Medical Imaging Technology تكنولوجيا التصوير الطبي
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- ItemEfficacy of automated breast ultrasound as a screening tool for detecting breast lesions in comparison with mammography and breast biopsy(Al-Quds Univeersity, 2024-12-14) Ahlam Mohammad Asad Mubarak; أحلام محمد مباركAutomated breast ultrasound (ABUS) has been developed as an advanced imaging technology designed to overcome the limitations of conventional breast screening modalities, particularly the reduced sensitivity of mammography in dense breast tissue and the operator dependency of handheld ultrasound (HHUS). This study aimed to evaluate the efficacy of ABUS as a screening tool for detecting breast lesions in comparison with mammography and breast biopsy. A descriptive, retrospective cross-sectional study was conducted involving 133 women who underwent both mammography and ABUS at Yazan Radiology Center in Bethlehem between January and December 2023. The mean age of participants was 51.71 years. Breast density distribution showed that 38.3% were classified as category C, 37.6% as category B, 13.5% as category D, and 10.5% as category A. Findings demonstrated that ABUS exhibited higher sensitivity than mammography, particularly in women with dense breasts, and detected a greater number of lesions. The overall accuracy of ABUS was 61.65%, representing a statistically significant difference compared to mammography. No significant associations were observed between ABUS-detected lesions and demographic characteristics except for breast density. In conclusion, ABUS shows substantial potential as an effective screening modality, offering improved lesion detection in dense breast tissue and demonstrating advantages over mammography in sensitivity and diagnostic performance.
- ItemDeep Clustering Approaches for Carotid Artery Calcification Detection in Panoramic Radiographs for Enhancing Cardiovascular Risk Prediction(Al-Quds University, 2026-01-08) Nadeen Khaled Ibrahim Erekat; نادين خالد إبراهيم عريقاتCardiovascular disease remains a leading cause of death worldwide, making early identification of vascular risk markers essential for prevention. Carotid artery calcifications can occasionally be visualized on panoramic dental radiographs, offering an opportunistic indicator of atherosclerotic burden during routine dental care. Yet, manual identification is challenged by inter-reader variability and the presence of anatomical mimics, and many published AI solutions rely on supervised learning that requires large, densely labeled datasets. This thesis investigates an alternative pathway by developing an unsupervised deep clustering framework for carotid region analysis on panoramic radiographs, and by integrating questionnaire-based risk factor modeling to support broader cardiovascular risk stratification. This single-center retrospective cross-sectional observational study included 1,107 panoramic radiographs acquired between February 2025 and August 2025 during routine dental examinations at Abraj Dental Clinics affiliated with Al-Quds University, in collaboration with the Faculty of Dentistry. In addition, a cross-sectional questionnaire sub-study was prospectively administered to a subset of participants (n = 438) to capture cardiovascular risk profiles and support complementary non-imaging analyses. The imaging cohort comprised 48% males and 52% females, with an age range of 18–85 years and a mean age of approximately 40 years. Preprocessing consisted of contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by extraction of bilateral carotid regions of interest (ROIs), resized to 128×128 pixels. To represent each ROI, a dual feature strategy was adopted. Interpretable handcrafted radiographic features were computed to quantify intensity distributions, texture patterns, and edge- and morphology-related cues potentially associated with calcification. In parallel, deep representations were learned using a convolutional autoencoder pretrained for 300 epochs with mean squared error loss and a low-dimensional latent space, producing compact feature vectors suitable for downstream clustering. Four clustering methods were evaluated: K-Means, hierarchical clustering, Gaussian Mixture Models, and Deep Embedded Clustering (DEC). Clustering quality was assessed using internal validation metrics that do not require ground truth labels, including the Silhouette score, Davies–Bouldin index, and Calinski–Harabasz index. To connect unsupervised clustering outcomes to clinical relevance, the thesis adopted a patient-level validation strategy, where each patient contributed left and right ROI assignments. A high-risk validation subset of 21 patients was defined using confirmed cardiovascular disease history or documented carotid calcifications in clinical records. Patient-level accuracy was calculated based on the proportion of high-risk patients grouped into the dominant high-risk cluster. Model interpretability was further supported using GradCAM++ visualization to highlight salient ROI regions consistent with expected calcification-related patterns. DEC