After Kanda’s first report in 2014 on gadolinium (Gd) deposition in brain tissue, a considerable number of studies have investigated the explanation for the observation. Gd deposition in brain tissue after repeated administration of gadolinium-based contrast medium (GBCM) has been histologically proven, and chelate stability has been shown to affect the deposition. However, the mechanism for this deposition has not been fully elucidated. Recently, a hypothesis was introduced that involves the ‘glymphatic system’, which is a coined word that combines ‘gl’ for glia cell and ‘lymphatic’ system. According to this hypothesis, the perivascular space functions as a conduit for cerebrospinal fluid to flow into the brain parenchyma. The perivascular space around the arteries allows cerebrospinal fluid to enter the interstitial space of the brain tissue through water channels controlled by aquaporin 4. The cerebrospinal fluid entering the interstitial space clears waste proteins from the tissue. It then flows into the perivascular space around the vein and is discharged outside the brain. In addition to the hypothesis regarding the glymphatic system, some reports have described that after GBCM administration, some of the GBCM distributes through systemic blood circulation and remains in other compartments including the cerebrospinal fluid. It is thought that the GBCM distributed into the cerebrospinal fluid cavity via the glymphatic system may remain in brain tissue for a longer duration compared to the GBCM in systemic circulation. Glymphatic system may of course act as a clearance system for GBCM from brain tissue. Based on these findings, the mechanism for Gd deposition in the brain will be discussed in this review. The authors speculate that the glymphatic system may be the major contributory factor to the deposition and clearance of gadolinium in brain tissue.

Purpose: To elucidate differences between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) with MR image-based texture features.

Methods: This was an Institutional Review Board (IRB)-approved retrospective study. Consecutive, pathologically proven, initially treated 44 patients with GBM and 16 patients with PCNSL were enrolled. We calculated a total of 67 image texture features on the largest contrast-enhancing lesion in each patient on post-contrast T₁-weighted images. Texture analyses included first-order features (histogram) and second-order features calculated with gray level co-occurrence matrix, gray level run length matrix (GLRLM), gray level size zone matrix, and multiple gray level size zone matrix. All texture features were measured by two neuroradiologists independently and the intraclass correlation coefficients were calculated. Reproducible features with the intraclass correlation coefficients of greater than 0.7 were used for hierarchical clustering between the cases and the features along with unpaired t statistics-based comparisons under the control of false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed to find the predominant features in evaluating the differences between GBM and PCNSL.

Results: Twenty-one out of the 67 features satisfied the acceptable intraclass correlation coefficient and the FDR constraints. PCA suggested first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage as the distinguished features. Compared with PCNSL, run percentage and median were significantly lower, and entropy and run length non-uniformity were significantly higher in GBM.

Conclusions: Among MR image-based textures, first-order entropy, median, GLRLM-based run length non-uniformity, and run percentage are considered to enhance differences between GBM and PCNSL.

Magnetic resonance fingerprinting (MRF) is a promising framework that allows the quantification of multiple magnetic resonance parameters with a single scan. MRF using fast imaging with steady-state precession (MRF-FISP) has robustness to off-resonance artifacts and has many applications in inhomogeneous fields. However, the spoiler gradient used in MRF-FISP is sensitive to diffusion motion, and may lead to quantification errors when the spoiler moment increases. In this study, we examined the effect of the diffusion weighting in MRF-FISP caused by spoiler gradients. The T₂ relaxation times were greatly underestimated when large spoiler moments were used. The T₂ underestimation was prominent for tissues with large values of T₂ and diffusion coefficients. The T₂ bias was almost independent of the apparent diffusion coefficient (ADC) and T₂ values when the ADC map was measured and incorporated into the matching process. These results reveal that the T₂ underestimation resulted from the diffusion weighting caused by the spoiler gradients.

Purpose: Mural nodules and papillary projections can be seen in benign ovarian endometriosis (OE) and malignant transformation of OE (endometriosis-associated ovarian cancer [EAOC]), which can pose a challenging diagnostic dilemma to clinicians. We identify the preoperative imaging characteristics helpful to the differential diagnosis between benign OE with mural nodules and EAOC.

Materials and Methods: This was a retrospective study of 82 patients who were diagnosed pathologically to have OE with mural nodules (n = 42) and malignant transformations of these tumors (n = 40) at the Nara Medical University Hospital from January 2008 to January 2015. All patients were assessed with contrast-enhanced MRI before surgery. Patient demographics, and clinical and pathologic features were analyzed to detect the significant differences between the two groups.

Results: Histological examinations of resected OE tissue specimens revealed that a majority (78.6%) of the mural nodular lesions were retracted blood clots. We found that the patients with malignant mural nodules, when compared to those with benign nodules, were older, had larger cyst diameters and larger mural nodule sizes, and were more likely to exhibit a taller than wider lesion. They were also more likely to present with various signal intensities on T₁-weighted images (T₁WI), high-signal intensity on T₂-weighted images (T₂WI), a lower proportion of shading on T₂WI, and were more likely to show an anterior location of the cyst. In the multivariate logistic regression analysis, “Height” (>1.5 cm) and “Height-Width ratio (HWR)” (>0.9) of mural nodules, maximum diameter of the cyst (>7.9 cm), and age at diagnosis (>43 years) were independent predictors to distinguish EAOC from OE with mural nodules.

Conclusion: The “Height” and “HWR” of the mural nodules in the cyst may yield a novel potential diagnostic factor for differentiating EAOC from benign OE with mural nodules.