Optimising the quantitative analysis in functional pet brain imaging

Patlak analysis techniques based on linear regression are often applied to positron emission tomography (PET) images to estimate a number of physiological parameters. The Patlak equation forms the basis for most extension works regarding graphical analysis of many tracers in quantitative PET measurements. Patlak analysis is primarily used to obtain the rate constant Ki, which represents the tracer transfer rate from plasma to the targeted tissue. One of the most common issues associated with Patlak analysis is the introduction of statistical noise, adopted originally from the images, that affects the slope of the graphical plot, leading to bias, and causes errors in the calculation of the rate constant Ki i. In this thesis, several statistical and noise reduction methods for 2 and 3 dimensional data are proposed and applied to simulated 18F-FDOPA brain images generated from a PET imaging simulator. The methods were applied to investigate whether their utilisation could reduce the bias and error caused by noisy images and improve the accuracy of quantitative measurements. Then, validation step extended to 18F-FDOPA PET images obtained from a clinical trial for Parkinson’s disease. The minimum averaged SE, SSE and the highest averaged reduction of noisy Ki values were found with the feasible generalised least squares (FGLS) model. Battle-Lemarie wavelet (BLW) showed significant change in data for the 3D PET images. Savitzky-Golay filtering (SGF) demonstrated significant change for most of the noise levels applied to 2D data. In clinical 18F-FDOPA images, the mean and standard deviation of standard error (SE) and sum-squared error (SSE) were significantly reduced in both baseline and after therapy groups. This work has the potential to be extended to other graphical analysis in quantitative PET data measurements

Optimising graphical techniques applied to irreversible tracers

Graphical analysis techniques are often applied to positron emission tomography (PET) images to estimate physiological parameters. Patlak analysis is primarily used to obtain the rate constant (Ki) that indicates the transfer of a tracer from plasma to the irreversible compartment and ultimately describes how the tracer binds to the targeted tissue. One of the most common issues associated with Patlak analysis is the introduction of statistical noise that affects the slope of the graphical plot and causes bias. In this study, several statistical methods are proposed and applied to PET time activity curves (TACs) for both reversible and irreversible regions that are involved in the equation. A dynamic PET imaging simulator for the Patlak model was used to evaluate the statistical methods employed to reduce the bias introduced in the acquired data

Evaluations of Paranasal Sinus Disease Using Multidetector Computed Tomography in Taif City, Saudi Arabia

Background: This study aimed to evaluate paranasal sinusitis disease and determine if there is a relationship between the anatomical variation of sinusitis based on the age and gender of the patient and, if so, to identify the most affected demographic group. Methods and Results: This study included 130 patients (76 men and 54 women with ages ranging from 18 years to 75 years) diagnosed with PNS disease and was conducted in the Radiology Department of King Abdelaziz Specialist Hospital and King Faisal Hospital (Taif city, Saudi Arabia) from January 1 2021 to January 31 2022. The evaluation of sinusitis was conducted using multidetector computed tomography. The clinical symptoms included 70% cases of nasal obstruction, 53% cases of headache, 28.5% cases of nasal discharge, 17.7% cases of facial pain, and 3.1% cases of general malaise. The types of sinusitis included maxillary sinusitis (88.5%), sphenoid sinusitis (28.5%), ethmoid sinusitis (43.8%), and frontal sinusitis (23.5%). The study found no significant anatomical variation of sinuses based on age and gender (P>0.05). Conclusion: An evaluation of paranasal sinusitis disease using an MDCT scan shows that there are no gender or age-related differences in the prevalence of the disease. Moreover, the study demonstrates that there is no significant anatomical variation of sinuses based on age and gender

[Corrigendum] Evaluations of Paranasal Sinus Disease Using Multidetector Computed Tomography in Taif City, Saudi Arabia

Corrigendum for 'Evaluations of Paranasal Sinus Disease Using Multidetector Computed Tomography in Taif City, Saudi Arabia' by: Alotaibi O, Osman H, Hadi Y, Alzamil Y, Alyahyawi A, Al-Enezi MS, Alafer F, Abanomy A, Khandaker MU, Almeshari M. International Journal of Biomedicine 12(4): 575-579. DOI: 10.21103/Article12(4)_OA9 Following the publication of this article, the authors have realized that errors were made with the description of the listed affiliation addresses. Therefore, the author affiliations and addresses, in this paper should have appeared as follows: Osama Alotaibi1,2, Hamid Osman3, Yasser Hadi 4, Yasser Alzamil5, Amjad Alyahyawi5,6, Mamdouh S. Al-Enezi5, Feras Alafer7, Ahmad Abanomy8, Mayeen Uddin Khandaker9,10, and Meshari Almeshari5; 1Department of Radiology, College of Applied Medical Sciences, University of Ha'il, Ha'il, Saudi Arabia 2Department of Radiology, King Abdulaziz Specialist Hospital-Taif, Taif, Saudi Arabia 3Department of Radiological Sciences, College of Applied Medical Science, Taif University, P.O. Box 2425, Taif 21944, Saudi Arabia 4Department of Medical Imaging and Intervention, King Abdullah Medical City (KAMC), Makkah, Saudi Arabia 5Department of Diagnostic Radiology, College of Applied Medical Sciences, University of Ha'il, Ha'il, Saudi Arabia. 6Centre for Nuclear and Radiation Physics, Department of Physics, University of Surrey, Guildford, Surrey GU2 7XH, UK 7Department of Radiological Sciences, College of Applied Medical Sciences, Jouf University, Sakaka, Saudi Arabia 8Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box 10219, Riyadh 11433, Saudi Arabia 9Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Bandar Sunway, Malaysia 10Department of General Educational Development, Faculty of Science and Information Technology, Daffodil, International University, DIU Rd, Dhaka 1341, Bangladesh The authors apologize for any inconvenience caused

Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification

One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%

Efficient SCAN and Chaotic Map Encryption System for Securing E-Healthcare Images

The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed by worn-out radiologists. Transmission of these medical data increases in frequency due to patient overflow, and protecting confidentiality, along with integrity and availability, emerges as one of the most crucial components of security. Medical images generally contain sensitive information about patients and are therefore vulnerable to various security threats during transmission over public networks. These images must be protected before being transmitted over this network to the public. In this paper, an efficient SCAN and chaotic-map-based image encryption model is proposed. This paper describes pixel value and pixel position manipulation based on SCAN and chaotic theory. The SCAN method involves translating an image’s pixel value to a different pixel value and rearranging pixels in a predetermined order. A chaotic map is used to shift the positions of the pixels within the block. Decryption follows the reverse process of encryption. The effectiveness of the suggested strategy is evaluated by computing the histogram chi-square test, MSE, PSNR, NPCR, UACI, SSIM, and UQI. The efficiency of the suggested strategy is demonstrated by comparison analysis. The results of analysis and testing show that the proposed program can achieve the concept of partial encryption. In addition, simulation experiments demonstrate that our approach has both a faster encryption speed and higher security when compared to existing techniques

Pre-contrast CT calcium score correlation with significant risk factors for coronary artery disease

Computed Tomography (CT) scan is one of the most widely used methods for evaluating the pre-contrast CT calcium scoring. This study aims to assess the correlation of pre-contrast CT calcium scoring with major risk factors for coronary artery disease (CAD). A descriptive study was conducted from Oct 2021 to Jan 2022 and included 109 patients. The mean age of the patients was 54.5 ± 14.1 (range 40–79) years old. The spreadsheet was used to gather the data via Picture Archive and Communication System (PACS) from the radiology department of King Salman Specialist Hospital, Hail (KSSHH). The study showed the percentage of male and female patients were 41.0% and 59.0%, respectively. More than zero calcium score among males was found to be 67.0%, whereas among females, it was at least 50.0%. The male group of 60–79 years old was found to be the most effected age group with more than zero calcium, while the female age group of 70–79 years showed the same zero calcium score. The percentage of calcium prevalence (score >0) in coronary arteries is affected by a number of risk factors. Coronary artery calcium (CAC) has emerged as a consistent means for assessing the risk of primary cardiovascular outcomes, especially for asymptomatic people. This study showed that the patient group with more than one risk factor for coronary heart disease had no evidence of calcium scoring. Study recommends to enlarge sample size in the coming future studies and to perform calcium scoring using CT scan before computed tomography angiofraphy (CTA)

Awareness level, knowledge and attitude towards breast cancer among staff and students of Hail University, Saudi Arabia

Introduction Awareness of screening procedures and illness warning signals is critical for expanding and implementing screening programs in society, which would improve the odds of early identification of breast cancer. Objectives This study aimed to evaluate the knowledge, awareness, attitudes, and practices related to breast cancer risk factors, signs, symptoms and methods of screening among female faculty and students at Hail University in the Kingdom of Saudi Arabia. Methods A cross-sectional study was conducted from January 2021 through February 2021 in the Hail region of Saudi Arabia. A closed-ended questionnaire, which consisted of 37 questions, was distributed online (using a Google Forms link) in both English and Arabic languages. Data was collected from 425 female subjects who participated in the study. Results The study showed an overall knowledge level of 46.36% regarding breast cancer. Participants had average knowledge about risk factors, signs, and symptoms, whereas their awareness and practice of breast self-examination and screening methods were weak. Conclusion The current study concluded that public awareness of breast cancer remains relatively low, and Saudi Arabia still needs several public awareness initiatives using mass media, such as television, the Internet, and radio, as well as social media. Special awareness programs should also be held in places where a large number of women can easily be reached, such as colleges, universities, and hospitals