Locally low-rank denoising in transform domains.

Purpose: To develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and Methods: LLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. Results: For both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. Conclusion: A transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.

Relative contribution of susceptibility weighted imaging, compared to conventional MRI, in the detection of common bile-duct calculi.

PURPOSE We aimed to evaluate the relative contribution of susceptibility weighted imaging (SWI) in the detection of common bile-duct (CBD) stones in comparison to the conventional MRI protocol containing magnetic resonance cholangiopancreatography (MRCP), balanced turbo field echo (BTFE), and T2-weighted spin-echo imaging techniques. METHODS MRI data containing MRCP, BTFE, T2-weighted imaging, and abdominal SWI were independently evaluated by 2 sets of experienced radiologists in 44 patients with confirmed CBD stones. Endoscopic retrograde cholangiopancreatography, and endoscopic ultrasound where available, was used as the reference gold standard. Evaluation was performed for the visualization of CBD stones in each of the MRI techniques. Relative contribution of SWI was classified into one of four categories for each case: (1) no contribution to CBD stone visualization; (2) same as conventional techniques; (3) improved diagnostic confidence; and (4) critical for diagnosis. Stone size was also assessed. RESULTS Inter-rater agreement coefficient for CBD stone visualization was found to be "good" in MRCP (0.77), "very good" in SWI (0.94) and BTFE (0.84), and moderate in T2-weighted imaging (0.54). CBD stones were visualized with SWI in 86.4% and 82%, with MRCP in 70.5% and 70.5% cases, with BTFE in 73% and 61.4% cases, with T2-weighted imaging in 45.5% and 52.3% cases by reviewers 1 and 2, respectively. SWI did not contribute to CBD stone visualization in 2.3% (1/44); was the same as conventional techniques in 31.8% (14/44) cases; improved diagnostic confidence in 34.1%; and was critical for diagnosis in 20.5% cases. CONCLUSION SWI has the potential to serve as a strong adjunct to conventional MRI protocols used for CBD stone evaluation with very small scan-time penalty.

Role of intra-tumoral vasculature imaging features on susceptibility weighted imaging in differentiating primary central nervous system lymphoma from glioblastoma: a multiparametric comparison with pathological validation.

PURPOSE: Primary objective of this study was to retrospectively evaluate the potential of a range of qualitative and quantitative multiparametric features assessed on T2, post-contrast T1, DWI, DCE-MRI, and susceptibility-weighted-imaging (SWI) in differentiating evenly sampled cohort of primary-central-nervous-system-lymphoma (PCNSL) vs glioblastoma (GB) with pathological validation. METHODS: The study included MRI-data of histopathologically confirmed ninety-five GB and PCNSL patients scanned at 3.0 T MRI. A total of six qualitative features (three from T2 and post-contrast T1, three from SWI: thin-linear-uninterrupted-intra-tumoral-vasculature, broken-intra-tumoral-microvasculature, hemorrhage) were analyzed by three independent radiologists. Ten quantitative features from DWI and DCE-MRI were computed using in-house-developed algorithms. For qualitative features, Cohen's Kappa-interrater-variability-analysis was performed. Z-test and independent t-tests were performed to find significant qualitative and quantitative features respectively. Logistic-regression (LR) classifiers were implemented for evaluating performance of individual and various combinations of features in differentiating PCNSL vs GB. Performance evaluation was done via ROC-analysis. Pathological validation was performed to verify disintegration of vessel walls in GB and rim of viable neoplastic lymphoid cells with angiocentric-pattern in PCNSL. RESULTS: Three qualitative SWI features and four quantitative DCE-MRI features (rCBVcorr, Kep, Ve, and necrosis-volume-percentage) were significantly different (p < 0.05) between PCNSL and GB. Best diagnostic performance was observed with LR classifier using SWI features (AUC-0.99). The inclusion of quantitative features with SWI feature did not improve the differentiation accuracy. CONCLUSIONS: The combination of three qualitative SWI features using LR provided the highest accuracy in differentiating PCNSL and GB. Thin-linear-uninterrupted-intra-tumoral-vasculature in PCNSL and broken-intra-tumoral-microvasculature with hemorrhage in GB are the major contributors to the differentiation.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.

PURPOSE: This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation. METHODS: Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features. RESULTS: Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma. CONCLUSION: We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.