Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice.

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer.

[Comparison of Image Quality Using Metal Artifact Reduction Magnetic Resonance Sequences at Different Static Magnetic Fields].

Purpose: This study aims to optimize the selection of the magnetic resonance imaging (MRI) scanners and metal artifact reduction magnetic resonance sequence (MARS) in patients with metallic implants by comparing the image quality of MARS at different static magnetic fields.

Method: A titanium alloy hip prostheses stem was covered with the pork phantom. Nifedipine 10 mg was placed as a simulated lesion near the hip joint of the phantom.

Evaluation of apparent diffusion coefficient of two-dimensional BLADE turbo gradient- and spin-echo diffusion-weighted imaging with a breast phantom.

This study aimed to evaluate the reliability of apparent diffusion coefficient (ADC) values generated with two-dimensional turbo gradient- and spin-echo with BLADE trajectory diffusion-weighted imaging (TGSE-BLADE-DWI) sequence using a breast diffusion phantom. TGSE-BLADE-DWI and single-shot spin-echo echo-planar imaging (SS-EPI-DWI) were performed using a 3.0 T magnetic resonance imaging scanner.

Comparison of TGSE-BLADE DWI, RESOLVE DWI, and SS-EPI DWI in healthy volunteers and patients after cerebral aneurysm clipping.

Diffusion-weighted magnetic resonance imaging is prone to have susceptibility artifacts in an inhomogeneous magnetic field. We compared distortion and artifacts among three diffusion acquisition techniques (single-shot echo-planar imaging [SS-EPI DWI], readout-segmented EPI [RESOLVE DWI], and 2D turbo gradient- and spin-echo diffusion-weighted imaging with non-Cartesian BLADE trajectory [TGSE-BLADE DWI]) in healthy volunteers and in patients with a cerebral aneurysm clip. Seventeen healthy volunteers and 20 patients who had undergone surgical cerebral aneurysm clipping were prospectively enrolled.

Diagnostic advantage of thin slice 2D MRI and multiplanar reconstruction of the knee joint using deep learning based denoising approach.

The purpose of this study is to evaluate whether thin-slice high-resolution 2D fat-suppressed proton density-weighted image of the knee joint using denoising approach with deep learning-based reconstruction (dDLR) with MPR is more useful than 3D FS-PD multi planar voxel image. Twelve patients who underwent MRI of the knee at 3T and 13 knees were enrolled. Denoising effect was quantitatively evaluated by comparing the coefficient of variation (CV) before and after dDLR.

Differences in apparent diffusion coefficients between normal brain echo-planar images and turbo spin-echo diffusion-weighted images with distortion correction.

Purpose: We performed echo-planar imaging (EPI) and turbo spin-echo (TSE) diffusion-weighted imaging (DWI) using magnetic resonance imaging (MRI) to obtain basic clinical data of the apparent diffusion coefficient (ADC) in various parts of normal brains and compared the datasets using our retrospective distortion correction technique.

Materials And Methods: The normal brains of 32 patients who underwent health check were scanned on a 1.5-T MRI instrument using EPI- and TSE-DWI.

Evaluation of motion artifacts in brain magnetic resonance images using convolutional neural network-based prediction of full-reference image quality assessment metrics.

Motion artifacts in magnetic resonance (MR) images mostly undergo subjective evaluation, which is poorly reproducible, time consuming, and costly. Recently, full-reference image quality assessment (FR-IQA) metrics, such as structural similarity (SSIM), have been used, but they require a reference image and hence cannot be used to evaluate clinical images. We developed a convolutional neural network (CNN) model to quantify motion artifacts without using reference images.

Cardiovascular magnetic resonance virtual tagging with B-spline-based free-form deformation.

Purpose: We developed a virtual tagging technique that reconstructs tagging images using the displacement field obtained by applying B-spline free-form deformation (FFD) between diastolic images and images of other cardiac phases in cardiac cine MRI. The purpose of this study was to validate its characteristics and usefulness in phantom and patient studies.

Methods: Digital phantoms simulating uniform and non-uniform wall motion models were created, and virtual tagging images were reconstructed with various matrix sizes and tag resolutions to evaluate the accuracy of FFD and the characteristics of the tags.

Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.

To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.

Diffusion-weighted breast magnetic resonance imaging with distortion correction using non-rigid image registration: a clinical study.

Our distortion correction method for diffusion-weighted breast images was applied to clinical studies to evaluate its practicality. We used b values of 0 and 1500 s/mm and dynamic images captured after injecting the contrast medium for magnetic resonance imaging (MRI). This method is based on non-rigid image registration, and dispenses with multiple scans, reducing examination time and patient burden.

Novel distortion correction method for diffusion-weighted imaging based on non-rigid image registration between low b value image and anatomical image.

Purpose: This study aimed to develop a novel technique for retrospective distortion correction based on non-rigid image registration in magnetic resonance diffusion image.

Methods: A 3.0 T MRI scanner with an 18-channel dedicated breast coil and the outer shell of the original breast phantom, which provided images with non-uniform fat-suppression based on clinical data were used.

Impact of the Number of Iterations in Compressed Sensing Reconstruction on Ultrafast Dynamic Contrast-enhanced Breast MR Imaging.

Purpose: To assess the impact of the number of iterations of compressed sensing (CS) reconstruction on the kinetic parameters and image quality in dynamic contrast-enhanced (DCE)-MRI of the breast, with prospectively undersampled CS-accelerated scans.

Materials And Methods: Breast examinations including ultrafast DCE-MRI using CS were conducted for 21 patients. Images were reconstructed with different numbers of iterations.

Ultrafast dynamic contrast-enhanced mri of the breast using compressed sensing: breast cancer diagnosis based on separate visualization of breast arteries and veins.

Purpose: To evaluate the feasibility of ultrafast dynamic contrast-enhanced (UF-DCE) magnetic resonance imaging (MRI) with compressed sensing (CS) for the separate identification of breast arteries/veins and perform temporal evaluations of breast arteries and veins with a focus on the association with ipsilateral cancers.

Materials And Methods: Our Institutional Review Board approved this study with retrospective design. Twenty-five female patients who underwent UF-DCE MRI at 3T were included.