Chemical shift-encoded MRI with compressed sensing combined with parallel imaging for proton density fat fraction measurement of the lumbar vertebral bone marrow.

We aimed to investigate the accuracy of proton density fat fraction (PDFF) measurement of the lumbar vertebral bone marrow using chemical shift-encoded magnetic resonance imaging (CSE-MRI) with compressed sensing combined with parallel imaging (CSPI). This study recruited a commercially available phantom, and 43 patients. Fully sampled data without CSPI and under-sampled data with CSPI acceleration factors of 2.

Chemical Shift-Encoded MRI of the Lumbar Vertebral Bone Marrow for Detecting Osteoporosis With Low Trabecular Bone Quality in Patients With Breast Cancer Receiving Aromatase Inhibitors.

Background: Osteoporosis with low trabecular bone quality (OLB) in patients with breast cancer receiving aromatase inhibitor (AI) therapy is associated with an increased risk of vertebral fractures. The capability of chemical shift-encoded MRI (CSE-MRI) in detecting OLB needs to be investigated.

Purpose: To assess the diagnostic performance of proton density fat fraction (PDFF) and R2* measurements from CSE-MRI for detecting OLB in postmenopausal women with breast cancer undergoing AI therapy.

Analysis of the enzymatic degradation of lysozyme fibrils using a combination method of non-denaturing gel electrophoresis and double staining with Coomassie Brilliant Blue G-250 and R-250 dyes.

The Amyloid fibrils of proteins are involved in various diseases, such as Alzheimer's disease. To suppress such amyloid fibrils, it is essential to develop methods to elucidate their enzymatic degradation process. Lysozyme in egg white has been well studied as a model protein of amyloid fibrils.

[A Resected Case of Angiomyolipoma Difficult to Be Distinguished from Hepatocellular Carcinoma].

A 42-year-old man complaining of left back pain was admitted to our hospital. The hepatis B and C surface antigens were negative. The serum levels of tumor markers were within the reference ranges.

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.