors were investigated for sCT generation and dose calculation on sCT.

RESULTS

For sCT generation, random model error near the edge of the body (∼200 HU) was higher than that within the body (∼100 HU near the bone edge and <10 HU in soft tissue). The mean absolute error (MAE) was 49 ± 5, 191 ± 23, and 503 ± 70 HU for the whole body, bone, and air in the patient, respectively. Random model errors of the proton range were small (<0.2 mm) for all spots and evenly distributed throughout the proton fields. Systematic errors of the proton range were -1.0(±2.2) mm and 0.4(±0.9)%, respectively, and were unevenly distributed within the proton fields. For 4.5% of the spots, large errors (>5 mm) were found, which may relate to MRI-CT mismatch due to, e.g., registration, MRI distortion anatomical changes, etc.

CONCLUSION

The sCT model was shown to be robust, i.e., had a low random model error. However, further investigation to reduce and even predict and manage systematic error is still needed for future MRI-only proton therapy.

A body mass index-based method for "MR-only" abdominal MR-guided adaptive radiotherapy

PURPOSE

Dose calculation for MR-guided radiotherapy (MRgRT) at the 0.35 T MR-Linac is currently based on deformation of planning CTs (defCT) acquired for each patient. We present a simple and robust bulk density overwrite synthetic CT (sCT) method for abdominal treatments in order to streamline clinical workflows.

METHOD

Fifty-six abdominal patient treatment plans were retrospectively evaluated. All patients had been treated at the MR-Linac using MR datasets for treatment planning and plan adaption and defCT for dose calculation. Bulk density CTs (4M-sCT) were generated from MR images with four material compartments (bone, lung, air, soft tissue). The relative electron densities (RED) for bone and lung were extracted from contoured CT structure average REDs. For soft tissue, a correlation between BMI and RED was evaluated. Dose was recalculated on 4M-sCT and compared to dose distributions on defCTs assessing dose differences in the PTV and organs at risk (OAR).

RESULTS

Mean RED of bone was 1.17 ± 0.02, mean RED of lung 0.17 ± 0.05. The correlation between BMI and RED for soft tissue was statistically significant (p < 0.01). PTV dose differences between 4M-sCT and defCT were Dmean: -0.4 ± 1.0%, D1%: -0.3 ± 1.1% and D95%: -0.5 ± 1.0%. OARs showed D2%: -0.3 ± 1.9% and Dmean: -0.1 ± 1.4% differences. Local 3D gamma index pass rates (2%/2mm) between dose calculated using 4M-sCT and defCT were 96.8 ± 2.6% (range 89.9-99.6%).

CONCLUSION

The presented method for sCT generation enables precise dose calculation for MR-only abdominal MRgRT.

Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging

Background and Purpose

Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images.

Materials and methods

CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters.

Results

sCT images were generated in 2 s and 2.5 s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained.

Conclusion

U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.

Artificial Intellgence in the Era of Precision Oncological Imaging

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases

INTRODUCTION

This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax.

METHODS

Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,T_A) and external (10,T_B) test set. Image accuracy of generated sCT was evaluated computing the mean absolute (MAE) and mean error (ME) with respect the original CT. Three treatment plans were calculated for each patient considering MRI as reference image: original CT, sCT (pure sCT) and sCT with GTV density override (hybrid sCT) were used as Electron Density (ED) map. Dose accuracy was evaluated comparing treatment plans in terms of gamma analysis and Dose Volume Histogram (DVH) parameters.

RESULTS

No significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT.

CONCLUSION

The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.

Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel

INTRODUCTION

Targeted dose-escalation and reduction of dose to adjacent organs at risk have been the main goal of radiotherapy in the last decade. Prostate cancer benefited the most from this process. In recent years, the development of Intensity Modulated Radiation Therapy (IMRT) and Stereotactic Body Radiotherapy (SBRT) radically changed clinical practice, also thanks to the availability of modern imaging techniques. The aim of this paper is to explore the relationship between diagnostic imaging and prostate cancer radiotherapy techniques.

