Medical Imaging Systems Research Articles

First-principles calculations to investigate Structural, electronic, and optical properties of MgF2 monolayer in 1T-phase and 2H-phase using hybrid functional

Full-potential linearized augmented plane-wave (FP-LAPW) method, in combination with the HSE06 hybrid functional, has been employed to investigate the structural, electronic, and optical properties of MgF 2 monolayer in 1T- and 2H-phase. The stability is examined via phonon dispersion curves and cohesive energy. In both phases, MgF 2 single layer exhibits insulator nature with energy gaps as large as 7.67(10.02) and 5.88(8.27) eV obtained by the WC(HSE06) functional, respectively. The density of states and valence charge distribution suggest ionic character of this two-dimensional (2D) material. Strain effect analyses indicate that the band gap decreases nearly linearly when switching the strain nature from compressive to tensile. Calculated dielectric function and optical properties including reflectivity, refractive index, and absorption coefficient assert the optical transparency in a wide energy range from infrared to ultraviolet. Results suggest that MgF 2 monolayer may hold prospect to be applied in the spectroscopy and medical imaging systems.

Improved Classification Model using CNN for Detection of Alzheimer’s Disease

Alzheimer’s Disease (AD) is commonly called a neurodegenerative disorder and it is a common form of dementia. There is no permanent cure for this brain disease hence the early diagnosis of such disease using medical imaging system is highly significant. Machine learning models play a vital role in the detection of AD. Since most of the conventional machine learning models find it difficult to detect the essential features to classify the disease, an advanced deep learning framework called Convolutional Neural Network (CNN) is used in this study to detect essential features automatically and classify the disease. The building components of the proposed CNN-based classification method include convolution layer, batch normalization process, ReLU, and Max-pooling operation. The main objective of this CNN-based classification method is to predict whether the patient is suffering from Alzheimer's disease through the analysis of brain MRI. The proposed methodology implemented is identical to a classification-based system that undergoes training, evaluation, and testing process. Finally, the softmax layer is applied for classification, and the Adam optimization technique is applied for reducing the loss, and by applying Adam quicker convergence can be achieved. The proposed improved CNN classification method achieves an accuracy of 97.8%.

Survey-based Evaluation of the Use of Picture Archiving and Communication Systems in an Eye Hospital-Ophthalmologists' Perspective.

Picture archiving and communication system (PACS) is a medical imaging system for sharing, storage, retrieval, and access of medical images stored. Our study aimed to identify ophthalmologists' views on PACS, with the comparison between 3 platforms, namely electronic patient record (ePR), HEYEX (Heidelberg Engineering, Switzerland), and FORUM (Zeiss, US), following their implementation in an eye hospital for common ophthalmic investigations [visual field, optical coherence tomography (OCT) of retinal nerve fiber layer and macula, and fluorescein/indocyanine green angiography (FA/ICG)]. An online survey was distributed among ophthalmologists in a single center. Primary outcome included comparison of PACS with paper-based system. Secondary outcomes included pattern of use and comparison of different PACS platforms. Survey response rate was 28/37 (75.7%). Images were most commonly accessed through ePR (median: 80% of time, interquartile range: 50 to 90%).All systems scored highly in information display items (median scores ≥7.5 out of 10) and in reducing patient identification error in investigation filing and retrieval during consultation compared to paper (score ≥7.0). However, ePR was inferior to paper in "facilitating comparison with previous results" in all investigation types (scores 3.0 to 4.5). ePR scored significantly higher in all system quality items than HEYEX ( P < 0.001) and FORUM ( P < 0.022), except login response time ( P = 0.081). HEYEX scored significantly higher among vitreoretinaluveitis members (VRU) for information quality items for OCT macula and FA/ICG [VRU: 10.0 (8.0 to 10.0), non-VRU: 8.0 (6.75 to 9.25), P = 0.042]. Overall feedback for PACS among ophthalmologists was positive, with limitations of inefficiency in use of information, for example, comparison with previous results. Subspecialty played an important role in evaluating PACS.