achieved the strongest clustering performance across the internal metrics, with a Silhouette score of 0.214, a Davies–Bouldin index of 1.752, and a Calinski–Harabasz index of 524, indicating improved within-cluster cohesion and between-cluster separation relative to baseline methods. Patient-level validation also favored DEC, which achieved 95.2% accuracy in aggregating high-risk patients into a dominant cluster with fewer anomalous cases compared with K-Means, hierarchical clustering, and GMM. To incorporate non-imaging determinants of cardiovascular risk, prospectively administered questionnaire data were analyzed for 438 participants (220 high risk, 218 low risk). Univariate association testing using chi-square statistics with Cramér’s V suggested notable associations with age, physical activity, sleep duration, sedentary time, dietary behaviors, selected health awareness indicators, and mental health measures such as relaxation difficulty and feelings of worthlessness. A Random Forest classifier trained on questionnaire features achieved high predictive performance (accuracy = 0.9318, ROC AUC = 0.9821, F1 = 0.9291), and feature importance analysis highlighted socioeconomic factors and psychological distress-related variables among influential predictors alongside age and lifestyle behaviors. In conclusion, this thesis demonstrates that unsupervised deep clustering of anatomically defined carotid ROIs on panoramic radiographs can yield coherent groupings aligned with clinically defined high-risk status, while offering an interpretable and label-efficient screening support pathway. Combining imaging-based signals with questionnaire-derived risk factors may further strengthen cardiovascular risk stratification and support targeted referral for confirmatory vascular assessment. Future work should incorporate gold-standard vascular confirmation, multi-site external validation, and longitudinal outcome linkage to establish clinical reliability and generalizability.
- ItemCross-Modality Transfer Learning for Reliable Lung Cancer Nodule Classification in Low-Dose CT(Al-Quds University, 2026-01-08) Sara Rasheed Saleh Asfour; سارة عصفورLung cancer is the leading cause of cancer-related mortality worldwide and remains a major health burden in Palestine. According to the Palestine Annual Health Report 2024, lung and bronchus cancers accounted for 316 newly diagnosed cases (10.5 per 100,000 population) and 266 deaths, with 85% occurring among males. These epidemiological patterns highlight the urgent need for locally validated AI solutions that support early lung cancer detection and robust clinical decision-making, particularly in resource-limited healthcare settings where radiological expertise and structured screening programs remain limited. This study proposes a dose-aware hybrid deep-learning–machine-learning framework for multi-class Lung-RADS classification using heterogeneous chest CT datasets collected from multiple Palestinian institutions. The framework integrates optimized contrast enhancement, deep feature extraction using VGG16, and five classical machine-learning classifiers (LR, SVM, RF, GB, and DT). Contrast enhancement was systematically evaluated using two subsets of 120 malignant cases, one LDCT and one SDCT which is demonstrating that the hybrid CLAHE-USM provided the most balanced improvements (EME = 20.648, PSNR = 19.711, SSIM = 0.912). Dose-optimized parameters, including a clip limit of 3 for LDCT and 4 for SDCT, confirmed the importance of dose-specific preprocessing in stabilizing image quality. The classification results obtained across the LDCT, combined, external validation, and clinical validation datasets demonstrate that the proposed VGG16-machine learning framework provides robust and consistent performance for Lung-RADS-based nodule risk stratification. On the LDCT dataset, all classifiers achieved clinically meaningful performance, although variability was observed across Lung-RADS categories. GB and decision tree classifiers demonstrated powerful performance in LR2 and higher-risk categories, achieving accuracies above 0.85. A clear performance improvement was observed with increasing risk of malignancy. For LR4A and LR4B, most classifiers achieved higher accuracy than in lower-risk categories, reflecting the greater structural distinctiveness of high-risk nodules. The most important performance gains were observed on the combined LDCT-SDCT dataset, particularly for LR3 and LR4A-LR4B. In this setting, SVM and RF achieved near-ceiling performance, with accuracies exceeding 0.97 and AUC values approaching unity. External validation on an independent test set from Al-Makassed Hospital further confirmed the robustness of the proposed framework. For LR2, all major classifiers achieved accuracies above 0.84, whereas for LR4B, all major classifiers achieved accuracies above 0.86. The most stringent assessment of diagnostic performance was provided by Clinical validation on biopsy-confirmed LR4B cases from Augusta Victoria Hospital. SVM achieved the highest accuracy and AUC, followed closely by LR. Overall, this framework demonstrates strong potential as a practical decision-support tool for improving Lung-RADS-based risk stratification and supporting early lung cancer detection in resource-constrained healthcare settings
