Displaying publications 21 - 40 of 121 in total

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  1. Tawfiq Zyoud TY, Abdul Rashid SN, Suppiah S, Abdul Rahim E, Mahmud R
    Med J Malaysia, 2020 07;75(4):411-418.
    PMID: 32724006
    INTRODUCTION: Autopsy is one of the most important approaches to identify clearly the exact cause of death, whether it was due to natural causes, sudden death, or traumatic. Various studies have been done in different countries regarding ways to improve the diagnosis during autopsy. The imaging approach is one of the methods that has been used to complement autopsy findings and to enhance the diagnosis for achieving the most accurate post-mortem diagnosis. The aim of this study is to identify the role of imaging modalities that complement routine autopsy and correlate the findings of diagnostic imaging that can help improve the accuracy of diagnosing the cause of death.

    METHODS: We sourced articles from Scopus, Ovid and PubMed databases for journal publications related to post-mortem diagnostic imaging. We highlight the most relevant full articles in English that explain the type of modality that was utilised and the added value it provided for diagnosing the cause of death.

    RESULTS: Minimally invasive autopsies assisted by imaging modalities added a great benefit to forensic medicine, and supported conventional autopsy. In particular the role of post mortem computed tomography (PMCT), post mortem computed tomography angiography (PMMR) and positron emission tomography computed tomography (PMCTA) that have incremental benefits in diagnosing traumatic death, fractures, tissue injuries, as well as the assessment of body height or weight for corpse identification.

    CONCLUSION: PMCT and PMMR, with particular emphasis on PMCTA, can provide higher accuracy than the other modalities. They can be regarded as indispensable methods that should be applied to the routine autopsy protocol, thus improving the findings and accuracy of diagnosing the cause of death.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  2. Alirr OI, Rahni AAA, Golkar E
    Int J Comput Assist Radiol Surg, 2018 Aug;13(8):1169-1176.
    PMID: 29860549 DOI: 10.1007/s11548-018-1801-z
    PURPOSE: Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities.

    METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.

    RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.

