Photoacoustic viscoelasticity assessment of prefrontal cortex and cerebellum in normal and prenatal valproic acid-exposed rats

Mechanical properties of brain tissues are from principal features from different points of view; diagnosis, the performance of the brain and neurological disorders. Particularly viscoelastic properties of the brain tissues are determinative. In this study based on a proposed accurate and non-invasive method, we have measured the viscoelastic properties of prefrontal cortex and cerebellum, two important brain regions involved in motor learning and pathophysiology of autism spectrum disorder (ASD). In this regard, using photoacoustic systems, viscoelastic properties of tissues from the cerebellum and prefrontal cortex of normal and prenatal VPA (Valproic acid)-exposed (i.e. autistic-like) offspring rats are measured. Results of our study show that the cerebellums of normal tissues are stiffer than the tissue obtained from autistic-like rats, while the viscoelasticity of the prefrontal cortex of normal tissues is higher than that of autistic ones. The proposed method for the measurement of viscoelastic properties of the brain tissue has the potential not only for the fundamental studies but as a diagnosis technique.

Brain-Mimicking Phantom for Photoablation and Visualization

While the use of tissue-mimicking (TM) phantoms has been ubiquitous in surgical robotics, the translation of technology from laboratory experiments to equivalent intraoperative tissue conditions has been a challenge. The increasing use of lasers for surgical tumor resection has introduced the need to develop a modular, low-cost, functionally relevant TM phantom to model the complex laser-tissue interaction. In this paper, a TM phantom with mechanically and thermally similar properties as human brain tissue suited for photoablation studies and subsequent visualization is developed. The proposed study demonstrates the tuned phantom response to laser ablation for fixed laser power, time, and angle. Additionally, the ablated crater profile is visualized using optical coherence tomography (OCT), enabling high-resolution surface profile generation.

Brain tissue development of neonates with Congenital Septal Defect: Study on MRI Image Evaluation of Deep Learning Algorithm

Objectives:

This article is based on deep learning algorithms and uses MRI to study the development of congenital heart septal defects in neonatal brain tissue.

Methods:

From January 2018 to December 2019, 150 cases of congenital cardiac paper septal defect were retrospectively analyzed on 50 cases of normal newborns and neonates. The four index parametersbrain MR imaging, lateral ventricle pre-angle measurement index (F/F’), body index (D/ D’), caudal nucleus index (C/C’) were analyzed. The independent sample t test is performed to compare the difference parameters between groups.

Results:

F congenital heart disease group and control group/F ‘values were 0.301 ± 0.035 and 0.296 ± 0.031; Evans index was 0.239 ± 0.052 and 0.233 ± 0.025; 2 sets of D/D’ values were 0.261 ± 0.039 and 0.234 ± 0.032; C/C ‘value was 0.138 ± 0.018 and 0.124 ± 0.015 respectively. The congenital heart disease group D/D ‘, and the value of C/C’ obtained under the ROC curve area value, respectively 0.698 and 0.750, Youden index corresponding to the maximum D/D ‘, and the value of C/C’ values were 0.28 and 0.12.

Conclusion:

Lateral ventricle D/D ‘and C/C’ is more sensitive indicator which can be evaluated with the difference between the volume of congenital heart septal defects in newborn normal neonatal brain; when the D/D ‘value> 0.28, C/C’ value> 0.12. For the diagnosis and evaluation of congenital heart septal defect neonatal brain volume abnormalities have a certain reference value.