MATERIALS AND METHODS

Aiming to provide an overview of the integration between modern imaging and radiotherapy techniques, we performed a non-systematic search of papers exploring the predictive value of imaging before treatment, the role of radiomics in predicting treatment outcomes, implementation of novel imaging in RT planning and influence of imaging integration on use of RT in current clinical practice. Three independent authors (GF, IM and ID) performed an independent review focusing on these issues. Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used, and grey literature was searched for further papers of interest. The final choice of papers included was discussed between all co-authors.

RESULTS

This paper contains a narrative report and a critical discussion of the role of new modern techniques in predicting outcomes before treatment, in radiotherapy planning and in the integration with systemic therapy in the management of prostate cancer. Also, the role of radiomics in a tailored treatment approach is explored.

CONCLUSIONS

Integration between diagnostic imaging and radiotherapy is of great importance for the modern treatment of prostate cancer. Future clinical trials should be aimed at exploring the real clinical benefit of complex workflows in clinical practice.

Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT

Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT.

Inpainting the metal artifact region in MRI images by using generative adversarial networks with gated convolution

PURPOSE

Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images.

METHODS

MRI images containing or near the teeth of 70 patients were collected, and the scanning sequence was a T1-weighted high-resolution isotropic volume examination sequence. A total of 10,000 slices were obtained after data enhancement, of which 8,000 slices were used for training. MRI images were normalized to [-1,1]. Based on the randomly generated mask, U-Net, pix2pix, PConv with partial convolution, and GatedConv were used to inpaint the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. The inpainting effect on the test data set using dental masks was also evaluated. Besides, the artifact area of clinical MRI images was inpainted based on the mask sketched by physicians. Finally, the earring artifacts and artifacts caused by abnormal signal foci were inpainted to verify the generalization of the models.

RESULTS

GatedConv could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the results of U-Net, pix2pix, PConv, and Gatedconv, the masked MAEs were 0.1638, 0.1812, 0.1688, and 0.1596, respectively, and the masked PSNRs were 18.2136, 17.5692, 18.2258, and 18.3035 dB, respectively. Using dental masks, the results of U-Net, pix2pix, and PConv differed more from the real images in terms of alveolar shape and surrounding tissue compared with GatedConv. GatedConv could inpaint the metal artifact region in clinical MRI images more effectively than the other models, but the increase in the mask area could reduce the inpainting effect. Inpainted MRI images by GatedConv and CT images with metal artifact reduction coincided with alveolar and tissue structure, and GatedConv could successfully inpaint artifacts caused by abnormal signal foci while the other models failed. The ablation study demonstrated that GC and CA increased the reliability of the inpainting performance of GatedConv.

CONCLUSION

MRI images are affected by metal, and signal void areas appear near metal. GatedConv can inpaint the MRI metal artifact region in the image domain directly and effectively and improve image quality. Medical image inpainting by GatedConv has potential value for tasks such as PET attenuation correction in PET/MRI and adaptive radiotherapy of synthetic CT based on MRI. This article is protected by copyright. All rights reserved.

Precision Imaging Guidance in the Era of Precision Oncology: An Update of Imaging Tools for Interventional Procedures

Interventional oncology (IO) procedures have become extremely popular in interventional radiology (IR) and play an essential role in the diagnosis, treatment, and supportive care of oncologic patients through new and safe procedures. IR procedures can be divided into two main groups: vascular and non-vascular. Vascular approaches are mainly based on embolization and concomitant injection of chemotherapeutics directly into the tumor-feeding vessels. Percutaneous approaches are a type of non-vascular procedures and include percutaneous image-guided biopsies and different ablation techniques with radiofrequency, microwaves, cryoablation, and focused ultrasound. The use of these techniques requires precise imaging pretreatment planning and guidance that can be provided through different imaging techniques: ultrasound, computed tomography, cone-beam computed tomography, and magnetic resonance. These imaging modalities can be used alone or in combination, thanks to fusion imaging, to further improve the confidence of the operators and the efficacy and safety of the procedures. This article aims is to provide an overview of the available IO procedures based on clinical imaging guidance to develop a targeted and optimal approach to cancer patients.