    CONCLUSIONS: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  3. Sabrina B, Tan KL, Johann FK, Andre D
    Med J Malaysia, 2018 08;73(4):255-256.
    PMID: 30121691 MyJurnal
    Ureteric and bladder injuries are uncommon, difficult to diagnose and rarely occur in isolation. Diagnosis is often delayed or missed at presentation. Therefore, high clinical suspicion and appropriate timing of computed tomography (CT) are of paramount importance. We report two cases (ureteropelvic junction avulsion and ruptured dome of bladder) whereby the presentations were subtle and would have been missed if not for high clinical suspicion. This article discusses the problems associated with these urologic injuries, as well as how to develop a high index of suspicion based on the pattern of anatomical disruption, mechanism of injury, physiological abnormality and comorbidity.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  4. Nazimi AJ, Khoo SC, Nabil S, Nordin R, Lan TH, Rajandram RK, et al.
    J Craniofac Surg, 2019 Oct;30(7):2159-2162.
    PMID: 31232997 DOI: 10.1097/SCS.0000000000005667
    Orbital fractures pose specific challenge in its surgical management. One of the greatest challenges is to obtain satisfactory reconstruction by correct positioning of orbital implant. Intraoperative computed tomography (CT) scan may facilitate this procedure. The aim of this study was to describe the early use of intraoperative CT in orbital fractures repair in our center. The authors assessed the revision types and rates that have occurred with this technique. With the use of pre-surgical planning, optical intraoperative navigation, and intraoperative CT, the impact of intraoperative CT on the management of 5 cases involving a total number of 14 orbital wall fractures were described. There were 6 pure orbital blowout wall fractures reconstructed, involving both medial and inferior wall of the orbit fracturing the transition zone and 8 impure orbital wall fractures in orbitozygomaticomaxillary complex fracture. 4 patients underwent primary and 1 had delayed orbital reconstruction. Intraoperative CT resulted in intraoperative orbital implant revision, following final navigation planning position, in 40% (2/5) of patients or 14% (2/14) of the fractures. In revised cases, both implant repositioning was conducted at posterior ledge of orbit. Intraoperative CT confirmed true to original reconstruction of medial wall, inferior wall and transition zone of the orbit. Two selected cases were illustrated. In conclusion, intraoperative CT allows real-time assessment of fracture reduction and immediate orbital implant revision, especially at posterior ledge. As a result, no postoperative imaging was indicated in any of the patients. Long-term follow-ups for orbital fracture patients managed with intraoperative CT is suggested.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  5. Chen Z, Rajamanickam L, Cao J, Zhao A, Hu X
    PLoS One, 2021;16(12):e0260758.
    PMID: 34879097 DOI: 10.1371/journal.pone.0260758
    This study aims to solve the overfitting problem caused by insufficient labeled images in the automatic image annotation field. We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. The model consists of an InceptionV3 network pretrained on the ImageNet dataset and a label localization algorithm. First, the pretrained InceptionV3 network extracts features from the target dataset that are used to train a specific classifier and fine-tune the entire network to obtain an optimal model. Then, the obtained model is used to derive the probabilities of the predicted labels. For this purpose, we introduce a squeeze and excitation (SE) module into the network architecture that augments the useful feature information, inhibits useless feature information, and conducts feature reweighting. Next, we perform label localization to obtain the label probabilities and determine the final label set for each image. During this process, the number of labels must be determined. The optimal K value is obtained experimentally and used to determine the number of predicted labels, thereby solving the empty label set problem that occurs when the predicted label values of images are below a fixed threshold. Experiments on the Corel5k multilabel image dataset verify that CNN-2L improves the labeling precision by 18% and 15% compared with the traditional multiple-Bernoulli relevance model (MBRM) and joint equal contribution (JEC) algorithms, respectively, and it improves the recall by 6% compared with JEC. Additionally, it improves the precision by 20% and 11% compared with the deep learning methods Weight-KNN and adaptive hypergraph learning (AHL), respectively. Although CNN-2L fails to improve the recall compared with the semantic extension model (SEM), it improves the comprehensive index of the F1 value by 1%. The experimental results reveal that the proposed transfer learning model based on a label localization strategy is effective for automatic image annotation and substantially boosts the multilabel image annotation performance.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  6. Fadzil F, Mei AKC, Mohd Khairy A, Kumar R, Mohd Azli AN
    Int J Environ Res Public Health, 2022 Nov 02;19(21).
    PMID: 36361190 DOI: 10.3390/ijerph192114311
    Patients with mild traumatic brain injury (MTBI) with intracerebral hemorrhage (ICH), particularly those at higher risk of having ICH progression, are typically prescribed a second head Computer Tomography (CT) scan to monitor the disease development. This study aimed to evaluate the role of a repeat head CT in MTBI patients at a higher risk of ICH progression by comparing the intervention rate between patients with and without ICH progression.

    METHODS: 192 patients with MTBI and ICH were treated between November 2019 to December 2020 at a single level II trauma center. The Glasgow Coma Scale (GCS) was used to classify MTBI, and initial head CT was performed according to the Canadian CT head rule. Patients with a higher risk of ICH progression, including the elderly (≥65 years old), patients on antiplatelets or anticoagulants, or patients with an initial head CT that revealed EDH, contusional bleeding, or SDH > 5 mm, and multiple ICH underwent a repeat head CT within 12 to 24 h later. Data regarding types of intervention, length of stay in the hospital, and outcome were collected. The risk of further neurological deterioration and readmission rates were compared between these two groups. All patients were followed up in the clinic after one month or contacted via phone if they did not return.

    RESULTS: 189 patients underwent scheduled repeated head CT, 18% had radiological intracranial bleed progression, and 82% had no changes. There were no statistically significant differences in terms of intervention rate, risk of neurological deterioration in the future, or readmission between them.