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net

We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest object modeling, presence of noise, and signal dropout throughout the measurement. The base line image segmentation approaches are not sufficient for complex target segmentation throughout the various medical image types. To overcome the issues, a novel U-Net-based model proposed that consists of two consecutive networks with five and four encoding and decoding levels respectively. In each of networks, there are four residual blocks between the encoder-decoder path and skip connections that help the networks to tackle the vanishing gradient problem, followed by the multi-scale attention gates to generate richer contextual information. To evaluate our architecture, we investigated three distinct data-sets, (i.e., CVC-ClinicDB dataset, Multi-site MRI dataset, and a collected ultrasound dataset). The proposed algorithm achieved Dice and Jaccard coefficients of 95.79%, 91.62%, respectively for CRL, and 93.84% and 89.08% for fetal foot segmentation. Moreover, the proposed model outperformed the state-of-the-art U-Net based model on the external CVC-ClinicDB, and multi-site MRI datasets with Dice and Jaccard coefficients of 83%, 75.31% for CVC-ClinicDB, and 92.07% and 87.14% for multi-site MRI dataset, respectively.

Stateful-Service-Based Pupil Recognition in Natural Light Environments.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.

Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification

Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we propose a method for generating UAPs using a simple hill-climbing search based only on DNN outputs to demonstrate that UAPs are easily generatable using a relatively small dataset under black-box conditions with representative DNN-based medical image classifications. Black-box UAPs can be used to conduct both nontargeted and targeted attacks. Overall, the black-box UAPs showed high attack success rates (40–90%). The vulnerability of the black-box UAPs was observed in several model architectures. The results indicate that adversaries can also generate UAPs through a simple procedure under the black-box condition to foil or control diagnostic medical imaging systems based on DNNs, and that UAPs are a more serious security threat.

Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement.

Background Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS). Methods Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed. Results Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943–0.997) p=0.032; automated: HR 0.967 (95% CI 0.939–0.995) p=0.022) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938–0.999) p=0.043; automated: HR 0.963 [95% CI 0.933–0.995] p=0.024; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920–0.996) p=0.027; automated: HR 0.954 (95% CI 0.920–0.989) p=0.011). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p=0.21). Absolute values of LV volumes differed significantly between automated and manual approaches (p < 0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient. Conclusion Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR.

CPRO: Competitive Poor and Rich Optimizer-Enabled Deep Learning Model and Holoentropy Weighted-Power K-Means Clustering for Brain Tumor Classification Using MRI

A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.

Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation.

Touchless Interfaces in the Operating Room: A Study in Gesture Preferences

Touchless interfaces allow surgeons to control medical imaging systems autonomously while maintaining total asepsis in the Operating Room. This is specially relevant as it applies to the recent outbreak of COVID-19 disease. The choice of best gestures/commands for such interfaces is a critical step that determines the overall efficiency of surgeon-computer interaction. In this regard, usability metrics such as task completion time, memorability and error rate have a long-standing as potential entities in determining the best gestures. In addition, previous works concerned with this problem utilized qualitative measures to identify the best gestures. In this work, we hypothesize that there is a correlation between gestures’ qualitative properties and their usability metrics. In this regard, we conducted a user experiment with language experts to quantify gestures’ properties (v). Next, we developed a gesture-based system that facilitates surgeons to control the medical imaging software in a touchless manner. Next, a usability study was conducted with neurosurgeons and the standard usability metrics (u) were measured in a systematic manner. Lastly, multi-variate correlation analysis was used to find the relations between u and v. Statistical analysis showed that the v scores were significantly correlated with the usability metrics with an and p < 0.05. Once the correlation is established, we can utilize either gestures’ qualitative properties or usability metrics to identify the best set of gestures.

