Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Sex dependence of opioid-mediated responses to subanesthetic ketamine in rats

Subanesthetic ketamine is increasingly used for the treatment of varied psychiatric conditions, both on- and off-label. While it is commonly classified as an N-methyl D-aspartate receptor (NMDAR) antagonist, our picture of ketamine's mechanistic underpinnings is incomplete. Recent clinical evidence has indicated, controversially, that a component of the efficacy of subanesthetic ketamine may be opioid dependent. Using pharmacological functional ultrasound imaging in rats, we found that blocking opioid receptors suppressed neurophysiologic changes evoked by ketamine, but not by a more selective NMDAR antagonist, in limbic regions implicated in the pathophysiology of depression and in reward processing. Importantly, this opioid-dependent response was strongly sex-dependent, as it was not evident in female subjects and was fully reversed by surgical removal of the male gonads. We observed similar sex-dependent effects of opioid blockade affecting ketamine-evoked postsynaptic density and behavioral sensitization, as well as in opioid blockade-induced changes in opioid receptor density. Together, these results underscore the potential for ketamine to induce its affective responses via opioid signaling, and indicate that this opioid dependence may be strongly influenced by subject sex. These factors should be more directly assessed in future clinical trials.

Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data

This study presents the Fourier Decay Perception Generative Adversarial Network (FDP-GAN), an innovative approach dedicated to alleviating limitations in photoacoustic imaging stemming from restricted sensor availability and biological tissue heterogeneity. By integrating diverse photoacoustic data, FDP-GAN notably enhances image fidelity and reduces artifacts, particularly in scenarios of low sampling. Its demonstrated effectiveness highlights its potential for substantial contributions to clinical applications, marking a significant stride in addressing pertinent challenges within the realm of photoacoustic acquisition techniques.

Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals

BACKGROUND AND AIM

Ultrasound (US) imaging is increasingly preferred over other more invasive modalities in preclinical studies using animal models. However, this technique has some limitations, mainly related to operator dependence. To overcome some of the current drawbacks, sophisticated data processing models are proposed, in particular artificial intelligence models based on deep learning (DL) networks. This systematic review aims to overview the application of DL algorithms in assisting US analysis of images acquired in in vivo preclinical studies on animal models.

METHODS

A literature search was conducted using the Scopus and PubMed databases. Studies published from January 2012 to November 2022 that developed DL models on US images acquired in preclinical/animal experimental scenarios were eligible for inclusion. This review was conducted according to PRISMA guidelines.

RESULTS

Fifty-six studies were enrolled and classified into five groups based on the anatomical district in which the DL models were used. Sixteen studies focused on the cardiovascular system and fourteen on the abdominal organs. Five studies applied DL networks to images of the musculoskeletal system and eight investigations involved the brain. Thirteen papers, grouped under a miscellaneous category, proposed heterogeneous applications adopting DL systems. Our analysis also highlighted that murine models were the most common animals used in in vivo studies applying DL to US imaging.

CONCLUSION

DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. However, in this scenario, these techniques are still in their early stages, and there is room for improvement, such as sample sizes, data preprocessing, and model interpretability.

PerceptFlow: Real-Time Ultrafast Doppler Image Enhancement Using Deep Convolutional Neural Network and Perceptual Loss

Ultrafast ultrasound is an emerging imaging modality derived from standard medical ultrasound. It allows for a high spatial resolution of 100 μm and a temporal resolution in the millisecond range with techniques such as ultrafast Doppler imaging. Ultrafast Doppler imaging has become a priceless tool for neuroscience, especially for visualizing functional vascular structures and navigating the brain in real time. Yet, the quality of a Doppler image strongly depends on experimental conditions and is easily subject to artifacts and deterioration, especially with transcranial imaging, which often comes at the cost of higher noise and lower sensitivity to small blood vessels. A common solution to better visualize brain vasculature is either accumulating more information, integrating the image over several seconds or using standard filter-based enhancement techniques, which often over-smooth the image, thus failing both to preserve sharp details and to improve our perception of the vasculature. In this study we propose combining the standard Doppler accumulation process with a real-time enhancement strategy, based on deep-learning techniques, using perceptual loss (PerceptFlow). With our perceptual approach, we bypass the need for long integration times to enhance Doppler images. We applied and evaluated our proposed method on transcranial Doppler images of mouse brains, outperforming state-of-the-art filters. We found that, in comparison to standard filters such as the Gaussian filter (GF) and block-matching and 3-D filtering (BM3D), PerceptFlow was capable of reducing background noise with a significant increase in contrast and contrast-to-noise ratio, as well as better preserving details without compromising spatial resolution.

Mu Opioid Receptor Activation Mediates (S)-ketamine Reinforcement in Rats: Implications for Abuse Liability

BACKGROUND

(S)-ketamine is an NMDA receptor antagonist, but it also binds to and activates mu opioid receptors (MORs) and kappa opioid receptors in vitro. However, the extent to which these receptors contribute to (S)-ketamine's in vivo pharmacology is unknown.

METHODS

We investigated the extent to which (S)-ketamine interacts with opioid receptors in rats by combining in vitro and in vivo pharmacological approaches, in vivo molecular and functional imaging, and behavioral procedures relevant to human abuse liability.

RESULTS

We found that the preferential opioid receptor antagonist naltrexone decreased (S)-ketamine self-administration and (S)-ketamine-induced activation of the nucleus accumbens, a key brain reward region. A single reinforcing dose of (S)-ketamine occupied brain MORs in vivo, and repeated doses decreased MOR density and activity and decreased heroin reinforcement without producing changes in NMDA receptor or kappa opioid receptor density.

CONCLUSIONS

These results suggest that (S)-ketamine's abuse liability in humans is mediated in part by brain MORs.

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Simple Summary

Segmentation of brain tumor images from magnetic resonance imaging (MRI) is a challenging topic in medical image analysis. The brain tumor can take many shapes, and MRI images vary considerably in intensity, making lesion detection difficult for radiologists. This paper proposes a three-step approach to solving this problem: (1) pre-processing, based on morphological operations, is applied to remove the skull bone from the image; (2) the particle swarm optimization (PSO) algorithm, with a two-way fixed-effects analysis of variance (ANOVA)-based fitness function, is used to find the optimal block containing the brain lesion; (3) the K-means clustering algorithm is adopted, to classify the detected block as tumor or non-tumor. An extensive experimental analysis, including visual and statistical evaluations, was conducted, using two MRI databases: a private database provided by the Kouba imaging center—Algiers (KICA)—and the multimodal brain tumor segmentation challenge (BraTS) 2015 database. The results show that the proposed methodology achieved impressive performance, compared to several competing approaches.

Abstract

Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.