Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution.

Purpose: To evaluate and compare the image quality and lesion conspicuity of prostate T2-weighted imaging (T2WI) using four reconstruction methods: conventional Sensitivity Encoding (SENSE), compressed sensing (CS), model-based deep learning reconstruction (DL), and deep learning super-resolution reconstruction (SR).

Methods: This retrospective study included 49 patients who underwent multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) for suspected prostate cancer. Axial T2WI was acquired using two protocols: conventional SENSE and CS-based acquisition.

The Impact of Model-based Deep-learning Reconstruction Compared with that of Compressed Sensing-Sensitivity Encoding on the Image Quality and Precision of Cine Cardiac MR in Evaluating Left-ventricular Volume and Strain: A Study on Healthy Volunteers.

Purpose: To evaluate the effect of model-based deep-learning reconstruction (DLR) compared with that of compressed sensing-sensitivity encoding (CS) on cine cardiac magnetic resonance (CMR).

Methods: Cine CMR images of 10 healthy volunteers were obtained with reduction factors of 2, 4, 6, and 8 and reconstructed using CS and DLR. The visual image quality scores assessed sharpness, image noise, and artifacts.

Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.

Objectives: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.

Methods: We retrospectively analyzed 91 cases of laryngeal or hypopharyngeal cancer treated surgically at our hospital during the period April 2010 through May 2023, and we divided the cases into datasets for training (n = 61) and testing (n = 30). We reviewed the CT images and pathological diagnoses in all cases to determine the invasion positive- or negative-status as a ground truth.

Association between RNF213 p.R4810K and Progression of Cerebral Artery Negative Remodeling in Moyamoya Disease.

Negative remodeling, characterized by a decrease in the outer diameter of the terminal (C1) segment of the internal carotid artery and the proximal (M1) segment of the middle cerebral artery, is a hallmark of moyamoya disease. However, the role of the disease-susceptibility gene RNF213 in negative remodeling in moyamoya disease remains unclear. This study investigated the effect of RNF213 p.

Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.

Purpose: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs) T2-weighted imaging (T2WI).

Materials And Methods: We retrospectively analyzed the cases of 43 patients who underwent head/neck Fs-T2WI for the assessment of their head and neck lesions. All patients underwent two sets of Fs-T2WI scans with conventional- and DL-based reconstruction.

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging.

Effect of a Training System Utilizing 3-Dimensionally Printed Patient-Specific Vascular Models on Endovascular Catheterization Performance.

This study aimed to determine the effect of simulation training using a 3-dimensionally (3D) printed patient-specific vascular model on the advanced vascular catheterization skills of experienced interventional radiologists. Two specific anatomical types of 3D-printed patient-specific models from 2 patients with challenging celiac axis arterial anatomy were constructed. The Global Rating Scale of Endovascular Performance (GRS-EP) was used to evaluate vascular insertion skills.

Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.

Ventricular tachycardia (VT) is a severe arrhythmia commonly treated with implantable cardioverter defibrillators, antiarrhythmic drugs and catheter ablation (CA). Although CA is effective in reducing recurrent VT, its impact on survival remains uncertain, especially in patients with extensive scarring. Stereotactic arrhythmia radioablation (STAR) has emerged as a novel treatment for VT in patients unresponsive to CA, leveraging techniques from stereotactic body radiation therapy used in cancer treatments.

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance.

The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.

The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses.

Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

Background: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images.

Methods: Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status.

Predictive value of the hemispheric magnetic resonance angiography score on the development of indirect pial synangiosis after combined revascularization surgery for adult moyamoya disease.

Purpose: It is difficult to precisely predict indirect bypass development in the context of combined bypass procedures in moyamoya disease (MMD). We aimed to investigate the predictive value of magnetic resonance angiography (MRA) signal intensity in the peripheral portion of the major cerebral arteries for indirect bypass development in adult patients with MMD.

Methods: We studied 93 hemispheres from 62 adult patients who underwent combined direct and indirect revascularization between 2005 and 2019 and genetic analysis for RNF213 p.

Comparison of Echo-Planar Imaging and Compressed Sensing in the Estimation of Flow Metrics from Aortic 4D Flow MR Imaging: A Healthy Volunteer Study.

Purpose: Prolonged scanning of time-resolved 3D phase-contrast MRI (4D flow MRI) limits its routine use in clinical practice. An echo-planar imaging (EPI)-based sequence and compressed sensing can reduce the scan duration. We aimed to determine the impact of EPI for 4D flow MRI on the scan duration, image quality, and quantitative flow metrics.