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Fulltext Association of Radiation Doses and Cancer Risks from CT Pulmonary Angiography Examinations in Relation to Body Diameter

Harun HH, Abdul Karim MK, Abbas Z, Abdul Rahman MA, Sabarudin A, Ng KH

Diagnostics (Basel), 2020 Sep 09;10(9).
PMID: 32917029 DOI: 10.3390/diagnostics10090681

In this study, we aimed to estimate the probability of cancer risk induced by CT pulmonary angiography (CTPA) examinations concerning effective body diameter. One hundred patients who underwent CTPA examinations were recruited as subjects from a single institution in Kuala Lumpur. Subjects were categorized based on their effective diameter size, where 19-25, 25-28, and >28 cm categorized as Groups 1, 2, and 3, respectively. The mean value of the body diameter of the subjects was 26.82 ± 3.12 cm, with no significant differences found between male and female subjects. The risk of cancer in breast, lung, and liver organs was 0.009%, 0.007%, and 0.005% respectively. The volume-weighted CT dose index (CTDIvol) was underestimated, whereas the size-specific dose estimates (SSDEs) provided a more accurate description of the radiation dose and the risk of cancer. CTPA examinations are considered safe but it is essential to implement a protocol optimized following the As Low as Reasonably Achievable (ALARA) principle.
Fulltext Establishment of CTPA Local Diagnostic Reference Levels with Noise Magnitude as a Quality Indicator in a Tertiary Care Hospital

Harun HH, Abdul Karim MK, Abd Rahman MA, Abdul Razak HR, Che Isa IN, Harun F

Diagnostics (Basel), 2020 Sep 09;10(9).
PMID: 32916913 DOI: 10.3390/diagnostics10090680

This study aimed to establish the local diagnostic reference levels (LDRLs) of computed tomography pulmonary angiography (CTPA) examinations based on body size with regard to noise magnitude as a quality indicator. The records of 127 patients (55 males and 72 females) who had undergone CTPAs using a 128-slice CT scanner were retrieved. The dose information, scanning acquisition parameters, and patient demographics were recorded in standardized forms. The body size of patients was categorized into three groups based on their anteroposterior body length: P1 (14-19 cm), P2 (19-24 cm), and P3 (24-31 cm), and the radiation dose exposure was statistically compared. The image noise was determined quantitatively by measuring the standard deviation of the region of interest (ROI) at five different arteries-the ascending and descending aorta, pulmonary trunk, and the left and right main pulmonary arteries. We observed that the LDRL values were significantly different between body sizes (p < 0.05), and the median values of the CT dose index volume (CTDIvol) for P1, P2, and P3 were 6.13, 8.3, and 21.40 mGy, respectively. It was noted that the noise reference values were 23.78, 24.26, and 23.97 HU for P1, P2, and P3, respectively, which were not significantly different from each other (p > 0.05). The CTDIvol of 9 mGy and dose length product (DLP) of 329 mGy∙cm in this study were lower than those reported by other studies conducted elsewhere. This study successfully established the LDRLs of a local healthcare institution with the inclusion of the noise magnitude, which is comparable with other established references.
Fulltext Association of Physicochemical Characteristics, Aggregate Indices, Major Ions, and Trace Elements in Developing Groundwater Quality Index (GWQI) in Agricultural Area

Harun HH, Kasim MRM, Nurhidayu S, Ash'aari ZH, Kusin FM, Karim MKA

Int J Environ Res Public Health, 2021 04 25;18(9).
PMID: 33923119 DOI: 10.3390/ijerph18094562

The aim of this study was to propose a groundwater quality index (GWQI) that presents water quality data as a single number and represents the water quality level. The development of the GWQI in agricultural areas is vital as the groundwater considered as an alternative water source for domestic purposes. The insufficiency of the groundwater quality standard in Malaysia revealed the importance of the GWQI development in determining the quality of groundwater. Groundwater samples were collected from thirteen groundwater wells in the Northern Kuala Langat and the Southern Kuala Langat regions from February 2018 to January 2019. Thirty-four parameters that embodied physicochemical characteristics, aggregate indicator, major ions, and trace elements were considered in the development of the GWQI. Multivariate analysis has been used to finalize the important parameters by using principal component analysis (PCA). Notably, seven parameters-electrical conductivity, chemical oxygen demand (COD), magnesium, calcium, potassium, sodium, and chloride were chosen to evaluate the quality of groundwater. The GWQI was then verified by comparing the groundwater quality in Kota Bharu, Kelantan. A sensitivity analysis was performed on this index to verify its reliability. The sensitivity GWQI has been analyzed and showed high sensitivity to any changes of the pollutant parameters. The development of GWQI should be beneficial to the public, practitioners, and industries. From another angle, this index can help to detect any form of pollution which ultimately could be minimized by controlling the sources of pollutants.
Effect of iterative reconstruction algorithm levels on noise index and figure-of-merit in CT pulmonary angiography examinations

Harun HH, Karim MKA, Abbas Z, Sabarudin A, Muniandy SC, Ibahim MJ

J Xray Sci Technol, 2020;28(5):893-903.
PMID: 32741801 DOI: 10.3233/XST-200699

PURPOSE: To evaluate the influence of iterative reconstruction (IR) levels on Computed Tomography (CT) image quality and to establish Figure of Merit (FOM) value for CT Pulmonary Angiography (CTPA) examinations.
METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol) and SNR2 divided by the size-specific dose estimates (SSDE).
RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p
Fulltext Forest fire surveillance systems: A review of deep learning methods

Saleh A, Zulkifley MA, Harun HH, Gaudreault F, Davison I, Spraggon M

Heliyon, 2024 Jan 15;10(1):e23127.
PMID: 38163175 DOI: 10.1016/j.heliyon.2023.e23127

This review aims to critically examine the existing state-of-the-art forest fire detection systems that are based on deep learning methods. In general, forest fire incidences bring significant negative impact to the economy, environment, and society. One of the crucial mitigation actions that needs to be readied is an effective forest fire detection system that are able to automatically notify the relevant parties on the incidence of forest fire as early as possible. This review paper has examined in details 37 research articles that have implemented deep learning (DL) model for forest fire detection, which were published between January 2018 and 2023. In this paper, in depth analysis has been performed to identify the quantity and type of data that includes images and video datasets, as well as data augmentation methods and the deep model architecture. This paper is structured into five subsections, each of which focuses on a specific application of deep learning (DL) in the context of forest fire detection. These subsections include 1) classification, 2) detection, 3) detection and classification, 4) segmentation, and 5) segmentation and classification. To compare the model's performance, the methods were evaluated using comprehensive metrics like accuracy, mean average precision (mAP), F1-Score, mean pixel accuracy (MPA), etc. From the findings, of the usage of DL models for forest fire surveillance systems have yielded favourable outcomes, whereby the majority of studies managed to achieve accuracy rates that exceeds 90%. To further enhance the efficacy of these models, future research can explore the optimal fine-tuning of the hyper-parameters, integrate various satellite data, implement generative data augmentation techniques, and refine the DL model architecture. In conclusion, this paper highlights the potential of deep learning methods in enhancing forest fire detection that is crucial for forest fire management and mitigation.