Pattern Clustering of Symmetric Regional Cerebral Edema on Brain MRI in Patients with Hepatic Encephalopathy

Purpose

Metabolic abnormalities in hepatic encephalopathy (HE) cause brain edema or demyelinating disease, resulting in symmetric regional cerebral edema (SRCE) on MRI. This study aimed to investigate the usefulness of the clustering analysis of SRCE in predicting the development of brain failure.

Materials and Methods

MR findings and clinical data of 98 consecutive patients with HE were retrospectively analyzed. The correlation between the 12 regions of SRCE was calculated using the phi (Φ) coefficient, and the pattern was classified using hierarchical clustering using the φ2 distance measure and Ward's method. The classified patterns of SRCE were correlated with clinical parameters such as the model for end-stage liver disease (MELD) score and HE grade.

Results

Significant associations were found between 22 pairs of regions of interest, including the red nucleus and corpus callosum (Φ = 0.81, p < 0.001), crus cerebri and red nucleus (Φ = 0.72, p < 0.001), and red nucleus and dentate nucleus (Φ = 0.66, p < 0.001). After hierarchical clustering, 24 cases were classified into Group I, 35 into Group II, and 39 into Group III. Group III had a higher MELD score (p = 0.04) and HE grade (p = 0.002) than Group I.

Conclusion

Our study demonstrates that the SRCE patterns can be useful in predicting hepatic preservation and the occurrence of cerebral failure in HE.

Diagnostic Performance Using a Combination of MRI Findings for Evaluating Cognitive Decline

Purpose

We investigated potentially promising imaging findings and their combinations in the evaluation of cognitive decline.

Materials and Methods

This retrospective study included 138 patients with subjective cognitive impairments, who underwent brain MRI. We classified the same group of patients into Alzheimer's disease (AD) and non-AD groups, based on the neuropsychiatric evaluation. We analyzed imaging findings, including white matter hyperintensity (WMH) and cerebral microbleeds (CMBs), using the Kruskal-Wallis test for group comparison, and receiver operating characteristic (ROC) curve analysis for assessing the diagnostic performance of imaging findings.

Results

CMBs in the lobar or deep locations demonstrated higher prevalence in the patients with AD compared to those in the non-AD group. The presence of lobar CMBs combined with periventricular WMH (area under the ROC curve [AUC] = 0.702 [95% confidence interval: 0.599-0.806], p < 0.001) showed the highest performance in differentiation of AD from non-AD group.

Conclusion

Combinations of imaging findings can serve as useful additive diagnostic tools in the assessment of cognitive decline.

Cross modality generative learning framework for anatomical transitive Magnetic Resonance Imaging (MRI) from Electrical Impedance Tomography (EIT) image

This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation. The initial EIT image was generated at three different frequencies (70 kHz, 140 kHz, and 200 kHz) using a 16-electrode belt. Wrist T1-weighted images were acquired on a 1.5T MRI. A total of 19 normal volunteers were imaged using both EIT and MRI, which resulted in 713 paired EIT and MRI images. The cascaded CycleGAN, end-to-end CycleGAN, and Pix2Pix models were trained and tested on the same cohort. The proposed method achieved the highest accuracy in bone detection, with 0.97 for the proposed cascaded CycleGAN, 0.68 for end-to-end CycleGAN, and 0.70 for the Pix2Pix model. Visual inspection showed that the proposed method reduced bone-related errors in the MRI-style anatomical reference compared with end-to-end CycleGAN and Pix2Pix. Multifrequency EIT inputs reduced the testing normalized root mean squared error of MRI-style anatomical reference from 67.9% ± 12.7% to 61.4% ± 8.8% compared with that of single-frequency EIT. The mean conductivity values of fat and bone from regularized EIT were 0.0435 ± 0.0379 S/m and 0.0183 ± 0.0154 S/m, respectively, when the anatomical prior was employed. These results demonstrate that the proposed framework is able to generate MRI-style anatomical references from EIT images with a good degree of accuracy.

Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI)

Hippocampus is a part of the limbic system in human brain that plays an important role in forming memories and dealing with intellectual abilities. In most of the neurological disorders related to dementia, such as, Alzheimer's disease, hippocampus is one of the earliest affected regions. Because there are no effective dementia drugs, an ambient assisted living approach may help to prevent or slow the progression of dementia. By segmenting and analyzing the size/shape of hippocampus, it may be possible to classify the early dementia stages. Because of complex structure, traditional image segmentation techniques can't segment hippocampus accurately. Machine learning (ML) is a well known tool in medical image processing that can predict and deliver the outcomes accurately by learning from it's previous results. Convolutional Neural Networks (CNN) is one of the most popular ML algorithms. In this work, a U-Net Convolutional Network based approach is used for hippocampus segmentation from 2D brain images. It is observed that, the original U-Net architecture can segment hippocampus with an average performance rate of 93.6%, which outperforms all other discussed state-of-arts. By using a filter size of [Formula: see text], the original U-Net architecture performs a sequence of convolutional processes. We tweaked the architecture further to extract more relevant features by replacing all [Formula: see text] kernels with three alternative kernels of sizes [Formula: see text], [Formula: see text], and [Formula: see text]. It is observed that, the modified architecture achieved an average performance rate of 96.5%, which outperforms the original U-Net model convincingly.

Diagnostic magnetic resonance imaging in synovial chondromatosis of the temporomandibular joint

The aim of this paper was to investigate the clinical and magnetic resonance imaging (MRI) features of synovial chondromatosis (SC) of the temporomandibular joint (TMJ). Fourteen patients with SC of the TMJ were included in the study. Clinical and MRI features were analysed and divided into three types based on MRI classification: type I with loose bodies, type II with homogeneous masses, and type III with a mixture of loose bodies and homogeneous masses. All SCs occurred in the superior compartment of the TMJ. There were two patients (14%) categorised as type I, five (36%) as type II and seven (50%) as type III. Four patients (29%) had disc perforation, and nine had bone erosion; among those nine, seven (78%) had type III and two (22%) type II. Histological examination showed inflammation and calcification in the synovial membrane and, and cartilage of the hyaline type in all cases. MRI has advantages in the diagnosis of SC.