Lightweight switchable bifunctional metasurface based on VO2: High-efficiency absorption and ultra-wideband circular polarization conversion

Switchable multi-function metasurfaces have received much attention due to their excellent performance of manipulating electromagnetic waves. In this paper, we present a novel design that can realize a high-efficiency absorber and an ultra-wideband circular polarization converter based on an anisotropic metasurface through the phase transition of vanadium dioxide (VO2). At room temperature, the metasurface acts an ultra-wideband circular polarization converter with the VO2 film maintains an insulating state, the theoretical analysis and numerical simulation show that the polarization conversion ratio reaches more than 90% in an ultra-wide band (6.3–15.0 THz) and even reaches almost 100% at two peaks, the relative bandwidth is 82%. When the temperature increases to 68 ℃, the metasurface becomes a high-efficiency absorber with VO2 film in metallic state. Numerical calculations show that the absorber has over 90% high-efficiency absorption of X-linearly polarized wave in the 10.8–13.2 THz frequency range and over 90% high-efficiency absorption of left-handed circular polarization wave in the 10.8–14.4 THz frequency range, both are angle insensitivity for small incident angles from 0° to 20°. The physical mechanisms of the two functions based on our structure are discussed. Our novel design has potential significance in future terahertz medical imaging, non-destructive detectors, polarization converters, and THz communication system.

X-ray imaging detector for radiological applications adapted to the context and requirements of low- and middle-income countries.

This paper describes the development of a novel medical x-ray imaging system adapted to the needs and constraints of low- and middle-income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible light when excited by the x rays, and then, a calibrated multi-camera architecture converts the visible light from the scintillator into a set of digital images. The partial images are then unwarped, enhanced, and stitched through parallel field programmable gate array processing units and specialized software. All the detector components were carefully selected focusing on optimizing the system's image quality, robustness, cost-effectiveness, and capability to work in harsh tropical environments. With this aim, different customized and commercial components were characterized. The resulting detector can generate high quality medical diagnostic images with detective quantum efficiency levels up to 60% (@2.34 μGy), even under harsh environments, i.e., 60 °C and 98% humidity.

Operational Learning-Based Boundary Estimation in Electromagnetic Medical Imaging

Incorporating boundaries of the imaging object as <i>a priori</i> information to imaging algorithms can significantly improve the performance of EM medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject&#x2019;s movement, a learning-based method is proposed to estimate the boundary (external contour) of the imaged object using the same EM imaging data. While imaging techniques may discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned model is verified through independent clinical human trials by using a head imaging system with a 16-element antenna array that works across the band 0.7&#x2013;1.6 GHz. The evaluation demonstrated that the model achieves average dissimilarity of 0.012 in Hu-moment while detecting head boundary. The model enables fast scan and image creation while eliminating the need for additional devices for accurate boundary estimation.

A Review on Procedures on Design and Development of Framework for Content Based Image Retrieval

Abstract: Digital Image Databases and documentation provide lot of research areas. Significant among them is, The Content Based Image Retrieval (CBIR) research area for manipulating large amount of image databases and archives. The development in the field of medical imaging system has lead industries to conceptualize a complete automated system for the medical procedures, diagnosis, treatment and prediction. There is a continuous research in the area of CBIR systems typically for medical images, which provides a successive algorithm development for achieving generalized methodologies, which could be widely used. The achievement of such system mainly depends upon the strength, accuracy and speed of the retrieval systems. Content based image retrieval (CBIR) system is valuable in medical systems as it provides retrieval of the images from the large dataset based on similarities. The aim of this paper is to discuss the various techniques, the assumptions and its scope suggested by various researchers and setup a further roadmap of the research in the field of CBIR system for medical image. Keywords: Content–based image retrieval (CBIR), Digital Images, Medical Imaging

Generalized sidelobe canceler beamforming combined with Eigenspace-Wiener postfilter for medical ultrasound imaging.

BACKGROUND: The beamforming algorithm is key to the image quality of the medical ultrasound system. The generalized sidelobe canceler (GSC) beamforming can improve the image quality in lateral resolution, but the contrast is not improved correspondingly.OBJECTIVE: In our research, we try to optimize the generalized sidelobe canceler to obtain images that achieve an improvement in both lateral resolution and contrast.METHODS: We put forward a new beamforming algorithm which combines the generalized sidelobe canceler and Eigenspace-Wiener postfilter. According to eigenspace decomposition of the covariance matrix of the received data, the components of the Wiener postfilter can be calculated from the signal matrix and the noise matrix. Then, the adaptive weight vector of GSC is further constrained by the Eigenspace-Wiener postfilter, which make the output energy of the receiving array closer to the desired signal than the conventional GSC output.RESULTS: We compare the new beamforming algorithm with delay-and-sum (DS) beamforming, synthetic aperture (SA) beamforming, and GSC beamforming using the simulated and experimental data sets. The quantitative results show that our method reduces the FWHM by 85.5%, 80.5%, and 38.9% while improving the CR by 123.6%, 47.7%, 84.4% on basis of DS, SA, and GSC beamforming, respectively.CONCLUSIONS: The new beamforming algorithm can obviously improve the imaging quality of medical ultrasound imaging systems in both lateral resolution and contrast.

