Age-related brain-region-specific changes in diffusion anisotropy in subarachnoid space of young adults.

The glymphatic system theory postulates that brain waste is removed through the cerebrospinal fluid (CSF) flow. According to this theory, CSF in the subarachnoid space (SAS) moves to the perivascular space around the penetrating arteries, flows into parenchyma to mix with interstitial fluid and brain waste, and then moves to the perivenous space to be flushed out of the brain. Despite the controversies about the glymphatic theory, it is clear that SAS plays a key role in waste clearance.

Quantitative mapping of renal oxygen consumption using pseudo-continuous arterial spin labeling and quantitative susceptibility mapping in humans.

Purpose: To propose a new method for quantitatively mapping the renal metabolic rate of oxygen (RMRO) and to evaluate the proposed method using a caffeine challenge.

Theory And Methods: Pseudo-continuous arterial spin labeling (pCASL) and QSM sequences were used to obtain MR images in the kidney. Six healthy volunteers were scanned on caffeine and control days.

Noninvasive ROS imaging and drug delivery monitoring in the tumor microenvironment.

Reactive oxygen species (ROS) that are overproduced in certain tumors can be considered an indicator of oxidative stress levels in the tissue. Here, we report a magnetic resonance imaging (MRI)-based probe capable of detecting ROS levels in the tumor microenvironment (TME) using ROS-responsive manganese ion (Mn)-chelated, biotinylated bilirubin nanoparticles (Mn@bt-BRNPs). These nanoparticles are disrupted in the presence of ROS, resulting in the release of free Mn, which induces T1-weighted MRI signal enhancement.

CSF pulsations measured in Parkinson's disease patients using EPI-based fMRI data.

Introduction: Cerebrospinal fluid (CSF) flow is involved in brain waste clearance and may be impaired in neurodegenerative diseases such as Parkinson's disease. This study aims to investigate the relationship between the CSF pulsation and the development of dementia in Parkinson's disease (PD) patients using EPI-based fMRI.

Methods: We measured CSF pulsation in the 4th ventricle of 17 healthy controls and 35 PD patients using a novel CSF pulsation index termed "CSFpulse" based on echo-planar imaging (EPI)-based fMRI.

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

Purpose: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.

Methods: The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC-MRI time series and the arterial input functions.

Longitudinal Magnetic Resonance Imaging with ROS-Responsive Bilirubin Nanoparticles Enables Monitoring of Nonalcoholic Steatohepatitis Progression to Cirrhosis.

Despite the vital importance of monitoring the progression of nonalcoholic fatty liver disease (NAFLD) and its progressive form, nonalcoholic steatohepatitis (NASH), an efficient imaging modality that is readily available at hospitals is currently lacking. Here, a new magnetic-resonance-imaging (MRI)-based imaging modality is presented that allows for efficient and longitudinal monitoring of NAFLD and NASH progression. The imaging modality uses manganese-ion (Mn)-chelated bilirubin nanoparticles (Mn@BRNPs) as a reactive-oxygen-species (ROS)-responsive MRI imaging probe.

CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer.

Background: MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming manual contouring step.

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI.

Purpose: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI).

Method: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm) was performed, followed by DL reconstruction and super resolution processing.

Distortion correction using topup algorithm by single k-space (TASK) for echo planar imaging.

Distortion of echo planar imaging (EPI) can be corrected using B field maps, which can be estimated with the topup algorithm that requires two EPI images with opposite distortions. In this study, we propose a new algorithm, termed topup algorithm by single K-space (TASK), to generate two input images from a single k-space for the topup algorithm to correct EPI distortions. The centric EPI contains the opposite phase-encoding polarities in one k-space, which can be divided into two halves with opposite distortions.

Measurement of changes in cerebrospinal fluid pulsation after traumatic brain injury using echo-planar imaging-based functional MRI.

Traumatic brain injury (TBI) is a major public health concern worldwide, with a high incidence and a significant impact on morbidity and mortality. The alteration of cerebrospinal fluid (CSF) dynamics after TBI is a well-known phenomenon; however, the underlying mechanisms and their implications for cognitive function are not fully understood. In this study, we propose a new approach to studying the alteration of CSF dynamics in TBI patients.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques.

Prospective classification of Alzheimer's disease conversion from mild cognitive impairment.

Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is important to early detect whether the disease converts from mild cognitive impairment (MCI) which is a prodromal phase of AD. With the advance in brain imaging techniques, various machine learning algorithms have become able to predict the conversion from MCI to AD by learning brain atrophy patterns.

Attention fusion network with self-supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols.

Background: Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on Magnetic resonance imaging and computed tomography (CT), which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images.

An agonistic anti-Tie2 antibody suppresses the normal-to-tumor vascular transition in the glioblastoma invasion zone.

Tumor progression is intimately associated with the vasculature, as tumor proliferation induces angiogenesis and tumor cells metastasize to distant organs via blood vessels. However, whether tumor invasion is associated with blood vessels remains unknown. As glioblastoma (GBM) is featured by aggressive invasion and vascular abnormalities, we characterized the onset of vascular remodeling in the diffuse tumor infiltrating zone by establishing new spontaneous GBM models with robust invasion capacity.

Measuring glomerular blood transfer rate in kidney using diffusion-weighted arterial spin labeling.

Purpose: To propose a two-compartment renal perfusion model for calculating glomerular blood transfer rate ( ) as a new measure of renal function.

Theory: The renal perfusion signal was divided into preglomerular and postglomerular flows according to flow velocity. By analyzing perfusion signals acquired with and without diffusion gradients, we estimated , the blood transfer rate from the afferent arterioles into the glomerulus.

Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning.

Background: Acceleration of MR imaging (MRI) is a popular research area, and usage of deep learning for acceleration has become highly widespread in the MR community. Joint acceleration of multiple-acquisition MRI was proven to be effective over a single-acquisition approach. Also, optimization in the sampling pattern demonstrated its advantage over conventional undersampling pattern.

Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization.

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique that provides the spatial distribution of magnetic susceptibility values of tissues. QSMs can be obtained by deconvolving the dipole kernel from phase images, but the spectral nulls in the dipole kernel make the inversion ill-posed. In recent years, deep learning approaches have shown a comparable QSM reconstruction performance to the classic approaches, in addition to the fast reconstruction time.

Measurement of CSF pulsation from EPI-based human fMRI.

It is recently discovered that the glymphatic system and meningeal lymphatic system are the primary routes for the clearance of brain waste products. The CSF flow is part of these systems, facilitating the clearance procedure. Nonetheless, the relationship between CSF flow and brain functional activity has been underexplored.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer.

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated.

Single-shot pseudo-centric EPI for magnetization-prepared imaging.

Purpose: To implement a single-shot centric-reordered EPI (1sh-CenEPI), which reduces TE significantly, thus enabling to improve SNR for magnetization-prepared imaging.

Methods: We proposed a 1sh-CenEPI in which grouped oscillating readout gradients, phase-encoding blips within each group, and big phase-encoding jumps between two consecutive groups are incorporated to encode whole k-space from the center to the edges in a single shot. The concept was tested on phantoms and human brains at 3 T.