Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading.

PURPOSE: To evaluate the efficacy of optimized T1-Perfusion MRI protocol (protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE (CSENSE) to differentiate high-grade-glioma (HGG) and low-grade-glioma (LGG) and to compare it with the conventional protocol (protocol-1) with partial brain coverage used in our center. METHODS: This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted (W) fast-field-echo (FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation (COV), Relative-Percentage-Error (RPE), Normalized-Mean-Squared-Error (NMSE) and qualitative scoring were used for the former study. Tracer-kinetic (Ktrans,ve,vp) and hemodynamic (rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG. RESULTS: The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artefacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964). CONCLUSION: CSENSE (R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas.

Evaluation of Gall Bladder Stones Using Susceptibility Weighted Imaging.

OBJECTIVE: To evaluate the visualization of gallbladder stones on susceptibility-weighted imaging (SWI). MATERIALS AND METHODS: Imaging data from 47 patients who underwent clinically indicated cholecystectomy was reviewed. Breath-hold SWI was added to the magnetic resonance imaging protocol and magnitude and phase data was reviewed for gall-stones visualization. Phase signature, that is, diamagnetic, paramagnetic, or mixed, was also noted in the stones. Magnetic susceptibility value of surgically extracted gallstones were imaged ex vivo (n = 37). RESULTS: In 45 of 47 cases, gallstones were surgically confirmed. In 43 cases, gallstones were visualized in the SWI. In 1 case, although routine imaging failed, stones were visualized on SWI. In 29 diamagnetic, 7 paramagnetic and 9 cases mixed phase were seen. In an ex vivo study, magnetic susceptibility of stones was found ranging between -0.102 and -0.916 ppm for diamagnetic and 0.203 and 486 ppm for paramagnetic stones. CONCLUSIONS: Gallbladder stones can be visualized with SWI and may be added to the routine magnetic resonance imaging protocol for its evaluation.

Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components.

BACKGROUND: Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed. PURPOSE: To develop an artificial intelligence-based methodology for glioma grading using T1 perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients. STUDY TYPE: Retrospective. POPULATION: The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients. FIELD STRENGTH/SEQUENCE: Conventional MRI images (2D T1 -W, dual PD-T2 -W, and 3D FLAIR) and 3D T1 perfusion MRI data obtained at 3 T. ASSESSMENT: Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T1 perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T1 perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading. STATISTICAL TESTS: Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis. RESULTS: Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90th percentile value of T1 perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier. DATA CONCLUSION: It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T1 perfusion MRI and volumes of tumor components. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306.