Dosimetric Accuracy of MR-Guided Online Adaptive Planning for Nasopharyngeal Carcinoma Radiotherapy on 1.5 T MR-Linac

Purpose

The aim of this study is to evaluate the dose accuracy of bulk relative electron density (rED) approach for application in 1.5 T MR-Linac and assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for nasopharyngeal carcinoma (NPC) patients.

Methods

Ten NPC patients formerly treated on conventional linac were included in this study, with their original planning CT and MRI collected. For each patient, structures such as the targets, organs at risk, bone, and air regions were delineated on the original CT in the Monaco system (v5.40.02). To simulate the online adaptive workflow, firstly all contours were transferred to MRI from the original CT using rigid registration in the Monaco system. Based on the structures, three different types of synthetic CT (sCT) were generated from MRI using the bulk rED assignment approach: the sCT_ICRU uses the rED values recommended by ICRU46, the sCT_tailor uses the patient-specific mean rED values, and the sCT_Homogeneity uses homogeneous water equivalent values. The same treatment plan was calculated on the three sCTs and the original CT. Dose calculation accuracy was investigated in terms of gamma analysis, point dose comparison, and dose volume histogram (DVH) parameters.

Results

Good agreement of dose distribution was observed between sCT_tailor and the original CT, with a gamma passing rate (3%/3 mm) of 97.81% ± 1.06%, higher than that of sCT_ICRU (94.27% ± 1.48%, p = 0.005) and sCT_Homogeneity (96.50% ± 1.02%, p = 0.005). For stricter criteria 1%/1 mm, gamma passing rates for plans on sCT_tailor, sCT_ICRU, and sCT_Homogeneity were 86.79% ± 4.31%, 79.81% ± 3.63%, and 77.56% ± 4.64%, respectively. The mean point dose difference in PTV_nx between sCT_tailor and planning CT was −0.14% ± 1.44%, much lower than that calculated on sCT_ICRU (−8.77% ± 2.33%) and sCT_Homogeneity (1.65% ± 2.57%), all with p < 0.05. The DVH differences for the plan based on sCT_tailor were much smaller than sCT_ICRU and sCT_Homogeneity.

Conclusions

The bulk rED-assigned sCT by adopting the patient-specific rED values can achieve a clinically acceptable level of dose calculation accuracy in the presence of a 1.5 T magnetic field, making it suitable for online adaptive MR-guided radiotherapy for NPC patients.

Personalized modeling to improve pseudo-CT images for magnetic resonance imaging-guided adaptive radiotherapy

PURPOSE

Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) greatly improves daily tumor localization and enables online re-planning to obtain maximum dosimetric benefits. However, accurately predicting patient-specific electron density maps for adaptive radiotherapy (ART) planning remains a challenge. Therefore, this study proposes a personalized modeling framework for generating pseudo-computed tomography (pCT) in MRIgART.

METHODS AND MATERIALS

Eighty-three patients who received MRIgART were included and CT simulations were performed on all the patients. Daily T2-weighted 1.5 T MRI was acquired using the Unity MR-linac for adaptive planning. Pairs of co-registered CT and daily MRI images of the randomly selected training set (68 patients) were inputted into a generative adversarial network (GAN) to establish a population model. The personalized model for each patient in the test set (15 patients) was acquired using model fine-tuning, which adopted the pair of the deformable-registered CT and the first daily MRI to fine-tune the population model. The pCT quality was quantitatively evaluated in the second and the last fractions with three metrics: intensity accuracy using mean absolute error (MAE); anatomical structure similarity using dice similarity coefficient (DSC); and dosimetric consistency using gamma-passing rate (GPR).

RESULTS

The image generation speed was 65 slices per second. For the last fractions, and for head-neck, thoracoabdominal, and pelvic cases, the average MAEs were 76.8 HU vs. 123.6 HU, 38.1 HU vs. 52.0 HU, and 29.5 HU vs. 39.7 HU, respectively. Furthermore, the average DSCs of bone were 0.92 vs. 0.80, 0.85 vs. 0.73, and 0.94 vs. 0.88; and the average GPRs (1%/1 mm) were 95.5% vs. 84.7%, 97.7% vs. 92.8%, and 95.5% vs. 88.7%, for personalized vs. population models, respectively. Results of the second fractions were similar.

