Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

Abstract

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Deep learning in MRI‐guided radiation therapy: A systematic review

“…The most common evaluation metric resistant to unbalanced data is the area under the curve (AUC) of a receiver operating characteristics (ROC) curve 133–135 . In a ROC curve, the x‐axis represents the false positive (FP) rate, while the y‐axis relates the true positive (TP) rate.…”

“…The most common evaluation metric resistant to unbalanced data is the area under the curve (AUC) of a receiver operating characteristics (ROC) curve. 133 , 134 , 135 In a ROC curve, the x‐axis represents the false positive (FP) rate, while the y‐axis relates the true positive (TP) rate. In addition, the ROC curve can be viewed as a visual representation to help find the best trade‐off between sensitivity and specificity for the clinical application by comparing one minus the specificity versus the sensitivity of the model.…”

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

“…As for the efforts towards a better standardization of pulse sequence parameters across institutions, a remarkable advancement may be represented by synthetic MRI (SyMRI), using a technique named quantification of relaxation times and proton density by multi-echo acquisition of a saturation-recovery using turbo spin-echo readout (QRAPMASTER) ( 150 ). This technique estimates T 2 , T 1 , and PD values voxel-wise by fitting the Bloch equations to a QRAPMASTER SyMRI sequence: T 2 values are computed from the multiple echoes acquired, T 1 values from the saturation pulses acquired with different delays, and PD values by extrapolating the signal intensity at TE zero ( 151 ). This approach not only allows to obtain quantitative voxel-wise maps of T 1 , T 2 , and PD, but also to generate T 1 -, T 2 -weighted and T 2 -weighted FLAIR images with arbitrary TE, TR, and TI.…”

“…This would potentially enable to more easily generate images with uniform pulse sequence parameters from multi-centric acquisitions. SyMRI is already available on some commercial scanners (e.g., “SyntAc” on Siemens, “MAGiC” on GE), and some studies advocate for the accuracy of SyMRI in parameter quantification ( 152 ) and for its potential usefulness in gliomas ( 151 ). An alternative approach to improve the comparability of images across institutions and manufacturers may be represented by standardization methods applied during post-processing ( 153 ), possibly with the aid of AI ( 154 ).…”

The value of synthetic MRI in detecting the brain changes and hearing impairment of children with sensorineural hearing loss

“…Studies by Vagberg et al ( 2013 ) have validated SyMRI volumetric analysis as a reliable method for determining brain parenchymal fraction (BPF) in MS, showing that BPF is notably lower in pediatric MS cases, primarily due to GM loss (Yeh et al, 2009 ). These quantitative values are invaluable in evaluating brain tumors, aiding in differentiation between glioblastomas and metastases (Badve et al, 2017 ), as well as revealing the internal structure of tumors and lesions in MS (Granberg et al, 2016 ; Chen et al, 2021 ; Nunez-Gonzalez et al, 2022 ). While research on brain relaxation time in SNHL, particularly in children within the first year, is lacking, the potential for SyMRI in exploring this area remains untapped.…”