PATIENT AND STAFF DOSES FOR VARIOUS INTERVENTIONAL RADIOLOGY AND CARDIOLOGY EXAMINATIONS IN TURKEY.

This study aims to determine the radiation doses of patients and staff during different interventional radiology and cardiology examinations. Dose measurements for interventional radiology examinations were performed in Ibn-i Sina Hospital of Ankara University using Siemens Artis-Zee medical imaging system. Patient dose measurement was carried out for interventional cardiology examinations in Cardiology Department of TOBB-ETU University, Medical Faculty Hospital using Philips Allura Centron interventional X-ray system. Patient doses were obtained in terms of kerma area product (KAP) and cumulative air kerma (CAK) from KAP meter attached to the angiography system. Performance tests of the angiography system were performed before patient dose measurements. Staff dose measurements were carried out with thermoluminescence dosimeters (TLD-100) placed in certain areas on the staff. Patient dose measurements were performed for 15 different interventional radiology examinations on a total of 431 patients and for four different cardiology examinations on a total of 299 patients. Monte Carlo based PCXMC 2.0 program was used to calculate patient effective doses. Lower extremity arteriography was the most common examination with a mean KAP value of 30Gycm2 and mean effective dose value of 1.2mSv for total number of 194 patients. Mean KAP values calculated for coronary angiography, percutaneous coronary intervention, electrophysiological procedures and radiofrequency cardiac ablation examinations were 62.8, 162.8, 16.7 and 70.6Gycm2, respectively. Radiologist, nurse and technician effective dose normalised to the unit KAP of patient dose were 0.15, 0.11 and 0.14μSvGy-1cm-2. Similarly, cardiologist, nurse and technician effective dose normalised to the unit KAP of patient dose were 0.22, 0.15 and 0.09μSvGy-1cm-2. Measured KAP and CAK values vary depending on the type and complexity of the examination. The measured staff doses during cardiac examinations were higher when compared with that measured for interventional radiology as expected.

Test-retest reproducibility of echocardiography and CMR in the same patients for assessing left atrial function

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia Background Cardiovascular magnetic resonance (CMR) has been shown to give higher left atrial (LA) volumes than trans-thoracic echocardiography (TTE) but the agreement and test-retest reproducibility of LA functional measure have not been directly compared in the same patient cohort. Methods People with type 2 diabetes mellitus (T2D) without cardiovascular disease underwent TTE and CMR on the same day on two separate occasions with a mean period of 11± 4 days. TTE images were analysed using TomTec-ARENA (v2.4, 2D-CPA), where Medis Suite (v3.1, medical imaging system) was used for CMR images. The analysis included LA strain, corresponding to LA reservoir, conduit, and booster pump (contraction function), LA volumes, and LA emptying fraction (LAEF), which was calculated using area length method. Strain and volume were assessed on 4- and 2-chamber long-axis and average values were calculated for both modalities. Results 10 participants with T2D (mean age 65.6 ± 7.3 years, 50% male) were studied. CMR LA volumes were significantly higher and LAEF significantly lower compared to TTE (P &amp;lt; 0.01 for all), whilst reservoir strain on CMR was lower compared to TTE (29.2 ± 6.5 vs 33.8 ± 3.7, p = 0.04). The inter-modality agreement was moderate for LA volumes and contraction strain (ICC ≥0.55) and poor for LAEF, strain at reservoir and conduit. Overall, the test-retest reproducibility of CMR was higher than for TTE, but the overall limits of agreement were not too dissimilar on Bland-Altman analysis (Table &amp; Figure). TTE reproducibility was good for LA volumes and strain at conduit (CoV 18-24%, ICC ∼0.80) and moderate for LAEF, strain at reservoir and contraction. CMR showed excellent test-retest reproducibility for LA volumes, EF and strain at reservoir (CoV 7-20%, ICC ≥0.83). Conclusion The test-retest reproducibility of CMR is slightly better than TTE for LA assessment using volumes and strain. LA volumes have superior reproducibility compared to functional LA parameters. Abstract Table: Test-retest reproducibility Abstract Figure: Bland-Altman plot

