Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.

The aim of this study was to investigate the effect of iterative motion correction (IMC) on reducing artifacts in brain magnetic resonance imaging (MRI) with deep learning reconstruction (DLR). The study included 10 volunteers (between September 2023 and December 2023) and 30 patients (between June 2022 and July 2022) for quantitative and qualitative analyses, respectively. Volunteers were instructed to remain still during the first MRI with fluid-attenuated inversion recovery sequence (FLAIR) and to move during the second scan.

Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict.

Rationale And Objectives: To determine if super-resolution deep learning reconstruction (SR-DLR) improves the depiction of cranial nerves and interobserver agreement when assessing neurovascular conflict in 3D fast asymmetric spin echo (3D FASE) brain MR images, as compared to deep learning reconstruction (DLR).

Materials And Methods: This retrospective study involved reconstructing 3D FASE MR images of the brain for 37 patients using SR-DLR and DLR. Three blinded readers conducted qualitative image analyses, evaluating the degree of neurovascular conflict, structure depiction, sharpness, noise, and diagnostic acceptability.

Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.

The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.

Clinical Impact of Deep Learning Reconstruction in MRI.

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio.

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study.

Purpose: To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration.

Materials And Methods: Twenty-one healthy volunteers underwent MRI of the right knee on a 1.

Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.

Purpose: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR.

Methods: In this prospective study, 21 volunteers (mean age: 42.

Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.

Purpose: This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR).

Methods: Proton density-weighted images of the right knee of 27 healthy volunteers were obtained by 3T and 1.

Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.

Purpose: To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).

Methods: Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled.

Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.

Background: T2-weighted imaging (T2WI) is a key sequence of MRI studies of the pancreas. The single-shot fast spin echo (single-shot FSE) sequence is an accelerated form of T2WI. We hypothesized that denoising approach with deep learning-based reconstruction (dDLR) could facilitate accelerated breath-hold thin-slice single-shot FSE MRI, and reveal the pancreatic anatomy in detail.

Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes.

Objectives: To investigate whether deep learning reconstruction (DLR) provides improved cervical spine MR images using a 1.5 T unit in the evaluation of degenerative changes without increasing imaging time.

Methods: This study included 21 volunteers (age 42.

Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.

Purpose: The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T.

Materials And Methods: In this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.

Objectives: To assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRCP) and breath-hold 3D MRCP (BH-MRCP) with and without denoising procedure using deep learning-based reconstruction (dDLR) at 1.5 T.

Methods: Forty-two patients underwent MRCP at 1.

Effects of Gadolinium Deposition in the Brain on Motor or Behavioral Function: A Mouse Model.

Background Recent studies showing gadolinium deposition in multiple organs have raised concerns about the safety of gadolinium-based contrast agents (GBCAs). Purpose To explore whether gadolinium deposition in brain structures will cause any motor or behavioral alterations. Materials and Methods This study was performed from July 2019 to December 2020.

Embolization of visceral arterial aneurysms: Simulation with 3D-printed models.

Objectives: The present technical article aimed to describe the efficacy of three-dimensional (3D)-printed hollow vascular models as a tool in the preoperative simulation of endovascular embolization of visceral artery aneurysms.

Methods: From November 2015 to November 2016, four consecutive endovascular treatments of true visceral artery aneurysms were preoperatively simulated with 3D-printed hollow models. The mean age of the patients (one male and three females) was 54 (range: 40-71) years.

Application of CT texture analysis to assess the localization of primary aldosteronism.

We performed present study to investigate whether the localization of primary aldosteronism (PA) can be predicted using quantitative texture analysis on unenhanced computed tomography (CT). Plain CT data of 82 PA patients (54 unilateral (right-sided:left-sided = 24:30), 28 bilateral) were analyzed retrospectively. After semi-automatically setting the region of interest to include the whole adrenal gland, texture analyses were performed with or without a Laplacian of Gaussian filter with various spatial scaling factors (SSFs).

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study.

Objectives: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.

Methods: This clinical retrospective study was approved by the institutional review board, and the requirement for written informed consent was waived. Midsagittal T1-weighted MRI of a total of 419 subjects (125 Parkinson's disease (PD), 98 progressive supranuclear palsy (PSP), and 54 multiple system atrophy with predominant parkinsonian features (MSA-P) patients, and 142 normal subjects) between January 2012 and April 2016 was retrospectively assessed.

The inhibitory effect of gadoxetate disodium on hepatic transporters: a study using indocyanine green.

Objectives: To assess the inhibitory effect of gadoxetate disodium on the transporter system using indocyanine green (ICG).

Materials And Methods: Groups of six female B6 Albino mice were injected with the test agent (0.62 mmol/kg gadoxetate disodium) or phosphate-buffered saline (control) 10 min before injection of ICG.

Quantitative computed tomography texture analyses for anterior mediastinal masses: Differentiation between solid masses and cysts.

Objectives: To investigate whether solid anterior mediastinal masses could be differentiated from cysts using quantitative computed tomography (CT) texture analyses in unenhanced CT (UECT) or contrast enhanced CT (CECT).

Materials And Methods: This clinical retrospective study included 76 UECT images (40 men and 36 women, 28 cystic (mean diameter, 29.5 mm) and 48 solid (mean diameter, 48.

Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis.

Objectives: To determine if texture analysis of non-contrast-enhanced CT (NECT) images is able to predict nonalcoholic steatohepatitis (NASH).

Methods: NECT images from 88 patients who underwent a liver biopsy for the diagnosis of suspected NASH were assessed and texture feature parameters were obtained without and with filtration. The patient population was divided into a predictive learning dataset and a validation dataset, and further divided into groups according to the prediction of liver fibrosis as assessed by hyaluronic acid levels.

Vaginal delivery-related changes in the pelvic organ position and vaginal cross-sectional area in the general population.

Purpose: Evaluate the effect of vaginal delivery on pelvic organ positions and vaginal cross-sectional areas.

Methods: MRI of 119 premenopausal women were grouped according to the number of deliveries. The distances from the three 3-reference points (bladder, uterus, and rectum) to two 2-lines (pubococcygeal-line (PCL) and midpubic-line (MPL)), length of H- and M-lines and vaginal cross-sectional area were compared between the groups.

Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator.

We assessed the relationship between the heterogeneity of HCC on preoperative non-contrast-enhanced CT and patient prognosis. The heterogeneity of CT images from 122 patients was assessed and texture feature parameters such as mean, standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis were obtained using filtration. The relationship between CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed.

Gadoxetate disodium-induced tachypnoea and the effect of dilution method: a proof-of-concept study in mice.

Objectives: To directly investigate the rapid respiratory effect of gadoxetate disodium in an experimental study using mice.

Methods: After confirming the steady respiratory state under general anaesthesia, eight mice were injected with all test agents in the following order: phosphate-buffered saline (A, control group), 1.25 mmol/kg of gadoteridol (B) or gadopentetate dimeglumine (C), or 0.

Influence of outflow-obstructed liver volume and venous communication development: A three-dimensional volume study in living donors.

Living donor liver transplantation using the left liver graft with the middle hepatic vein (MHV) is a well-established procedure. Following such procedures, outflow obstruction occurs in remnant livers. However, the effects of the outflow-obstructed liver volume (LV ), with or without venous communication development, remain unclear.