Displaying publications 1 - 20 of 765 in total

  1. Wen LY, Wah LP, Mohamad NF, Singh S, Toong LY
    J Fam Pract, 2023 Mar;72(2):E1-E7.
    PMID: 36947782 DOI: 10.12788/jfp.0563
    A patient's age, clinical presentation, medical history, and circumstances at time of palsy onset suggest likely underlying causes and help prioritize choice of imaging.
    Matched MeSH terms: Magnetic Resonance Imaging*
  2. Manan HA, Franz EA, Yahya N
    Eur J Cancer Care (Engl), 2021 Jul;30(4):e13428.
    PMID: 33592671 DOI: 10.1111/ecc.13428
    PURPOSE: Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique.

    METHODS: A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines.

    RESULTS: Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool.

    CONCLUSIONS: rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.

    Matched MeSH terms: Magnetic Resonance Imaging*
  3. Voon NS, Manan HA, Yahya N
    Strahlenther Onkol, 2023 Aug;199(8):706-717.
    PMID: 37280382 DOI: 10.1007/s00066-023-02089-3
    PURPOSE: Increasing evidence implicates changes in brain function following radiotherapy for head and neck cancer as precursors for brain dysfunction. These changes may thus be used as biomarkers for early detection. This review aimed to determine the role of resting-state functional magnetic resonance imaging (rs-fMRI) in detecting brain functional changes.

    METHODS: A systematic search was performed in the PubMed, Scopus, and Web of Science (WoS) databases in June 2022. Patients with head and neck cancer treated with radiotherapy and periodic rs-fMRI assessments were included. A meta-analysis was performed to determine the potential of rs-fMRI for detecting brain changes.

    RESULTS: Ten studies with a total of 513 subjects (head and neck cancer patients, n = 437; healthy controls, n = 76) were included. A significance of rs-fMRI for detecting brain changes in the temporal and frontal lobes, cingulate cortex, and cuneus was demonstrated in most studies. These changes were reported to be associated with dose (6/10 studies) and latency (4/10 studies). A strong effect size (r = 0.71, p 

    Matched MeSH terms: Magnetic Resonance Imaging/methods
  4. Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H
    J Healthc Eng, 2022;2022:2550120.
    PMID: 35444781 DOI: 10.1155/2022/2550120
    In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  5. Chow LS, Paley MNJ
    Magn Reson Imaging, 2021 06;79:76-84.
    PMID: 33753137 DOI: 10.1016/j.mri.2021.03.014
    The optic nerve is known to be one of the largest nerve bundles in the human central nervous system. There have been many studies of optic nerve imaging and post-processing that have provided insights into pathophysiology of optic neuritis related to multiple sclerosis and neuromyelitis optica spectrum disorder, glaucoma, and Leber's hereditary optic neuropathy. There are many challenges in optic nerve imaging, due to the morphology of the nerve through its course to the optic chiasm, its mobility due to eye movements and the high signal from cerebrospinal fluid and orbital fat surrounding the optic nerve. Recently, many advanced and fast imaging sequences have been used with post-processing techniques in attempts to produce higher resolution images of the optic nerve for evaluating various diseases. Magnetic resonance imaging (MRI) is one of the most common imaging methodologies for the optic nerve. This review paper will focus on recent MRI advances in optic nerve imaging and explain several post-processing techniques being used for analysis of optic nerve images. Finally, some challenges and potential for future optic nerve studies will be discussed.
    Matched MeSH terms: Magnetic Resonance Imaging
  6. Nasaruddin, N.H., Yusoff, A.N., Sharanjeet Kaur, Nasrudin, N.F., Muda, S.
    Ocular abnormalities have apparent effects on brain activation. However, neuroimaging data about the ocular characteristics of healthy participants are still lacking to be compared with data for patients with ocular pathology. The objective of this multiple participants’ functional magnetic resonance imaging (fMRI) studies was to investigate the brain activation characteristics of healthy participants when they view stimuli of various shapes, pattern and size. During the fMRI scans, the participants view the growing ring, rotating wedge, flipping hour glass/bow tie, quadrant arc and full checker board stimuli. All stimuli have elements of black-and-white checkerboard pattern. Statistical parametric mapping (SPM) was used in generating brain activation via fixed-effects (FFX) and conjunction analyses. The stimuli of various shapes, pattern and size produce different brain activation with more activation concentrated in the left hemisphere. These results are supported by the conjunction analysis which indicated that the left pre-central, post-central, superior temporal and occipital gyrus as well as the left cingulate cortices were involved when the participants viewed each given stimulus. Differential activation analysis showed activation with high specificity in the occipital region due to the stimuli of various shapes, pattern and size. The activation in the right middle temporal gyrus was found to be significantly higher in response to moving stimuli as compared to stationary stimuli. This confi rms the involvement of the right middle temporal gyrus in the observation of movements. The black-and-white checkerboard stimuli of various shapes, pattern and size, stationary and moving was found to 1) activate visual as well as other cortices in temporal and parietal lobes, 2) cause asymmetry in brain function and 3) exhibit functional integration characteristics in several brain areas.
    Keywords: fMRI; SPM; visual stimulus; occipital gyrus; middle temporal gyrus
    Matched MeSH terms: Magnetic Resonance Imaging
  7. Khan DM, Kamel N, Muzaimi M, Hill T
    Brain Connect, 2021 02;11(1):12-29.
    PMID: 32842756 DOI: 10.1089/brain.2019.0721
    Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.
    Matched MeSH terms: Magnetic Resonance Imaging
  8. Hafizzi Awang NMS, Mohd Noor R, Ramli R, Abdullah B
    Gulf J Oncolog, 2022 Jan;1(38):78-81.
    PMID: 35156648
    BACKGROUND: The infratemporal fossa poses a great challenge to surgeons due to its complex anatomy and communications to many surrounding areas. The disorders that arise from this area can be infections and neoplasms. They can cause varieties of complications due to the extension of the pathologies and compression effect to the other adjacent structures. Inflammatory pseudotumor of the infratemporal fossa is one of the rare disorders of the head and neck.

