ارائه الگوریتمی به منظور بهبود سیگنال فوتواکوستیک شبیه‌سازی شده مغزی
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
1 - استادیار، گروه مهندسی برق، دانشکده برق و کامپیوتر، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.
کلید واژه: تصویربرداری فوتواکوستیک, جبران‌سازی, مشابهت‌یابی فضای برداری, الگوریتم M-MVA.,
چکیده مقاله :
تصویربرداری فوتواکوستیک یک فناوری جدید امیدوار کننده در حوزه تصویربرداری پزشکی می‌باشد که طی سال‌های اخیر برای آشکارسازی غیرتهاجمی ناهنجاری‌های مولکولی، ساختاری و عملکردی در بافت‌های زیستی مورد بررسی قرار گرفته است. یکی از کاربردهای بسیار مهم و پیش‌رونده این تکنولوژی تصویربرداری درون‌جمجمه‌ای از مغز می‌باشد. با این وجود از آنجایی که جمجمه مهم‌ترین مانع پراکنده‌ساز در مقابل انتشار سیگنال فوتواکوستیک مغزی می‌باشد، اثرات اکوستیک ناشی از حضور جمجمه در مسیر انتشار سیگنال به شدت میزان کارایی این روش را در تصویربرداری درون‌جمجمه‌ای از مغز تحت تأثیر قرار می‌دهد. در این مقاله روشی مبتنی بر الگوریتم ژنتیک به منظور استفاده از تئوری مشابهت‌یابی فضای برداری برای جبران‌سازی اثرات ناشی از جمجمه بر روی سیگنال فوتواکوستیک دریافتی از مغز معرفی شده است. داده‌های لازم با استفاده از شبیه‌ساز مبتنی بر روش ray-tracing تولید شده است. در الگوریتم جبران‌سازی اطلاعات دامنه و زمان نقاط اکسترمم سیگنال فوتواکوستیک در حوزه زمان – فرکانس را به عنوان بردارهای ويژگی به کار برده و از دو معیار میانگین شباهت بردارهای ویژگی و حاصل‌جمع وزن‌دار شباهت بردارهای ویژگی برای محاسبه امتیاز شباهت استفاده شده است. شبیه‌سازی‌های عددی مبتنی بر مدل مذکور نشان می‌دهد که به هنگام استفاده از معیار حاصل‌جمع وزن‌دار شباهت بردارهای ویژگی، کاربرد الگوریتم ژنتیک در تخمین مقدار بهینه وزن نسبت داده شده به هر بردار ویژگی دقت نتیجه جبران‌سازی را بالا برده و مقدار خطای انحراف معیار نرمالیزه را نسبت به معیار میانگین شباهت بردارهای ویژگی کاهش می‌دهد. به طور کلی بررسی نتایج قابلیت روش پیشنهادی را در جبران‌سازی مؤثر اثرات جمجمه بر روی سیگنال فوتواکوستیک دریافتی از مغز نشان می‌دهد.
Abstract
Photoacoustic imaging has been investigated for non-invasive detection of molecular, structural, and functional abnormalities in biological tissues. One of the important applications of this technology is brain imaging. However, skull’s acoustic effects reduce the effectiveness of this method. This paper presents an algorithm based on vector space similarity model to compensate for the skull’s effects. Numerical simulations show that using the genetic algorithm to estimate the optimal weight assigned to each feature vector improves compensation accuracy and reduces the normalized standard deviation error.
Introduction: Photoacoustic imaging works based on acoustic detection of optical absorption from tissue chromophores. Since the skull is the main dispersive barrier for the propagation of acoustic waves, it distorts the amplitude and phase of the received signal. This paper introduces a novel approach based on vector space similarity model for compensating the effects of the skull on the received brain photoacoustic signal.
Method: The necessary data is generated through a ray-tracing-based simulator. The extremum information of the signal in the time-frequency domain is used as feature vectors and their similarities are computed using a weighted sum, in which the optimal weight for each feature vector is determined through the genetic algorithm.
Results: The results indicate that the skull causes significant distortions to both the amplitude and phase of the received signal. Moreover, the results from the compensation algorithm indicate that utilizing the weighted sum of feature vector similarities improves the accuracy of compensation compared to the mean feature vector similarity criterion.
Discussion: The paper presents an effective method for compensating the skull induced distortions on brain photoacoustic signals. By employing vector space similarity model and optimizing feature vector weights, the proposed approach enhances compensation and reduces the error in the received signal, which can improve the accuracy of photoacoustic brain imaging.
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