Beyond the brain: Extra-axial pathology on diffusion weighted imaging in neuroimaging

Beyond the surface: how ex-vivo diffusion-weighted imaging reveals large animal brain microstructure and connectivity

Diffusion-weighted Imaging (DWI) is an effective and state-of-the-art neuroimaging method that non-invasively reveals the microstructure and connectivity of tissues. Recently, novel applications of the DWI technique in studying large brains through ex-vivo imaging enabled researchers to gain insights into the complex neural architecture in different species such as those of Perissodactyla (e.g., horses and rhinos), Artiodactyla (e.g., bovids, swines, and cetaceans), and Carnivora (e.g., felids, canids, and pinnipeds). Classical in-vivo tract-tracing methods are usually considered unsuitable for ethical and practical reasons, in large animals or protected species. Ex-vivo DWI-based tractography offers the chance to examine the microstructure and connectivity of formalin-fixed tissues with scan times and precision that is not feasible in-vivo. This paper explores DWI's application to ex-vivo brains of large animals, highlighting the unique insights it offers into the structure of sometimes phylogenetically different neural networks, the connectivity of white matter tracts, and comparative evolutionary adaptations. Here, we also summarize the challenges, concerns, and perspectives of ex-vivo DWI that will shape the future of the field in large brains.

Differentiation of hemangioblastoma from brain metastasis using MR amide proton transfer imaging

BACKGROUND AND PURPOSE

Differentiation between hemangioblastoma and brain metastasis remains a challenge in neuroradiology using conventional MRI. Amide proton transfer (APT) imaging can provide unique molecular information. This study aimed to evaluate the usefulness of APT imaging in differentiating hemangioblastomas from brain metastases and compare APT imaging with diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging.

METHODS

This retrospective study included 11 patients with hemangioblastoma and 20 patients with brain metastases. Region-of-interest analyses were employed to obtain the mean, minimum, and maximum values of APT signal intensity, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV), and these indices were compared between hemangioblastomas and brain metastases using the unpaired t-test and Mann-Whitney U test. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC). AUCs were compared using DeLong's method.

RESULTS

All MRI-derived indices were significantly higher in hemangioblastoma than in brain metastasis. ROC analysis revealed the best performance with APT-related indices (AUC = 1.000), although pairwise comparisons showed no significant difference between the mean ADC and mean rCBV.

CONCLUSIONS

APT imaging is a useful and robust imaging tool for differentiating hemangioblastoma from metastasis.

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms (P < 0.05). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.

Utility of multi-parametric quantitative magnetic resonance imaging of the lacrimal gland for diagnosing and staging Graves' ophthalmopathy

PURPOSE

To explore radiological changes of the lacrimal gland (LG) in Graves' ophthalmopathy (GO) based on multi-parametric quantitative MRI and its clinical utility in LG diagnosis and activity in GO.

METHODS

We enrolled 99 consecutive patients with GO (198 eyes) and 12 Graves' Disease (GD) patients (24 eyes) from July 2018 to June 2020. Clinical, laboratory, and MRI data were collected at the first visit. Based on clinical activity scores, eyes with GO were subdivided into active and inactive groups. T2-relaxation time (T2) and the absolute reduction in T1-relaxation time (ΔT1) were determined. After MRI and processing, we performed descriptive data analysis and group comparisons. Novel logistic regression predictive models were developed for diagnosing and staging GO. Diagnostic performance of MRI parameters and models was assessed by receiver operating characteristic curve analysis.

RESULTS

LG in GO group had significantly higher T2 and ΔT1 values than the GD group [106.25(95.30,120.21) vs. 83.35(78.15,91.45), P<0.001, and 662.62(539.33,810.95) vs. 547.35(458.62,585.57), P = 0.002, respectively]. The GO group had higher T2 of LG indicating higher disease activity [110.93(102.54,127.67) vs. 93.29(87.06,101.96), P < 0.001]. Combining T2 and ΔT1 values of LG, Model I had higher diagnostic value for distinguishing GO from GD (AUC=0.94, 95 %CI: 0.89,0.99, P<0.001). Meanwhile, T2 of LG had higher diagnostic value for grading GO activity (AUC = 0.84, 95 %CI: 0.76,0.92, P<0.001).

CONCLUSIONS

Multi-parametric quantitative MRI parameters of the LG in GO were significantly altered. Novel models combining LG T2 and ΔT1 values showed excellent predictive performances in diagnosing GO. Furthermore, T2 of LG showed practical utility for staging GO.

Neuroimaging in Sickle Cell Disease: A Review

Sickle cell disease is the most common hereditary hemoglobinopathy, which results in abnormally shaped and rigid red blood cells. These sickle-shaped red blood cells cause vaso-occlusion and ischemic phenomena that can affect any organ in the body. As a common cause of disability, the neurological manifestations of sickle cell disease are particularly important. Neuroimaging has a crucial role in the diagnosis, management, and prevention of the complications of sickle cell disease. These complications can affect the brain parenchyma, vasculature, and skull and can be ascribed directly or indirectly to a vasculopathy of small and large vessels. Vaso-occlusion can cause ischemic stroke. Ischemic damage in the absence of an acute neurological deficit, and therefore only apparent on neuroimaging, is termed silent cerebral ischemia. Weakening of the arterial walls can cause aneurysms. In its most severe form, a vasculopathy of the terminal internal carotid arteries can progress to moyamoya syndrome, characterized by steno-occlusive disease and the formation of friable collateral arteries. Rupture of aneurysms or friable collateral arteries is a potential cause of intracranial hemorrhage. The skull and vertebrae may be affected by extra-medullary hematopoiesis, due to severe anemia, or iron deposition, due to chronic red blood cell transfusion. Impaired blood supply to bone is associated with osteomyelitis and osteonecrosis. Fat embolization syndrome is a rare complication of osteonecrosis, which may cause devastating neurological impairment. Awareness and early recognition of the diverse manifestations of sickle cell disease on neuroimaging is crucial to ensure optimal treatment in a complex patient cohort.