    CONCLUSION: Repeat head CT in mild TBI patients with no neurological deterioration is not recommended, even in patients with a higher risk of ICH progression.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  7. Tan TL, Illa NE, Ting SY, Hwong PL, Azmel A, Shunmugarajoo A, et al.
    Med J Malaysia, 2023 Mar;78(2):155-162.
    PMID: 36988524
    INTRODUCTION: The co-existence of coronavirus disease 2019 (COVID-19) and pulmonary thromboembolic (PTE) disease poses a great clinical challenge. To date, few researches have addressed this important clinical issue among the South-East Asian populations. The objectives of this study were as follow: (1) to describe the clinical characteristics and computed tomographical (CT) features of patients with PTE disease associated with COVID-19 infection and (2) to compare these parameters with those COVID-19 patients without PTE disease.

    MATERIALS AND METHODS: This cross-sectional study with retrospective record review was conducted in Hospital Tengku Ampuan Rahimah, Selangor, Malaysia. We included all hospitalised patients with confirmed COVID-19 infection who had undergone CT pulmonary angiogram (CTPA) examinations for suspected PTE disease between April 2021 and May 2021. Clinical data and laboratory data were extracted by trained data collectors, whilst CT images retrieved were analysed by a senior radiologist. Data analysis was performed using Statistical Package for the Social Sciences (SPSS) version 20.

    RESULTS: We studied 184 COVID-19 patients who were suspected to have PTE disease. CTPA examinations revealed a total of 150 patients (81.5%) suffered from concomitant PTE disease. Among the PTE cohort, the commonest comorbidities were diabetes mellitus (n=78, 52.0%), hypertension (n=66, 44.0%) and dyslipidaemia (n=25, 16.7%). They were generally more ill than the non-PTE cohort as they reported a significantly higher COVID-19 disease category during CTPA examination with p=0.042. Expectedly, their length of both intensive care unit stays (median number of days 8 vs. 3; p=0.021) and hospital stays (median number of days 14.5 vs. 12; p=0.006) were significantly longer. Intriguingly, almost all the subjects had received either therapeutic anticoagulation or thromboprophylactic therapy prior to CTPA examination (n=173, 94.0%). Besides, laboratory data analysis identified a significantly higher peak C-reactive protein (median 124.1 vs. 82.1; p=0.027) and ferritin levels (median 1469 vs. 1229; p=0.024) among them. Evaluation of CT features showed that COVID-19 pneumonia pattern (p<0.001) and pulmonary angiopathy (p<0.001) were significantly more profound among the PTE cohort. To note, the most proximal pulmonary thrombosis was located in the segmental (n=3, 2.0%) and subsegmental pulmonary arteries (n=147, 98.0%). Also, the thrombosis predominantly occurred in bilateral lungs with multilobar involvement (n=95, 63.3%).

    CONCLUSION: Overall, PTE disease remains prevalent among COVID-19 patients despite timely administration of thromboprophylactic therapy. The presence of hyperinflammatory activities, unique thrombotic locations as well as concurrent pulmonary parenchyma and vasculature aberrations in our PTE cohort implicate immunothrombosis as the principal mechanism of this novel phenomenon. We strongly recommend future researchers to elucidate this important clinical disease among our post- COVID vaccination populations.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  8. Lam DC, Liam CK, Andarini S, Park S, Tan DSW, Singh N, et al.
    J Thorac Oncol, 2023 Oct;18(10):1303-1322.
    PMID: 37390982 DOI: 10.1016/j.jtho.2023.06.014
    INTRODUCTION: The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

