Magnetic resonance imaging最新文献

Purpose

This study aimed to assess changes in white matter microstructure among patients undergoing obstructive sleep apnea hypopnea syndrome (OSAHS) complicated by cognitive impairment through neurite orientation dispersion and density imaging (NODDI), and evaluate the relationship to cognitive impairment as well as the diagnostic performance in early intervention.

Methods

Totally 23 OSAHS patients, 43 OSAHS patients complicated by cognitive impairment, and 15 healthy controls were enrolled in OSA, OSACI and HC groups of this work. NODDI toolbox and FMRIB's Software Library (FSL) were used to calculate neurite density index (NDI), Fractional anisotropy (FA), volume fraction of isotropic water molecules (Viso), and orientation dispersion index (ODI). Tract-based spatial statistics (TBSS) were carried out to examine the above metrics with one-way ANOVA. This study explored the correlations of the above metrics with mini-mental state examination (MMSE), and montreal cognitive assessment (MoCA) scores. Furthermore, receiver operating characteristic (ROC) curves were plotted. Meanwhile, area under curve (AUC) values were calculated to evaluate the diagnostic performance of the above metrics.

Results

NDI, ODI, Viso, and FA were significantly different among different brain white matter regions, among which, difference in NDI showed the greatest statistical significance. In comparison with HC group, OSA group had reduced NDI and ODI, whereas elevated Viso levels. Conversely, compared to the OSA group, the OSACI group displayed a slight increase in NDI and ODI values, which remained lower than HC group, viso values continued to rise. Post-hoc analysis highlighted significant differences in these metrics, except for FA, which showed no notable changes or correlations with neuropsychological tests. ROC analysis confirmed the diagnostic efficacy of NDI, ODI, and Viso with AUCs of 0.6908, 0.6626, and 0.6363, respectively, whereas FA's AUC of 0.5042, indicating insufficient diagnostic efficacy.

Conclusions

This study confirmed that NODDI effectively reveals microstructural changes in white matter of OSAHS patients with cognitive impairment, providing neuroimaging evidence for early clinical diagnosis and intervention.

【Purpose】

Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation.

【Methods】

We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated.

【Results】

Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (p < 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory).

【Conclusions】

Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.

Purpose

Methods

Results

Conclusions

Purpose

To establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI).

Methods

Seventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition.

Results

A large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05.

Conclusion

Histopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.

We report use of a dual-density dielectric barrier surrounding a detachable high-pass radiofrequency (RF) birdcage coil to achieve an order-of-magnitude reduction of acoustic noise in a high-performance head gradient system. The barrier consisted of a 4.5 mm-thick mass-loaded vinyl and a 6 mm-thick polyurethane foam. It was inserted into the radial gap between the birdcage coil and the RF shield in a prototype head-only gradient system at 3 T. More than 9 dB_A reduction of sound pressure level was achieved on the average with representative, high acoustic-noise imaging sequences. Increased acoustic damping was apparent from acoustic impulse response functions. High dielectric constant of the mass-loaded vinyl effectively added distributed capacitance to the birdcage coil, lowering the resonance frequency, but not seriously degrading the RF transmission performance. The barrier occupied the radial space normally used for air cooling of the RF coil and the RF shield. The resulting omission of air cooling was found to be acceptable with efficient gradient thermal management and use of a high-resistivity RF shield for eddy current reduction. The proposed method can improve patient experience while preserving image quality in a high-power head-only gradient system.

Background

Despite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the “fast” compartment, non-rigid registration, and exclusion voxels with poor fit quality.

Methods

From June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm²). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations.

Results

All results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (−0.01 ± 0.03) × 10⁻³ mm²/s, (0.70 ± 10.40) × 10⁻³ mm²/s, and (−0.02 ± 0.04) × 10⁻³ mm²/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10⁻³ mm²/s, (−0.01 ± 0.21) × 10⁻³ mm²/s, (−13.37 ± 56.19) × 10⁻³ mm²/s, and (−0.01 ± 0.16) × 10⁻³ mm²/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers.

