Contribution of QSM Imaging to the Diagnosis of the Rare Syndrome of Leukoencephalopathy with Cysts and Calcification (LCC)

Quantitative MRI in leukodystrophies

Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.

Leukoencephalopathy with calcifications and cysts: A case report with literature review

Leukoencephalopathy with calcifications and cysts (LCC; OMIM #614561) is a rare disease and at present there are less than 100 cases reported worldwide. Mutations in the SNORD118 gene is now known to be the cause of LCC. We present a case who was heterozygous for the n.70G>A and n.6C>T sequence variants of the SNORD118 gene, variants which to date have not been described. Compared with the cases that we reviewed, our patient had the second longest time to diagnosis (age 56) from onset of symptoms 40 years prior. Moreover, his cousin's family has a high prevalence of epilepsy. This paper reviewed all published reports to date that had descriptive cases involving LCC as well as testing for the SNORD118 gene. Since 1996 only 85 patients have been described in 59 case reports. In this review, we summarize their clinical features, especially central nervous system symptoms, treatment, pathology, and gene testing results.

The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus

In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images is proposed, and deep learning realizes accurate segmentation of deep nuclei in brain QSM images. Experimental results show data first cuts each layer of 0 100 case data, based on the image center, from 384 × 288 to the size of 128 × 128. Image combination: each layer of the image in the layer direction combines with two adjacent images into a 2.5D image, i.e., (It - m It; It + i), where It represents the layer i image. At this time, the size of the image changes from 128 × 128 to 128 × 128 × 3, in which 3 represents three consecutive layers of images. The SNR of SWP I to STN is twice that of SWI. The small deep gray matter nuclei (RN, SN, and STN) in QSM images of the brain and the pancreas with irregular shape and large individual differences in abdominal CT images can be automatically segmented.

Automated adaptive preconditioner for quantitative susceptibility mapping

PURPOSE

To develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality.

THEORY AND METHODS

The total field was used to rapidly produce an approximate susceptibility map, which was then averaged and trended over R 2 ∗ binning to generate a spatially varying distribution of preconditioning values. This automated adaptive preconditioner was used to reconstruct QSM via total field inversion and was compared with its empirical counterparts in a numerical simulation, a brain experiment with 5 healthy subjects and 5 patients with intracerebral hemorrhage, and a cardiac experiment with 3 healthy subjects.

RESULTS

Among evaluated preconditioners, the automated adaptive preconditioner achieved the fastest convergence in reducing the RMSE of the QSM in the simulation, suppressed hemorrhage-associated artifacts while preserving surrounding brain tissue contrasts, and provided cardiac chamber oxygenation values consistent with those reported in the literature.

CONCLUSION

An automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.