    METHOD: A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

    RESULTS: Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

    CONCLUSIONS: Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  9. Wang W, Zhao X, Jia Y, Xu J
    PLoS One, 2024;19(2):e0297578.
    PMID: 38319912 DOI: 10.1371/journal.pone.0297578
    The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  10. Sachithanandan A, Lockman H, Azman RR, Tho LM, Ban EZ, Ramon V
    Med J Malaysia, 2024 Jan;79(1):9-14.
    PMID: 38287751
    INTRODUCTION: The poor prognosis of lung cancer has been largely attributed to the fact that most patients present with advanced stage disease. Although low dose computed tomography (LDCT) is presently considered the optimal imaging modality for lung cancer screening, its use has been hampered by cost and accessibility. One possible approach to facilitate lung cancer screening is to implement a risk-stratification step with chest radiography, given its ease of access and affordability. Furthermore, implementation of artificial-intelligence (AI) in chest radiography is expected to improve the detection of indeterminate pulmonary nodules, which may represent early lung cancer.

    MATERIALS AND METHODS: This consensus statement was formulated by a panel of five experts of primary care and specialist doctors. A lung cancer screening algorithm was proposed for implementation locally.

    RESULTS: In an earlier pilot project collaboration, AI-assisted chest radiography had been incorporated into lung cancer screening in the community. Preliminary experience in the pilot project suggests that the system is easy to use, affordable and scalable. Drawing from experience with the pilot project, a standardised lung cancer screening algorithm using AI in Malaysia was proposed. Requirements for such a screening programme, expected outcomes and limitations of AI-assisted chest radiography were also discussed.

    CONCLUSION: The combined strategy of AI-assisted chest radiography and complementary LDCT imaging has great potential in detecting early-stage lung cancer in a timely manner, and irrespective of risk status. The proposed screening algorithm provides a guide for clinicians in Malaysia to participate in screening efforts.

