Pusat Pengajian Sains Kesihatan - Monograf

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    Classification of glioma brain tumors in mr images using imagej-based radiomic analysis
    (2024-07)
    Zafrin, Nurizzatul Hadawiyah Mohamed
    The aim of this study is to classify glioma brain tumor grades using ImageJ-based radiomic analysis. This study utilizes magnetic resonance imaging (MRI) with ImageJ (Fiji) software to perform radiomic analysis, providing a quantitative method to evaluate Glioma tumor without the use of Gadolinium based contrast agents (GBCAs). Gliomas can range from low grade gliomas (LGGs) (Grades I and II) to high grade gliomas (HGGs) (Grades III and IV) tumors. Accurate grading of tumor is vital in determination of the appropriate treatment. This is a retrospective study on 12 T2-weighted (T2W) images (n=12) of patients with pathologically diagnosed glioma of different grades retrieved at the Radiology Department, Hospital Universiti Sains Malaysia from Picture Archiving and Communications System (PACS). A single slice of T2W image is chosen for analysis and all of the images were analysed for its image quality. Then, a workflow and protocol for image processing were explored using ImageJ. The lesion and normal appearing white matter (NAWM) region of interest (ROI) were selected for histogram analysis, along with lesion to normal tissue ratio (LNR) calculations and standard deviation of lesion (SDL) analysis for the assessment of tumor heterogeneity and intensity. Additionally, color thresholding, lookup table (LUT) images with 3D plot surface images, and midline shift angle measurements were used to assess tumor characteristics such as the margin, edema, and mass effect. The result of this study shows no specific trend for the tumor intensity based on LNR findings; however the SDL of the tumor shows an increasing trend across the glioma grades, which proves the increasing heterogeneity as the glioma grades increase. The margin of the tumor can be depicted by entropy thresholding, the edema is depicted by LUT images. Finally, the mass effect is depicted by the measurement of midline shift angle where higher-grade tumor depicts further deviation from 180.0° and in 3D surface plot images. In conclusion, ImageJ-based radiomic analysis provides an accessible and simple method for classifying glioma brain tumors. This approach may potentially facilitate tumor grading without the use of GBCAs.
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    Percentage depth dose (PDD) of 9 MEV electron beam in the medium with the presence of inhomogeneous tissue by using optically stimulated luminescence dosimeter (OSLD), EBT3 film and ionization chamber
    (2024-07)
    Malek, Nur Syazwany
    The human body has variations in tissue density such as high-density bone tissue and low-density lung tissue, which will impact the dose distribution in the medium. The study aims to evaluate the dosimetric accuracy of optically stimulated luminescence dosimeters (OSLD) and their ability to detect and measure dose perturbation by measuring the percentage depth dose (PDD) in the medium consisting of solid water, bone equivalent, and cork(lung) equivalent phantom. Three phantom setups of solid water phantom, solid water-bone phantom, and solid water-cork (lung) phantom were irradiated with a 9 MeV electron beam, and the PDD was measured using OSLD, EBT3 film, and ionization chamber. The PDD curve and electron range parameter obtained by OSLD was compared to other dosimeters and statistical test was conducted to determine the agreement between the PDD using the p-value. The results showed that PDD measured by OSLD was in good agreement with the ionization chamber and EBT3 film dosimetry in a homogenous solid water phantom setup. PDD in the inhomogeneous solid water-bone and solid water cork (lung) phantom setups measured by OSLD was also consistent with the EBT3 film and previous studies. No significant differences were observed between PDD measured by OSLD and the reference dosimetry, evidenced by p-value > 0.05 obtained from statistical tests. The overall results indicated the suitability of OSLD as a passive dosimeter in electron beam dosimetry in the medium with the presence of inhomogeneous tissue.
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    Quantitative study of iterative reconstruction algorithms of spect/ct in bone scan: a clinical and phantom study
    (2024-07)
    Hao, Lau Lik
    The integration of single photon emission computed tomography (SPECT) with computed tomography (CT), along with advancements in iterative image reconstruction algorithms, significantly enhances the feasibility of SPECT quantification in bone scan. Quantitative bone SPECT enables the precise measurement of radiotracer accumulation in bone lesions. This capability allows for the accurate assessments of the presence and extent of bone abnormalities, thereby improving diagnostic accuracy of bone scan. This study evaluates the impact of iterative reconstruction algorithms with various attenuation correction methods on SPECT quantification accuracy and image quality in bone scan across different iteration numbers in both phantom and clinical settings. In the phantom study, spheres in the NEMA 2012/ IEC 2008 phantom were filled with 300 kBq/ml of mixture of K2HPO4 solution and a 99mTc source, while the background region contained only 30 kBq/ml of 99mTc source, establishing a tumor-to-background ratio (TBR) of 10:1. The phantom underwent bone imaging using the standard protocol applied at Hospital Universiti Sains Malysia (HUSM). In the clinical study, a pelvic bone scan image with multiple lesions was retrieved from the XelerisTM workstation. Both phantom and clinical images were reconstructed using MLEM-CHANG, OSEM-CHANG, and OSEM-CT, with varying iteration products (4, 8, 12, 16, and 20 iterations for MLEM; 40, 80, 120, 160, and 200 iterations for OSEM). Quantitative analysis of activity concentration, recovery coefficient (RC), standardized uptake value (SUV), signal-to-noise ratio (SNR), and noise were performed using Dosimetry Toolkit and Q.Metrix software. OSEM-CT (-73.3% to 6.7%) demonstrated the smallest percentage difference between measured and actual activity concentration (300 kBq/ml) across all sphere volumes and iteration numbers compared to MLEM-CHANG (-86.7% to -43.3%) and OSEM-CHANG (-83.3% to -33.3%). For all algorithms, increasing the iteration numbers elevated RC, SUV, and noise, while SNR dropped. In the phantom study, there were no significant difference in RC and SNR among the algorithm pairs (MLEM-CHANG vs OSEM-CHANG, MLEM-CHANG vs OSEM-CT, and OSEM-CHANG vs OSEM-CT) across different iteration numbers (p>0.05), as tested using the Kruskal-Wallis test with post-hoc Bonferroni’s correction. In the clinical study, significant differences in SUV were displayed between MLEM-CHANG vs OSEM-CT and OSEM-CHANG vs OSEM-CT at all iteration numbers (p< 0.05). Additionally, the SNR of the lesions in clinical study showed significant differences between MLEM-CHANG and OSEM-CT at iterations of 8, 12 and 16 (p< 0.05). In summary, OSEM-CT illustrated higher activity concentration accuracy, RC, SUV, and SNR, along with lower noise level compared to OSEM-CHANG and MLEM-CHANG. Thus, OSEM-CT is recommended for accurate SPECT quantification and optimal image quality in bone scan.