Renal pelvic plasmacytoid subtype urothelial carcinoma accompanied with solitary mammary metastasis.

A 72-year-old female was referred to our institution for further evaluation of right renal tumor detected during work-up for macroscopic hematuria in other hospital. CT urography performed at our institution suggested renal pelvic tumor. Voiding cytology was atypical.

Visualization of small brain nuclei with a high-spatial resolution, clinically available whole-body PET scanner.

Objective: To verify the visibility of physiological F-fluorodeoxyglucose (F-FDG) uptake in nuclei in and around the brainstem by a whole-body (WB) silicon photomultiplier positron emission tomography (SiPM-PET) scanner with point-spread function (PSF) reconstruction using various iteration numbers.

Methods: Ten healthy subjects (5 men, 5 women; mean age, 56.0 ± 5.

DOCK2 regulates MRGPRX2/B2-mediated mast cell degranulation and drug-induced anaphylaxis.

Background: Drug-induced anaphylaxis is triggered by the direct stimulation of mast cells (MCs) via Mas-related G protein-coupled receptor X2 (MRGPRX2; mouse ortholog MRGPRB2). However, the precise mechanism that links MRGPRX2/B2 to MC degranulation is poorly understood. Dedicator of cytokinesis 2 (DOCK2) is a Rac activator predominantly expressed in hematopoietic cells.

Association between forefoot pain and sesamoid rotation angle determined using a weight-bearing plantar ultrasound imaging device.

Forefoot pain is a common symptom for several foot problems. This study aimed to determine whether parameters of forefoot structure (hallux valgus angle (HVA), transverse arch height (TAH) and sesamoid rotation angle (SRA)) are associated with forefoot pain. 547 feet of adult women were divided into two groups: without pain (n = 472) and with pain (n = 75).

Massively parallel single-cell genomics of microbiomes in rice paddies.

Plant growth-promoting microbes (PGPMs) have attracted increasing attention because they may be useful in increasing crop yield in a low-input and sustainable manner to ensure food security. Previous studies have attempted to understand the principles underlying the rhizosphere ecology and interactions between plants and PGPMs using ribosomal RNA sequencing, metagenomic sequencing, and genome-resolved metagenomics; however, these approaches do not provide comprehensive genomic information for individual species and do not facilitate detailed analyses of plant-microbe interactions. In the present study, we developed a pipeline to analyze the genomic diversity of the rice rhizosphere microbiome at single-cell resolution.

DeepPVC: prediction of a partial volume-corrected map for brain positron emission tomography studies via a deep convolutional neural network.

Background: Partial volume correction with anatomical magnetic resonance (MR) images (MR-PVC) is useful for accurately quantifying tracer uptake on brain positron emission tomography (PET) images. However, MR segmentation processes for MR-PVC are time-consuming and prevent the widespread clinical use of MR-PVC. Here, we aimed to develop a deep learning model to directly predict PV-corrected maps from PET and MR images, ultimately improving the MR-PVC throughput.

Brain partial volume correction with point spreading function reconstruction in high-resolution digital PET: comparison with an MR-based method in FDG imaging.

Objective: In quantitative positron emission tomography (PET) of the brain, partial volume effect due mainly to the finite spatial resolution of the PET scanner (> 3 mm full width at half maximum [FWHM]) is a primary source of error in the measurement of tracer uptake, especially in small structures such as the cerebral cortex (typically < 3 mm thickness). The aim of this study was to evaluate the partial volume correction (PVC) performance of point spread function-incorporated reconstruction (PSF reconstruction) in combination with the latest digital PET scanner. This evaluation was performed through direct comparisons with magnetic resonance imaging (MR)-based PVC (used as a reference method) in a human brain study.

A review on AI in PET imaging.

Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET).

Effect of hematocrit on cerebral blood flow measured by pseudo-continuous arterial spin labeling MRI: A comparative study with O-water positron emission tomography.

Introduction: In cerebral blood flow (CBF) quantification with pseudo-continuous arterial spin labeling (pCASL) MRI, arterial blood T1 (T1a) is usually fixed to a typical value (e.g., 1650 ms).

Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images.

Purpose: Oxygen extraction fraction (OEF) is a biomarker for the viability of brain tissue in ischemic stroke. However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps.

Iterative framework for image registration and partial volume correction in brain positron emission tomography.

Imprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration.

Error propagation analysis of seven partial volume correction algorithms for [F]THK-5351 brain PET imaging.

Background: Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome.

Methods: We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer's disease were simulated from individual PET and MR images.

Association Between Functional Movement Screen Scores and Injuries in Male College Basketball Players.

Context: The functional movement screen (FMS) is an assessment tool for movement dysfunction, which is used to reduce the risk of injury. Although the relationship between the FMS composite score and injuries has been extensively studied, the association between FMS scores and injuries in only college basketball players remains unknown.

Objective: To examine the relationship between the FMS score and injuries in basketball players.

Spatial coefficient of variation in pseudo-continuous arterial spin labeling cerebral blood flow images as a hemodynamic measure for cerebrovascular steno-occlusive disease: A comparative O positron emission tomography study.

Pseudo-continuous arterial spin labeling (pCASL) is a completely non-invasive method of cerebral perfusion measurement. However, cerebral blood flow (CBF) quantification is hampered by arterial transit artifacts characterized by bright vascular signals surrounded by decreased signals in tissue regions, which commonly appear in patients with reduced cerebral perfusion pressure. The spatial coefficient of variation (CoV) of pCASL CBF images has been proposed as an alternative region-of-interest (ROI)-based hemodynamic measure to predict prolonged arterial transit time (ATT).