The mammillothalamic tracts: Age-related conspicuity and normative morphometry on brain magnetic resonance imaging

Do the mammillary bodies atrophy with aging? A magnetic resonance imaging study

PURPOSE

This retrospective study aimed to explore age-related atrophy of the mammillary bodies (MBs) based on their temporal change using magnetic resonance imaging (MRI).

MATERIALS AND METHODS

The study included 30 adult outpatients who presented to the hospital and were followed for more than 100 months with annual MRIs. The bi-ventricular width (BVW), third ventricle width (TVW), and bi-mammillary dimension (BMD) were measured on axial T2-weighted imaging and analyzed.

RESULTS

The 30 patients comprised 1 in their 40s, 5 in their 50s, 6 in their 60s, 11 in their 70s, 5 in their 80s, and 2 in their 90s. The MBs were consistently detected with left-to-right symmetry. The mean BVW was 32 ± 2.2 mm on the initial (BVW1) and 32 ± 2.4 mm on the last (BVW2) MRI. The mean TVW was 7.0 ± 2.3 mm on the initial (TVW1) and 7.6 ± 2.7 mm on the last (TVW2) MRI. Furthermore, the mean BMD was 9.9 ± 1.3 mm on the initial (BMD1) and 10 ± 1.3 mm on the last (BMD2) MRI. Statistically, no age ranges had a large dimension for BVW1, BVW2, TVW1, TVW2, BMD1, or BMD2. Changes between TVW1 and TVW2 were significantly different in the patients in their 80s; changes between BMD1 and BMD2 were not different for any age range or between sexes.

CONCLUSIONS

Aging alone does not seem to promote MB atrophy. In healthy brains, the MBs may be stationary structures throughout life.

Cerebral blood flow regulates iron overload in the cerebral nuclei of hemodialysis patients with anemia

Hemodialysis patients exhibit anemia-related cerebral hyperperfusion and iron deposition (ID). However, the mechanisms underlying the pathology of cerebral ID are not clear. We investigated the role of cerebral blood flow (CBF) in the pathophysiology of cerebral ID in hemodialysis patients with anemia. This study recruited 33 hemodialysis patients with anemia and thirty-three healthy controls (HCs). All the subjects underwent quantitative susceptibility mapping (QSM) and arterial spin labeling (ASL) to measure ID and CBF in the cerebral nuclei. Furthermore, we evaluated lacunar infarction (LI), cerebral microbleeds, and total white matter hyperintensity volume (TWMHV). Hemodialysis patients with anemia showed significantly higher ID and CBF in some nuclei compared to the HCs after adjusting for age, sex, and total intracranial volume (TIV) [P < 0.05, false discovery rate (FDR) corrected]. CBF showed a positive correlation with ID in both patients and HCs after adjustments for age, gender, and TIV (P < 0.05, FDR corrected). Serum phosphorus, calcium, TWMHV, hypertension, and dialysis duration were independently associated with ID (P < 0.05). Hemoglobin, serum phosphorus, and LI were independently associated with CBF (P < 0.05). Mediation analysis demonstrated that CBF mediated the effects between hemoglobin and ID. Our study demonstrated that CBF mediated aberrant cerebral ID in hemodialysis patients with anemia.

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images

OBJECTIVES

The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI.

METHODS

This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality.

RESULTS

Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001).

CONCLUSION

The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only.

KEY POINTS

• Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

[MR morphometry in epileptology: progress and perspectives]

Morphometric MRI analysis improves neuroimaging of structural changes in epilepsy.

OBJECTIVE

To investigate diagnostic potential of MR brain morphometry in neurosurgical epileptology.

MATERIAL AND METHODS

An interdisciplinary working group reviewed the studies devoted to MR morphometry in epileptology as a part of state assignment No. 056-00119-22-00. Study subject was trials of MR-morphometry in epilepsy. Searching for literature data was conducted in international and national databases between 2017 and 2022 using certain keywords. Final analysis included 36 publications.

RESULTS

Currently, MR brain morphometry allows measurement of cortical volume and thickness, surface area and depth of furrows, as well as analysis of cortical tortuosity and fractal changes. In neurosurgical epileptology, MR-morphometry has the greatest diagnostic value in MR-negative epilepsy. This method simplifies preoperative diagnosis and reduces costs.

CONCLUSION

Morphometry in neurosurgical epileptology is an additional method for verifying the epileptogenic zone. Automated programs simplify application of this method.

MRI-Visible Anatomy of the Basal Ganglia and Thalamus

Conventional MR imaging does not discriminate basal ganglia and thalamic internal anatomy well. Radiology reports describe anatomic locations but not specific functional structures. Functional neurosurgery uses indirect targeting based on commissural coordinates or atlases that do not fully account for individual variability. We describe innovative MR imaging sequences that improve the visualization of normal anatomy in this complex brain region and may increase our understanding of basal ganglia and thalamic function. Better visualization also may improve treatments for movement disorders and other emerging functional neurosurgery targets. We aim to provide an accessible review of the most clinically-relevant neuroanatomy within the thalamus and basal ganglia.