A JROD survey: nationwide overview of radiotherapy data from 2015 to 2021.

The purpose of this survey was to examine the status of radiotherapy in Japan based on the cases registered in the Japanese Radiation Oncology Database (JROD), from 2015 to 2021, and to provide basic data to help improve the usefulness of the JROD in the future. The study population consisted of patients who underwent radiotherapy between 2014 and 2020 and did not opt out of the study. The survey item data analyzed in this study were entered into the database at each radiotherapy institution by referring to medical records from the preceding year.

A Japanese registry study and systematic review of particle therapy for renal cell carcinoma.

The feasibility and efficacy of particle beam therapy (PBT) using protons or carbon ions were compared with those of photon-based stereotactic body radiotherapy (SBRT) for primary renal cell carcinoma (RCC) via a systematic review and nationwide registry for PBT (Japanese Society for Radiation Oncology [JASTRO] particle therapy committee). Between July 2016 and May 2019, 20 patients with non-metastatic RCC who were treated at six Japanese institutes (using protons at three, using carbon ions at the other three) were registered in the nationwide database and followed up prospectively. The 20 patients comprised 15 men and had a median age of 67 (range: 57-88) years.

Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.

Background: To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses.

Purpose: To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning.

Material And Methods: \We retrospectively collected 100 retrospective ECG-gated CCTAs.

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement.

Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their diagnostic performance by using late gadolinium enhancement (LGE) MRI as a reference. Materials and methods MDE CT data obtained by averaging three acquisitions with a single breath hold 5 minutes after the contrast material injection in patients from July 2020 to October 2021 were retrospectively reviewed.

Super-resolution generative adversarial networks with static T2*WI-based subject-specific learning to improve spatial difference sensitivity in fMRI activation.

The spatial resolution of fMRI is relatively poor and improvements are needed to indicate more specific locations for functional activities. Here, we propose a novel scheme, called Static T2*WI-based Subject-Specific Super Resolution fMRI (STSS-SRfMRI), to enhance the functional resolution, or ability to discriminate spatially adjacent but functionally different responses, of fMRI. The scheme is based on super-resolution generative adversarial networks (SRGAN) that utilize a T2*-weighted image (T2*WI) dataset as a training reference.

Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing.

Background: It is unclear whether deep-learning-based super-resolution technology (SR) or compressed sensing technology (CS) can accelerate magnetic resonance imaging (MRI) .

Purpose: To compare SR accelerated images with CS images regarding the image similarity to reference 2D- and 3D gradient-echo sequence (GRE) brain MRI.

Material And Methods: We prospectively acquired 1.

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.

We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality.One hundred five and 21 subjects were used as training and test sets, respectively.

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.

In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases.