Biomedical data analysis from bidirectional optical-acoustic interface

Integration between optical and acoustic technologies offers significant advantages for stimulation and interrogation of living tissues. For instance, the hybrid opto-acoustic (OA) imaging methods, which combine optical excitation with acoustic signal detection, have become a powerful tool in biomedicine owing to its deep penetration depth, rich functional and molecular contrast, and high temporal and spatial resolution. On the other hand, optical imaging represents a highly versatile and sensitive approach for monitoring tissue responses to ultrasound stimulation. However, such combinations are often faced with multiple compatibility issues and practical limitations, which require development of unconventional data processing and image analysis methods. This thesis deals with two topics related to biomedical data analysis from hybrid optical-acoustic interface. The first topic is focused on monitoring of ultrasound neuromodulation, which is regarded as an emerging technique with high potential in brain therapy and neuroscience research whose main promise is the non-invasive targeting of deep brain structures with high spatial and temporal accuracy. For this, we established a comprehensive data analysis framework facilitating robust and reliable characterization of neural activation in the mouse cortex using fluorescence calcium imaging accomplished by a hybrid fluorescence-ultrasound setup. The developed algorithms are shown to enable the efficient characterization of distributed neuronal activity patterns in the murine brain following focused ultrasound stimulations. The developed framework can thus deepen our understanding on the mechanisms of ultrasound neuromodulation at the macroscopic level and facilitate the exploration of large parameter spaces. The second topic deals with data analysis methods for small animal and clinical OA imaging systems that commonly suffer from insufficient tomographic coverage, resulting in artifactual reconstructions hindering reliable image interpretation. Here, deep-learning based processing pipeline has been developed for efficient enhancement of OA image quality from sparse and limited-view data. The developed methods can benefit numerous OA imaging applications by mitigating common image artefacts, enhancing anatomical image contrast, accelerating data acquisition and image reconstruction, thus also facilitating the clinical translation of OA imaging technology. Overall, it is anticipated that the established methodology will facilitate the use of other hybrid optical-acoustic setups as practical standard tools in medicine and biology

Deep learning optoacoustic tomography with sparse data

The rapidly evolving field of optoacoustic (photoacoustic) imaging and tomography is driven by a constant need for better imaging performance in terms of resolution, speed, sensitivity, depth and contrast. In practice, data acquisition strategies commonly involve sub-optimal sampling of the tomographic data, resulting in inevitable performance trade-offs and diminished image quality. We propose a new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network and demonstrate its performance with whole body mouse imaging in vivo. To generate accurate high-resolution reference images for optimal training, a full-view tomographic scanner capable of attaining superior cross-sectional image quality from living mice was devised. When provided with images reconstructed from substantially undersampled data or limited-view scans, the trained network was capable of enhancing the visibility of arbitrarily oriented structures and restoring the expected image quality. Notably, the network also eliminated some reconstruction artefacts present in reference images rendered from densely sampled data. No comparable gains were achieved when the training was performed with synthetic or phantom data, underlining the importance of training with high-quality in vivo images acquired by full-view scanners. The new method can benefit numerous optoacoustic imaging applications by mitigating common image artefacts, enhancing anatomical contrast and image quantification capacities, accelerating data acquisition and image reconstruction approaches, while also facilitating the development of practical and affordable imaging systems. The suggested approach operates solely on image-domain data and thus can be seamlessly applied to artefactual images reconstructed with other modalities

Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.ISSN:2076-341

Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners

Deep learning of image- and time-domain data enhances the visibility of structures in optoacoustic tomography

