Deep MR to CT synthesis using unpaired data

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images

Machine learning applications in radiation oncology:Current use and needs to support clinical implementation

Background and purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. Materials and methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications. Results: In total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice. Conclusion: The results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction

A comparison of inverse optimization algorithms for HDR/PDR prostate brachytherapy treatment planning

PURPOSE: Graphical optimization (GrO) is a common method for high-dose-rate/pulsed-dose-rate (PDR) prostate brachytherapy treatment planning. New methods performing inverse optimization of the dose distribution have been developed over the past years. The purpose is to compare GrO and two established inverse methods, inverse planning simulated annealing (IPSA) and hybrid inverse treatment planning and optimization (HIPO), and one new method, enhanced geometric optimization-interactive inverse planning (EGO-IIP), in terms of speed and dose volume histogram (DVH) parameters. METHODS AND MATERIALS: For 26 prostate cancer patients treated with a PDR brachytherapy boost, an experienced treatment planner optimized the dose distributions using four different methods: GrO, IPSA, HIPO, and EGO-IIP. Relevant DVH parameters (prostate-V-100%, D-90%, V-150%; urethra-D-0.1cm3 and D-1.0cm3; rectum-D-0.1cm3 and D-2.0cm3; bladder-D-2.0cm3) were evaluated and their compliance to the constraints. Treatment planning time was also recorded. RESULTS: All inverse methods resulted in shorter planning time (mean, 4-6.7 min), as compared with GrO (mean, 7.6 min). In terms of DVH parameters, none of the inverse methods outperformed the others. However, all inverse methods improved on compliance to the planning constraints as compared with GrO. On average, EGO-IIP and GrO resulted in highest D-90%, and the IPSA plans resulted in lowest bladder D-2.0cm3 and urethra D-1.0cm3. CONCLUSIONS: Inverse planning methods decrease planning time as compared with GrO for PDR/high-dose-rate prostate brachytherapy. DVH parameters are comparable for all methods. (C) 2015 American Brachytherapy Society. Published by Elsevier Inc. All rights reserve

Novel tools for stepping source brachytherapy treatment planning: enhanced geometrical optimization and interactive inverse planning

Dose optimization for stepping source brachytherapy can nowadays be performed using automated inverse algorithms. Although much quicker than graphical optimization, an experienced treatment planner is required for both methods. With automated inverse algorithms, the procedure to achieve the desired dose distribution is often based on trial-and-error. A new approach for stepping source prostate brachytherapy treatment planning was developed as a quick and user-friendly alternative. This approach consists of the combined use of two novel tools: Enhanced geometrical optimization (EGO) and interactive inverse planning (IIP). EGO is an extended version of the common geometrical optimization method and is applied to create a dose distribution as homogeneous as possible. With the second tool, IIP, this dose distribution is tailored to a specific patient anatomy by interactively changing the highest and lowest dose on the contours. The combined use of EGO-IIP was evaluated on 24 prostate cancer patients, by having an inexperienced user create treatment plans, compliant to clinical dose objectives. This user was able to create dose plans of 24 patients in an average time of 4.4 min/patient. An experienced treatment planner without extensive training in EGO-IIP also created 24 plans. The resulting dose-volume histogram parameters were comparable to the clinical plans and showed high conformance to clinical standards. Even for an inexperienced user, treatment planning with EGO-IIP for stepping source prostate brachytherapy is feasible as an alternative to current optimization algorithms, offering speed, simplicity for the user, and local control of the dose level

MR-Only Brain Radiation Therapy : Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

Purpose: This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain. Methods and Materials: We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans. Results: sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: –2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: –0.05 to 0.03) for dose within the body contours and –0.13% ± 0.39% (±1 SD, range: –1.43 to 0.80) inside the planning target volume. Mean γ1mm/1% was 98.8% ± 2.2% for doses >50% of the prescribed dose. Conclusions: The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning

MR-Only Brain Radiation Therapy : Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

Purpose: This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain. Methods and Materials: We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans. Results: sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: –2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: –0.05 to 0.03) for dose within the body contours and –0.13% ± 0.39% (±1 SD, range: –1.43 to 0.80) inside the planning target volume. Mean γ1mm/1% was 98.8% ± 2.2% for doses >50% of the prescribed dose. Conclusions: The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning

Improved tumour control probability with MRI-based prostate brachytherapy treatment planning

Backgroun. Due to improved visibility on MRI, contouring of the prostate is improved compared to CT. The aim of this study was to quantify the benefits of using MRI for treatment planning as compared to CT-based planning for temporary implant prostate brachytherapy. Material and methods. CT and MRI image data of 13 patients were used to delineate the prostate and organs at risk (OARs) and to reconstruct the implanted catheters (typically 12). An experienced treatment planner created plans on the CT-based structure sets (CT-plan) and on the MRI-based structure sets (MRI-plan). Then, active dwell-positions and weights of the CT-plans were transferred to the MRI-based structure sets (CT-planMRI-contours) and resulting dosimetric parameters and tumour control probabilities (TCPs) were studied. Results. For the CT-planMRI-contours a statistically significant lower target coverage was detected: mean V100 was 95.1% as opposed to 98.3% for the original plans (p < 0.01). Planning on CT caused cold-spots that influence the TCP. MRI-based planning improved the TCPs by 6-10%, depending on the parameters of the radiobiological model used for TCP calculation. Basing the treatment plan on either CT- or MRI-delineations does not influence plan quality. Conclusion. Evaluation of CT-based treatment planning by transferring the plan to MRI reveals underdosage of the prostate, especially at the base side. Planning on MRI can prevent cold-spots in the tumour and improves the TCP. © 2013 Informa Healthcare