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. Our results show that the number of registered radiotherapy institutions and cases increased by ~50% in 2019 compared to those in 2015 (to 113 institutions and 60 575 cases, respectively). Among the survey item categories, the registration rate was lowest for prognostic information (13.9% on average over the 7-year period). In terms of the Japanese Society for Radiation Oncology disease site, the breast; lung, trachea and mediastinum and urogenital sites accounted for >50% of the total cases. The average survival and mortality rates over the 7-year study period were 67.4 and 17.4%, respectively. The X-ray radiotherapy completion rate exceeded 90% for all years and across all disease categories. 192Ir-based brachytherapy and 223Ra-based radionuclide therapy accounted for an average of 61.9 and 44.6%, respectively, of all corresponding cases over the 7-year period. In conclusion, this survey enables us to infer the actual status of radiotherapy in Japan based on the analysis of relevant nationwide data.

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. The total radiation dose was 66-79.6 Gy (relative biological effectiveness [RBE]). Over a median follow up of 31 months, the 3-year rates of overall survival (OS) and local control (LC) were 100% and 94.4%, respectively. No grade ≥ 3 toxicities were observed. Based on a random effects model, a meta-analysis including the present results revealed 3-year OS rates after SBRT and PBT of 75.3% (95% CI: 57.3-86.6) and 94.3% (95% CI: 86.8-97.6), respectively (P = 0.005), but the difference in LC rates between the two methods was not observed (P = 0.63). PBT is expected to have similar if not better treatment results compared with SBRT for primary renal cancer. In particular, PBT was shown to be effective even for large RCC and could provide a therapeutic option when SBRT is not indicated.

Deep Learning-based Post Hoc CT Denoising for the Coronary Perivascular Fat Attenuation Index.

RATIONALE AND OBJECTIVES: Coronary inflammation related to high-risk hemorrhagic plaques can be captured by the perivascular fat attenuation index (FAI) using coronary computed tomography angiography (CCTA). Since the FAI is susceptible to image noise, we believe deep learning (DL)-based post hoc noise reduction can improve diagnostic capability. We aimed to assess the diagnostic performance of the FAI in DL-based denoised high-fidelity CCTA images compared with coronary plaque magnetic resonance imaging (MRI) delivered high-intensity hemorrhagic plaques (HIPs). MATERIALS AND METHODS: We retrospectively reviewed 43 patients who underwent CCTA and coronary plaque MRI. We generated high-fidelity CCTA images by denoising the standard CCTA images using a residual dense network that supervised the denoising task by averaging three cardiac phases with nonrigid registration. We measured the FAIs as the mean CT value of all voxels (range of -190 to -30 HU) located within a radial distance from the outer proximal right coronary artery wall. The diagnostic reference standard was defined as HIPs (high-risk hemorrhagic plaques) using MRI. The diagnostic performance of the FAI in the original and denoised images was assessed using receiver operating characteristic curves. RESULTS: Of 43 patients, 13 had HIPs. The denoised CCTA improved the area under the curve (0.89 [95% confidence interval (CI) 0.78-0.99]) of the FAI compared with that in the original image (0.77 [95% CI, 0.62-0.91], p = 0.008). The optimal cutoff value for predicting HIPs in denoised CCTA was -69 HU with 0.85 (11/13) sensitivity, 0.79 (25/30) specificity, and 0.80 (36/43) accuracy. CONCLUSION: DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs.

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 1.3× and 2.0× faster 2D and 3D GRE images of 20 volunteers from the reference time by reducing the matrix size or increasing the CS factor. For SR, we trained the generative adversarial network (GAN), upscaling the low-resolution images to the reference images with twofold cross-validation. We compared the structural similarity (SSIM) index of accelerated images to the reference image. The rate of incorrect answers of a radiologist discriminating faster and reference image was used as a subjective image similarity (ISM) index. RESULTS: The SR demonstrated significantly higher SSIM than the CS (SSIM=0.9993-0.999 vs. 0.9947-0.9986; P < 0.001). In 2D GRE, it was challenging to discriminate the SR image from the reference image, compared to the CS (ISM index 40% vs. 17.5% in 1.3×; P = 0.039 and 17.5% vs. 2.5% in 2.0×; P = 0.034). In 3D GRE, the CS revealed a significantly higher ISM index than the SR (22.5% vs. 2.5%; P = 0.011) in 2.0 × faster images. However, the ISM index was identical for the 2.0× CS and 1.3× SR (22.5% vs. 27.5%; P = 0.62) with comparable time costs. CONCLUSION: The GAN-based SR outperformed CS in image similarity with 2D GRE for MRI acceleration. In addition, CS was more advantageous in 3D GRE than SR.

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. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. RESULTS: DLNR CCTAs showed 64.5% lower image noise (P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison (P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77-0.99) with original CCTAs. CONCLUSION: Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.

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. The efficacy of the scheme was evaluated through comparison with the activation maps obtained from the raw unpreprocessed functional data (raw fMRI). MRI images were acquired from 30 healthy volunteers using a 3 Tesla scanner. The modified SRGAN reconstructs a high-resolution image series from the original low-resolution fMRI data. For quantitative comparison, several metrics were calculated for both the STSS-SRfMRI and the raw fMRI activation maps. The ability to distinguish between two different finger-tapping tasks was significantly higher [p = 0.00466] for the reconstructed STSS-SRfMRI images than for the raw fMRI images. The results indicate that the functional resolution of the STSS-SRfMRI scheme is superior, which suggests that the scheme is a potential solution to realizing higher functional resolution in fMRI images obtained using 3T MRI.

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. Preaveraged images obtained in 100 patients as inputs and averaged images as ground truths were used to supervise a residual dense network (RDN). The original single-shot image, standard averaged image, RDN-denoised original (DLoriginal) image, and RDN-denoised averaged (DLave) image of holdout cases were compared. In 40 patients, the CT value and image noise in the left ventricular cavity and myocardium were assessed. The segmental presence of MDE in the remaining 40 patients who underwent reference LGE MRI was evaluated. The sensitivity, specificity, and accuracy of each type of CT image and the improvement in accuracy achieved with the RDN were assessed using odds ratios (ORs) estimated with the generalized estimation equation. Results Overall, 180 patients (median age, 66 years [IQR, 53-74 years]; 107 men) were included. The RDN reduced image noise to 28% of the original level while maintaining equivalence in the CT values (P < .001 for all). The sensitivity, specificity, and accuracy of the original images were 77.9%, 84.4%, and 82.3%, of the averaged images were 89.7%, 87.9%, and 88.5%, of the DLoriginal images were 93.1%, 87.5%, and 89.3%, and of the DLave images were 95.1%, 93.1%, and 93.8%, respectively. DLoriginal images showed improved accuracy compared with the original images (OR, 1.8 [95% CI: 1.2, 2.9]; P = .011) and DLave images showed improved accuracy compared with the averaged images (OR, 2.0 [95% CI: 1.2, 3.5]; P = .009). Conclusion The proposed denoising network supervised with averaged CT images reduced image noise and improved the diagnostic performance for myocardial delayed enhancement CT. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

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. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests.CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001).DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.

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. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.