Conclusion

With our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

Purpose

MRI is commonly used to aid breast cancer diagnosis and treatment evaluation. For patients with breast cancer, neoadjuvant chemotherapy aims to reduce the tumor size and extent of surgery necessary. The current clinical standard to measure breast tumor response on MRI uses the longest tumor diameter. Radiologists also account for other tissue properties including tumor contrast or pharmacokinetics in their assessment. Accurate longitudinal image registration of breast tissue is critical to properly compare response to treatment at different timepoints.

Methods

In this study, a deformable Fast Longitudinal Image Registration (FLIRE) algorithm was optimized for breast tissue. FLIRE was then compared to the publicly available software packages with high accuracy (DRAMMS) and fast runtime (Elastix). Patients included in the study received longitudinal T_1-weighted MRI without fat saturation at two to six timepoints as part of asymptomatic screening (n = 27) or throughout neoadjuvant chemotherapy treatment (n = 32). T_1-weighted images were registered to the first timepoint with each algorithm.

Results

Alignment and runtime performance were compared using two-way repeated measure ANOVAs (P < 0.05). Across all patients, Pearson's correlation coefficient across the entire image volume was slightly higher with statistical significance and had less variance for FLIRE (0.98 ± 0.01 stdev) compared to DRAMMS (0.97 ± 0.03 stdev) and Elastix (0.95 ± 0.03 stdev). Additionally, FLIRE runtime (10.0 mins) was 9.0 times faster than DRAMMS (89.6 mins) and 1.5 times faster than Elastix (14.5 mins) on a Linux workstation.

Conclusion

FLIRE demonstrates promise for time-sensitive clinical applications due to its accuracy, robustness across patients and timepoints, and speed.

Objectives

Compressed sensing allows for image reconstruction from sparsely sampled k-space data, which is particularly useful in dynamic contrast enhanced MRI (DCE-MRI). The aim of the study was to assess the diagnostic value of a volume-interpolated 3D T1-weighted spoiled gradient-echo sequence with variable density Cartesian undersampling and compressed sensing (CS) for head and neck MRI.

Methods

Seventy-one patients with clinical indications for head and neck MRI were included in this study. DCE-MRI was performed at 3 Tesla magnet using CS-VIBE (variable density undersampling, temporal resolution 3.4 s, slice thickness 1 mm). Image quality was compared to standard Cartesian VIBE. Three experienced readers independently evaluated image quality and lesion conspicuity on a 5-point Likert scale and determined the DCE-derived time intensity curve (TIC) types.

Results

CS-VIBE demonstrated higher image quality scores compared to standard VIBE with respect to overall image quality (4.3 ± 0.6 vs. 4.2 ± 0.7, p = 0.682), vessel contour (4.6 ± 0.4 vs. 4.4 ± 0.6, p < 0.001), muscle contour (4.4 ± 0.5 vs. 4.5 ± 0.6, p = 0.302), lesion conspicuity (4.5 ± 0.7 vs. 4.3 ± 0.9, p = 0.024) and showed improved fat saturation (4.8 ± 0.3 vs. 3.8 ± 0.4, p < 0.001) and movement artifacts were significantly reduced (4.6 ± 0.6 vs. 3.7 ± 0.7, p < 0.001). Standard VIBE outperformed CS-VIBE in the delineation of pharyngeal mucosa (4.2 ± 0.5 vs. 4.6 ± 0.6, p < 0.001). Lesion size in cases where a focal lesion was identified was similar for all readers for CS-VIBE and standard VIBE (p = 0.101). TIC curve assessment showed good interobserver agreement (k=0.717).

Conclusion

CS-VIBE with variable density Cartesian undersampling allows for DCE-MRI of the head and neck region with diagnostic, high image quality and high temporal resolution.