    CASE PRESENTATION: We report a patient with a pseudotumor of infratemporal fossa that extends to the orbital area and cavernous sinus, causing orbital apex syndromes. The diagnostic imaging, different surgical approaches of the biopsy and methods of treatment of this case are discussed.

    DISCUSSION AND CONCLUSION: Radiological imaging and immunohistopathology are essential in establishing the diagnosis and determine the complications. The surgeons must well understand the characteristics and the impact of the disorders on the adjacent structure and give prompt decision to provide definitive treatments.

    Matched MeSH terms: Magnetic Resonance Imaging
  9. Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, et al.
    Comput Intell Neurosci, 2021;2021:4931437.
    PMID: 34804143 DOI: 10.1155/2021/4931437
    Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.
    Matched MeSH terms: Magnetic Resonance Imaging
  10. Abdullah BJ, Bux SI, Chien D
    Med J Malaysia, 1997 Dec;52(4):445-53; quiz 454.
    PMID: 10968127
    MRI is now an important diagnostic tool in medical management. There are numerous safety issues to be considered by the clinicians prior to requesting an MRI examination for their patients. These include those related to the magnetic field, gradient magnetic fields, the patient and contrast medium. This paper discusses the dangers and necessary precautions essential to reduce the risk of untoward complications from MRI.
    Matched MeSH terms: Magnetic Resonance Imaging/adverse effects*
  11. Chuah SH, Md Sari NA, Chew BT, Tan LK, Chiam YK, Chan BT, et al.
    Phys Med, 2020 Oct;78:137-149.
    PMID: 33007738 DOI: 10.1016/j.ejmp.2020.08.022
    Differential diagnosis of hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) is clinically challenging but important for treatment management. This study aims to phenotype HHD and HCM in 3D + time domain by using a multiparametric motion-corrected personalized modeling algorithm and cardiac magnetic resonance (CMR). 44 CMR data, including 12 healthy, 16 HHD and 16 HCM cases, were examined. Multiple CMR phenotype data consisting of geometric and dynamic variables were extracted globally and regionally from the models over a full cardiac cycle for comparison against healthy models and clinical reports. Statistical classifications were used to identify the distinctive characteristics and disease subtypes with overlapping functional data, providing insights into the challenges for differential diagnosis of both types of disease. While HCM is characterized by localized extreme hypertrophy of the LV, wall thickening/contraction/strain was found to be normal and in sync, though it was occasionally exaggerated at normotrophic/less severely hypertrophic regions during systole to preserve the overall ejection fraction (EF) and systolic functionality. Additionally, we observed that hypertrophy in HHD could also be localized, although at less extreme conditions (i.e. more concentric). While fibrosis occurs mostly in those HCM cases with aortic obstruction, only minority of HHD patients were found affected by fibrosis. We demonstrate that subgroups of HHD (i.e. preserved and reduced EF: HHDpEF & HHDrEF) have different 3D + time CMR characteristics. While HHDpEF has cardiac functions in normal range, dilation and heart failure are indicated in HHDrEF as reflected by low LV wall thickening/contraction/strain and synchrony, as well as much reduced EF.
    Matched MeSH terms: Magnetic Resonance Imaging; Magnetic Resonance Imaging, Cine
  12. Liong CC, Rahmat K, Mah JS, Lim SY, Tan AH
    Can J Neurol Sci, 2016 Sep;43(5):719-20.
    PMID: 27670213 DOI: 10.1017/cjn.2016.269
    Matched MeSH terms: Magnetic Resonance Imaging*
  13. Liew A, Lee CC, Subramaniam V, Lan BL, Tan M
    J Magn Reson Imaging, 2023 Jun;57(6):1728-1740.
    PMID: 36208095 DOI: 10.1002/jmri.28456
    BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions.

    PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training.

    STUDY TYPE: Retrospective.

    POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS).

    FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS).

    ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM.

    STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan.

    RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected.

    DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity.


    Matched MeSH terms: Magnetic Resonance Imaging/methods
  14. Mabel HM, Othman NB, Cheah WK
    Med J Malaysia, 2022 May;77(3):403-405.
    PMID: 35638501
    Pontine infarct is a rare but clinically significant cause of an isolated facial nerve palsy. Prompt diagnosis with the use of magnetic resonance imaging (MRI) allows early initiation of treatment for such patients. We report a 62-year-old gentleman with diabetes, hypertension, and gout, presenting with lower motor neuron facial nerve palsy. This report highlights that isolated facial nerve palsy is not always associated with Bell's palsy, which remains the commonest cause of facial nerve paralysis. A thorough neurological examination and good clinical correlation with the patient's history and physical findings, coupled with the use of facial nerve anatomical knowledge and early employment of MRI, are imperative in clinching the diagnosis.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  15. Hamzah N, Malim NHAH, Abdullah JM, Sumari P, Mokhtar AM, Rosli SNS, et al.
    Neuroinformatics, 2023 Jul;21(3):589-600.
    PMID: 37344699 DOI: 10.1007/s12021-023-09637-3
    The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.
    Matched MeSH terms: Magnetic Resonance Imaging*
  16. Kaplan E, Chan WY, Altinsoy HB, Baygin M, Barua PD, Chakraborty S, et al.
    J Digit Imaging, 2023 Dec;36(6):2441-2460.
    PMID: 37537514 DOI: 10.1007/s10278-023-00889-8
    Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  17. Hanafiah M, Johari B, Ab Mumin N, Musa AA, Hanafiah H
    Br J Radiol, 2022 May 01;95(1133):20210857.
    PMID: 35007174 DOI: 10.1259/bjr.20210857
    OBJECTIVE: Primary open-angle glaucoma (POAG) is a degenerative optic neuropathy disease which has somewhat similar pathophysiology to Alzheimer's disease (AD). This study aims to determine the presence of medial temporal atrophy and parietal lobe atrophy in patients with POAG compared to normal controls using medial temporal atrophy (MTA) scoring and posterior cortical atrophy (PCA) scoring system on T1 magnetization-prepared rapid gradient-echo.

    METHODS: 50 POAG patients and 50 normal subjects were recruited and an MRI brain with T1-magnetization-prepared rapid gradient-echo was performed. Medial temporal lobe and parietal lobe atrophy were by MTA and PCA/Koedam scoring. The score of the PCA and MTA were compared between the POAG group and the controls.

    RESULTS: There was a significant statistical difference between PCA score in POAG and the healthy control group (p-value = 0.026). There is no statistical difference between MTA score in POAG compared to the healthy control group (p-value = 0.58).

    CONCLUSION: This study suggests a correlation between POAG and PCA score. Potential application of this scoring method in clinical diagnosis and monitoring of POAG patients.

    ADVANCES IN KNOWLEDGE: The scoring method used in AD may also be applied in the diagnosis and monitoring of POAGMRI brain, specifically rapid volumetric T1 spoiled gradient echo sequence, may be applied in POAG assessment.

    Matched MeSH terms: Magnetic Resonance Imaging/methods
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