Numerical and Clinical Evaluation of the Robustness of Open-source Networks for Parallel MR Imaging Reconstruction.

Purpose: Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datasets has been an open question. Here, we numerically and clinically evaluate the generalization of the reconstruction networks across various domains under clinically practical conditions and provide practical guidance on what points to consider when selecting models for clinical application.

Simultaneous Arterial and Venous Imaging Using 3D Quantitative Parameter Mapping.

Purpose: To increase the number of images that can be acquired in MR examinations using quantitative parameters, we developed a method for obtaining arterial and venous images with mapping of proton density (PD), RF inhomogeneity (B1), longitudinal relaxation time (T1), apparent transverse relaxation time (T2*), and magnetic susceptibility through calculation, all with the same spatial resolution.

Methods: The proposed method uses partially RF-spoiled gradient echo sequences to obtain 3D images of a subject with multiple scan parameters. The PD, B1, T1, T2*, and magnetic susceptibility maps are estimated using the quantification method we previously developed.

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo.

Purpose: MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T, T*, PD, and susceptibility) and B simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE).

Differentiation Between Multiple System Atrophy and Other Spinocerebellar Degenerations Using Diffusion Kurtosis Imaging.

Rationale And Objective: Differentiation between multiple system atrophy (MSA) and other spinocerebellar degenerations showing cerebellar ataxia is often difficult. Hence, we investigated whether magnetic resonance diffusion kurtosis imaging (DKI) could detect pathological changes that occur in these patients and be used for differential diagnosis.

Methods: Thirty-six subjects (12 patients with MSA accompanied by predominant cerebellar ataxia [MSA-C], 10 patients with spinocerebellar ataxias [SCAs] or sporadic adult-onset ataxia of unknown etiology [SAOA], and 14 healthy controls) were examined using 1.

The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging.

Purpose: Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain.

Differential diagnosis of parkinsonism by a combined use of diffusion kurtosis imaging and quantitative susceptibility mapping.

Purpose: We investigated whether diffusion kurtosis imaging (DKI) and quantitative susceptibility mapping (QSM) could detect pathological changes that occur in Parkinson's disease (PD), multiple system atrophy with predominant parkinsonism (MSA-P) or predominant cerebellar ataxia (MSA-C), and progressive supranuclear palsy syndrome (PSPS) and thus be used for differential diagnosis that is often difficult.

Methods: Seventy patients (41 with PD, 6 with MSA-P, 7 with MSA-C, 16 with PSPS) and 20 healthy controls were examined using a 3.0 T MRI scanner.

Detection of changes in the periaqueductal gray matter of patients with episodic migraine using quantitative diffusion kurtosis imaging: preliminary findings.

Introduction: The periaqueductal gray matter (PAG) is considered to play an important role in generating migraine, but findings from imaging studies remain unclear. Therefore, we investigated whether diffusion kurtosis imaging (DKI) can detect changes in the PAG of migraine patients.

Methods: We obtained source images for DKI from 20 patients with episodic migraine and 20 healthy controls using a 3 T magnetic resonance imaging scanner.

Optimization of Scan Parameters to Reduce Acquisition Time for Diffusion Kurtosis Imaging at 1.5T.

Purpose: To shorten acquisition of diffusion kurtosis imaging (DKI) in 1.5-tesla magnetic resonance (MR) imaging, we investigated the effects of the number of b-values, diffusion direction, and number of signal averages (NSA) on the accuracy of DKI metrics.

Methods: We obtained 2 image datasets with 30 gradient directions, 6 b-values up to 2500 s/mm(2), and 2 signal averages from 5 healthy volunteers and generated DKI metrics, i.

Differentiation among parkinsonisms using quantitative diffusion kurtosis imaging.

Differential diagnoses among Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy syndrome (PSPS) are often difficult. Hence, we investigated whether diffusion kurtosis imaging (DKI) could detect pathological changes that occur in these disorders and be used to differentiate between such patients. Fourteen patients (five with PD, four MSA, and five PSPS) and six healthy controls were examined using a 1.