- ItemThe Role of Invasive and Noninvasive Cardiac Imaging Modalities in the Diagnosis and Treatment of Ischemic Heart Disease(Al-Quds University, 2025-01-11) Yazan Abdulaziz Ghazi Abourmeileh; يزن عبد العزيز غازي أبورميلةThis research explains the role of medical imaging modalities in diagnosing a specific disease, which is ischemic heart disease, which is one of the most common leading cause of death all over the world. This research studies the role of invasive and noninvasive cardiac imaging modalities in the diagnosis of ischemic heart disease (IHD), focusing on their effectiveness, accuracy, and clinical using. The study used a quantitative research methodology, employing structured questionnaires distributed to medical cardiologist within Palestinian hospitals. Data were collected also from hospital medical record from 806 patients on their demographics, including age and gender, alongside diagnostic reports detailing imaging modality choices and their outcomes. Statistical tools were applied to analyze the collected data, identifying patterns and correlations between demographic factors and diagnostic effectiveness. The findings show significant variations in diagnostic effectiveness based on the imaging modality used. coronary computed tomography angiography (CCTA) and myocardial perfusion image (MPI) mostly used for diagnosing coronary artery disease in intermediate-to-high risk patients, whereas stress electrocardiogram (ECG) was more commonly used for lower-risk or symptomatic patients due to its accessibility and cost effectiveness. The results show that patient age and gender influenced the choice of diagnostic tools, with younger patients often undergoing methods like ECG, while older patients and those with complex symptoms were more likely to receive advanced imaging modalities. The study concludes that noninvasive imaging modalities are important tools in diagnosing and managing IHD. However, the selection of an appropriate modality should base on patient-specific factors, including clinical presentation, age, gender, the economic context, hospital protocols, and which protocol doctors follow. For Palestinian hospitals, improving access to advanced imaging modalities and training professionals on their application could enhance diagnostic accuracy and treatment outcomes. This research underscores the need for tailored diagnostic pathways and supports the integration of advanced, cost-effective imaging technologies in resource limited healthcare systems. Finally, we found that cardiologist in Palestinian hospitals follow the worldwide criteria for diagnosis ischemic heart disease, especially European society of cardiology and American heart association.
- ItemMeasurement of liver fat concentration using dual-source dual-energy computed tomography(Al-Quds University, 2024-08-01) Mahmoud Maher Mohammad Fakhoury; محمود ماهر محمد فاخوريThe increase in NAFLD prevalence in the Mediterranean region, which is closely associated with diet, obesity, and metabolic syndrome, justifies research about assessing liver fat accumulation and evaluating liver fat fraction using dual-source DECT. This research outlines how DECT can be used as a safe and noninvasive alternative to liver biopsy, the current method for measuring liver fat content. Liver biopsy is not only the gold standard but also an invasive procedure that is not practical for repeated monitoring. Five portions of fresh cow liver samples were obtained, minced, and supplemented with different amounts of melted sheep fat and iodinated contrast medium (OmnipaqueTM 300) to simulate different fat concentrations. Data were acquired using a Siemens SOMATOM Force dual-source dual-energy computed tomography (DECT) scanner with Siemens Healthineers Syngo.via using mixed image analysis and the Liver Virtual Non-Contrast (VNC) application on a VB60 workstation for fat fraction quantification. The results showed a good correlation between the actual fat added to samples and the fat fraction percentages calculated using the DECT software. For example, when we added 5g of fat (2.4% of the total sample's weight), we obtained a 3% fat fraction, and when we inserted 20g of fat (9% of the total sample's weight), we obtained a 10% fat fraction. The study confirmed a reliable quantification of liver fat content with DECT, with high specificity and accuracy. These findings indicate that DECT may serve as an important clinical tool for the early diagnosis and monitoring of NAFLD, facilitating a noninvasive and cost-effective alternative to MRI and liver biopsy. They also indicate the important role of DECT in the evaluation and follow-up of liver health, especially in regions endemic to NAFLD, where there may be an increased liver donation pool. Future studies utilizing larger sample sizes and cross-modality comparisons are needed to improve the trustworthiness and clinical relevance of these findings.