A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

Background and objectivesOver the past two decades, medical imaging has been extensively apply to diagnose diseases. Medical experts continue to have difficulties for diagnosing diseases with a single modality owing to a lack of information in this domain. Image fusion may be use to merge images of specific organs with diseases from a variety of medical imaging systems. Anatomical and physiological data may be included in multi-modality image fusion, making diagnosis simpler. It is a difficult challenge to find the best multimodal medical database with fusion quality evaluation for assessing recommended image fusion methods. As a result, this article provides a complete overview of multimodal medical image fusion methodologies, databases, and quality measurements. MethodsIn this article, a compendious review of different medical imaging modalities and evaluation of related multimodal databases along with the statistical results is provided. The medical imaging modalities are organized based on radiation, visible-light imaging, microscopy, and multimodal imaging. ResultsThe medical imaging acquisition is categorized into invasive or non-invasive techniques. The fusion techniques are classified into six main categories: frequency fusion, spatial fusion, decision-level fusion, deep learning, hybrid fusion, and sparse representation fusion. In addition, the associated diseases for each modality and fusion approach presented. The quality assessments fusion metrics are also encapsulated in this article. ConclusionsThis survey provides a baseline guideline to medical experts in this technical domain that may combine preoperative, intraoperative, and postoperative imaging, Multi-sensor fusion for disease detection, etc. The advantages and drawbacks of the current literature are discussed, and future insights are provided accordingly.

An FPGA-Accelerated Parallel Digital Beamforming Core for Medical Ultrasound Sector Imaging.

Plane-wave transmission followed by parallel receive beamforming is popular among high frame rate (HFR) ultrasound (US) imaging methods. However, due to technological limitations, HFR imaging is not widely successful in clinical US. The proposed work aims to design a field-programmable gate array (FPGA)-accelerated parallel beamforming core for medical US sector imaging systems. This architecture supports up to 128 channels and forms 28 beams per plane wave transmission in parallel. A block random access memories (BRAMs)-based, 28-read one-write (28R1W) multi-ported delay line architecture is actualized to realize the delay line. In addition, to optimize the FPGA memory, the required beam focusing delays are stored in an external static random access memory (SRAM) and are loaded into the internal delay line registers by a cycle stealing direct memory access (DMA). The FPGA prototype validation and verification are performed on the custom-designed Xilinx-Kintex-7 XC7C410T FPGA-based US imaging platform. The results showed that for a field of view (FOV) of 90° with 0.5° resolution, 640×480 imaging size, a ft/s of 714 is achieved. The performance of the proposed parallel beamformer architecture is compared with existing development works and concluded that the architecture is superior due to its occupancy of FPGA hardware resources and the processing speed.

Application of Binary Genetic Algorithm for Holographic Vascular Mimicking Phantom Reconstruction

Since medical imaging is one of the essential methods for the diagnosis and treatment of several diseases, the characterization and calibration of medical imaging systems with low-cost equipment is the most crucial issue. In this context, tissue-mimicking phantoms have long been used for this purpose. The advantage of phantoms is that, in addition to the desired size and internal properties, they can be produced in a way that best carries the characteristic properties of tissue models and can be standardized so that they can be used in imaging environments. For this reason, it is important to make low-cost phantom designs produced from materials that are easy to shape and available and to ensure that they can be imaged with high quality. In this study, digital holography and binary genetic algorithm (BGA) were used to reconstruct the images of phantoms that mimic the human vascular system produced at a low cost. The obtained results showed that BGA can be used as an alternative to the reconstruction methods commonly used in digital holography. Since BGA provides an alternative solution to obtain the image with high resolution in the reconstruction process without any image processing algorithm, it enables the diagnosis of diseases related to thin vascular structures in real-time with a reliable and non-contact method.