Images rendered with common optoacoustic system implementations are often afflicted with distortions and poor visibility of structures, hindering reliable image interpretation and quantification of bio-chrome distribution. Among the practical limitations contributing to artifactual reconstructions are insufficient tomographic detection coverage and suboptimal illumination geometry, as well as inability to accurately account for acoustic reflections and speed of sound heterogeneities in the imaged tissues. Here we developed a convolutional neural network (CNN) approach for enhancement of optoacoustic image quality which combines training on both time-resolved signals and tomographic reconstructions. Reference human finger data for training the CNN were recorded using a full-ring array system that provides optimal tomographic coverage around the imaged object. The reconstructions were further refined with a dedicated algorithm that minimizes acoustic reflection artifacts induced by acoustically mismatch structures, such as bones. The combined methodology is shown to outperform other learning-based methods solely operating on image-domain data

Report of Intracerebral Hemorrhage Following Myocardial Infarction

Background and Objectives: Stroke is a rare complication of myocardial infarction (AMI). Aspirin, plavix, and enoxaparin are among drug treatments for myocardial infarction, which lead to stroke. The present study is a case report of stroke after myocardial infarction, which discusses patient’s records and clinical history along with paraclinical findings. Case Report: The patient was a 60-year-old man with a history of heart disease and diabetes, presented with severe chest pain and dyspnea to the Emergency Department of Yasuj Sajad Hospital on January 29, 2015, and after taking ECG, it was found that there was no signs of myocardial infarction, but troponin test was positive two times. The diagnosis was myocardial infarction without ST segment elevation. The patient took aspirin and plavix, and after subcutaneous injection of enoxaparin at the dose of 80 mg, his level of consciousness decreased, which caused GCS:5, right-side mydriasis, and motor paralysis in the left half of the body, therefore, CT was performed, and the patient that had about 90 ml hemorrhage in temporoparietal lobe. The patient was transformed to the operating room and 60 ml blood was removed using partial lobectomy and a microscope. After hospitalization in ICU for several days, the patient was extubated under the SIMV mode. Considering the high prevalence of heart disease, especially increasing rate of myocardial infarction in the country, anticoagulants should be more carefully used and after administration of this group of drugs, patients be regularly monitored for side effects

Association of usf1s2 variant in the upstream stimulatory factor 1 gene with premature coronary artery disease in southern population of Iran

Background: Polymorphisms of the upstream transcription factor 1 (USF1) have been associated with familial combined hyperlipidemia (FCHL), type 2 diabetes and coronary heart diseases (CHD). In the current investigation, the association of USF1s2 variant of human USF1 gene with premature coronary artery disease (PCAD) was evaluated in a population from southern Iran. USF1s2 has the best potential as a functional variant .in the USF1 gene. Methods: In a case-control study USF1s2 variant of human USF1 gene was determined by polymerase chain reaction- restriction fragment length polymorphism (PCR-RFLP) technique using BsiHKA I restriction enzyme for 186 women under 55 years of age and 135 men less than 50 years of age who underwent diagnostic coronary angiography in Saadi, Nemazee and Kowsar Hospitals of Shiraz, between July 2009 and March 2012. Data on the history of familial myocardial infarction or other heart diseases, hypertension, and smoking habit were collected by a simple questionnaire. Blood sugar level and serum lipid profile of all participants were also obtained by measuring the levels of fasting blood sugar (FBS), total cholesterol (TC), triglycerides (TG), low density lipoprotein (LDL) and high-density lipoprotein cholesterol (HDL). Results: Frequencies of the major (G) and minor (A) alleles of usf1s2 gene variant were 0.74 and 0.26 in the whole population, respectively. Meanwhile, the prevalence of the minor allele was significantly higher in PCAD patients compared with control subjects. This difference remained significant even after adjustment for confounding parameters. Indeed, subjects with mutant homozygous genotype (AA) were about 5 times more likely to suffer from early-onset CAD than those with wild-type homozygous genotype (GG). Moreover, the baseline characteristics of the control subjects and patients were statistically similar for almost all parameters except for the number of male individuals; there was no significant difference among various genotypes in the patient group for any of these investigated variables. Conclusion: It appears that the usf1s2 variant in upstream transcription factor 1 gene is an independent predictor of premature coronary artery disease in our population and applies its effects without affecting blood sugar and lipid levels