    Matched MeSH terms: Tomography, X-Ray Computed/methods
  11. Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R
    Sci Rep, 2021 Jul 08;11(1):14133.
    PMID: 34238992 DOI: 10.1038/s41598-021-93658-y
    COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  12. Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R
    Sci Rep, 2021 04 15;11(1):8304.
    PMID: 33859222 DOI: 10.1038/s41598-021-87523-1
    COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  13. Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH
    Tomography, 2023 Dec 05;9(6):2158-2189.
    PMID: 38133073 DOI: 10.3390/tomography9060169
    Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  14. Anderson PJ, Yong R, Surman TL, Rajion ZA, Ranjitkar S
    Aust Dent J, 2014 Jun;59 Suppl 1:174-85.
    PMID: 24611727 DOI: 10.1111/adj.12154
    Following the invention of the first computed tomography (CT) scanner in the early 1970s, many innovations in three-dimensional (3D) diagnostic imaging technology have occurred, leading to a wide range of applications in craniofacial clinical practice and research. Three-dimensional image analysis provides superior and more detailed information compared with conventional plain two-dimensional (2D) radiography, with the added benefit of 3D printing for preoperative treatment planning and regenerative therapy. Current state-of-the-art multidetector CT (MDCT), also known as medical CT, has an important role in the diagnosis and management of craniofacial injuries and pathology. Three-dimensional cone beam CT (CBCT), pioneered in the 1990s, is gaining increasing popularity in dental and craniofacial clinical practice because of its faster image acquisition at a lower radiation dose, but sound guidelines are needed to ensure its optimal clinical use. Recent innovations in micro-computed tomography (micro-CT) have revolutionized craniofacial biology research by enabling higher resolution scanning of teeth beyond the capabilities of MDCT and CBCT, presenting new prospects for translational clinical research. Even after four decades of refinement, CT technology continues to advance and broaden the horizons of craniofacial clinical practice and phenomics research.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  15. Sabarudin A, Yusof MZ, Mohamad M, Sun Z
    Radiat Prot Dosimetry, 2014 Dec;162(3):316-21.
    PMID: 24255172 DOI: 10.1093/rpd/nct280
    A study on the radiation dose associated with cerebral CT angiography (CTA) and CT perfusion (CTP) was conducted on an anthropomorphic phantom with the aim of estimating the effective dose (E) and entrance skin dose (ESD) in the eyes and thyroid gland during different CTA and CTP protocols. The E was calculated to be 0.61 and 0.28 mSv in CTA with 100 and 80 kV(p), respectively. In contrast, CTP resulted in an estimated E of 2.74 and 2.07 mSv corresponding to 40 and 30 s protocols, respectively. The eyes received a higher ESD than the thyroid gland in all of these protocols. The results of this study indicate that combining both CTA and CTP procedures are not recommended in the stroke evaluation due to high radiation dose. Application of modified techniques in CTA (80 kV(p)) and CTP (30 s) is highly recommended in clinical practice for further radiation dose reduction.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  16. Teo PC, Kassim AY, Thevarajan K
    J Orthop Surg (Hong Kong), 2013 Dec;21(3):340-6.
    PMID: 24366797
    To propose a novel method to measure the neck shaft angle and anteversion of the femur using anteroposterior and 45-degree oblique radiographs.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  17. Abdul Rashid SN, Mohamad Saini SB, Abdul Hamid S, Muhammad SJ, Mahmud R, Thali MJ, et al.
    Br J Radiol, 2014 Apr;87(1036):20130472.
    PMID: 24472728 DOI: 10.1259/bjr.20130472
    The purpose of this study was to retrospectively evaluate the sensitivity, specificity and accuracy of identifying methamphetamine (MA) internal payloads in "drug mules" by plain abdominal digital radiography (DR).
    Matched MeSH terms: Tomography, X-Ray Computed/methods
  18. Adeshina AM, Hashim R, Khalid NE, Abidin SZ
    Interdiscip Sci, 2013 Mar;5(1):23-36.
    PMID: 23605637 DOI: 10.1007/s12539-013-0155-z
    In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain's blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom's macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens' functionality as a 3-D Multimodal Visualization System.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  19. Daud R, Abdul Kadir MR, Izman S, Md Saad AP, Lee MH, Che Ahmad A
    J Foot Ankle Surg, 2013 Jul-Aug;52(4):426-31.
    PMID: 23623302 DOI: 10.1053/j.jfas.2013.03.007
    The trapezium shape of the talar dome limits the use of 2-dimensional plain radiography for morphometric assessment because only 2 of the 4 required parameters can be measured. We used computed tomography data to measure the 4 morphologic parameters of the trochlea tali: anterior width, posterior width, trochlea tali length, and angle of trapezium shape. A total of 99 subjects underwent computed tomography scanning, and the left and right talus bones were both virtually modeled in 3 dimensions. The 4 morphologic parameters were measured 3 times each to obtain the intraclass correlation, and analysis of variance was used to check for any significant differences between the repeated measurements. The average intraclass correlation coefficient for the measurements for 2 to 3 trials was 0.94 ± 0.04. Statistical analyses were performed on the data from all 198 talus bones using SAS software, comparing male and female and left and right bones. All 4 morphometric values were greater in the male group. No significant differences were found between the left and right talus bones. A strong positive correlation was observed between the trochlea tali length and the anterior width. The angle of trapezium shape showed no correlation with the other 3 parameters. The measurements were compared with the dimensions of the current talar components of 4 total ankle arthroplasty implants. However, most of them did not perfectly match the trapezium shape of the talus from our population. We successfully analyzed the trapezium shape of the trochlea tali using reliable virtual 3-dimensional measurements. Compared with other published reports, our study showed a relatively smaller dimension of the trochlea tali than the European counterparts.
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
  20. Faizah MZ, Sharifah MI, Johoruddin K, Juliana AL
    Med J Malaysia, 2011 Oct;66(4):367-8.
    PMID: 22299562 MyJurnal
    Matched MeSH terms: Tomography, X-Ray Computed/methods*
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