CONCLUSIONS

The proposed personalized modeling framework remarkably improved pCT quality for multiple treatment sites and was well suited for the MRIgART clinical setting.

Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy

PURPOSE

The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT).

MATERIALS AND METHODS

We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon-Mann-Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance (p < 0.05) at the univariate analysis. The radiomic model showing the highest predictive value combined the features calculated starting from the gray level co-occurrence-based features. This model achieved an AUC of 0.73 in the training set and 0.91 in the validation set.

CONCLUSIONS

The proposed radiomic model showed promising performances in predicting 2-year overall survival before NACRT. Nevertheless, the observed results should be tested in larger studies with consistent external validation cohorts, to confirm their potential clinical use.

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance-Only Radiation Therapy Planning Using Perceptual Loss Models

PURPOSE

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region.

METHODS AND MATERIALS

Two models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT.

RESULTS

The sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image.

CONCLUSIONS

This study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

When your MR linac is down: Can an automated pipeline bail you out of trouble?

PURPOSE

The unique treatment delivery technique provided by magnetic resonance guided radiotherapy (MRgRT) can represent a significant drawback when system fail occurs. This retrospective study proposes and evaluates a pipeline to completely automate the workflow necessary to shift a MRgRT treatment to a traditional radiotherapy linac.

MATERIAL AND METHODS

Patients undergoing treatment during the last MRgRT system failure were retrospectively included in this study. The core of the proposed pipeline was based on a tool able to mimic the original MR linac dose distribution. The so obtained dose distribution (AUTO) has been compared with the distribution obtained in the conventional radiotherapy linac (MAN). Plan comparison has been performed in terms of time required to obtain the final dose distribution, DVH parameters, dosimetric indices and visual analogue scales scoring by radiation oncologists.

RESULTS

AUTO plans generation has been obtained within 10 min for all the considered cases. All AUTO plans were found to be within clinical tolerance, showing a mean target coverage variation of 1.7% with a maximum value of 4.3% and a minimum of 0.6% when compared with MAN plans. The highest OARs mean variation has been found for rectum V60 (6.7%). Dosimetric indices showed no relevant differences, with smaller gradient measure in favour of AUTO plans. Visual analogue scales scoring has confirmed comparable plan quality for AUTO plans.

CONCLUSION

The proposed workflow allows a fast and accurate generation of automatic treatment plans. AUTO plans can be considered equivalent to MAN ones, with limited clinical impact in the worst-case scenario.

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

PURPOSE

In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation. This work reviewed deep learning (DL) sCT generation methods and their associated image and dose evaluation, in the context of MRI-based dose calculation.

METHODS

We searched the PubMed and ScienceDirect electronic databases from January 2010 to March 2021. For each paper, several items were screened and compiled in figures and tables.

RESULTS

This review included 57 studies. The DL methods were either generator-only based (45% of the reviewed studies), or generative adversarial network (GAN) architecture and its variants (55% of the reviewed studies). The brain and pelvis were the most commonly investigated anatomical localizations (39% and 28% of the reviewed studies, respectively), and more rarely, the head-and-neck (H&N) (15%), abdomen (10%), liver (5%) or breast (3%). All the studies performed an image evaluation of sCTs with a diversity of metrics, with only 36 studies performing dosimetric evaluations of sCT.

CONCLUSIONS

The median mean absolute errors were around 76 HU for the brain and H&N sCTs and 40 HU for the pelvis sCTs. For the brain, the mean dose difference between the sCT and the reference CT was <2%. For the H&N and pelvis, the mean dose difference was below 1% in most of the studies. Recent GAN architectures have advantages compared to generator-only, but no superiority was found in term of image or dose sCT uncertainties. Key challenges of DL-based sCT generation methods from MRI in radiotherapy is the management of movement for abdominal and thoracic localizations, the standardization of sCT evaluation, and the investigation of multicenter impacts.