Displaying publications 61 - 80 of 765 in total

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  1. Mumin NA, Rahmat K, Hamid MTR, Ng WL, Chan WY, Cheah XY, et al.
    Curr Med Imaging, 2021;17(4):552-558.
    PMID: 33030134 DOI: 10.2174/1573405616666201007161119
    BACKGROUND: Primary breast angiosarcoma is a rare malignancy with non-specific clinical and radiological findings.

    CASE REPORT: A 30-year-old lady presented with left breast pain and lumpiness for over one year. She has had several breast ultrasounds (US) and was treated for acute mastitis and abscess. Subsequently, in view of the rapid growth of the lump and worsening pain, she was re-investigated with US, elastography, digital breast tomosynthesis (DBT) and MRI. MRI raised the suspicion of angiosarcoma. The diagnosis was confirmed after biopsy and she underwent mastectomy.

    DISCUSSION: Literature review on imaging findings of breast angiosarcoma, especially on MRI, is discussed. MRI features showed heterogeneous low signal intensity on T1 and high signal intensity on T2. Dynamic contrast enhancement (DCE) features included either early enhancement with or without washout in the delayed phase, and some reported central areas of non-enhancement.

    CONCLUSION: This case report emphasises on the importance of MRI in clinching the diagnosis of breast angiosarcoma, and hence, should be offered sooner to prevent diagnostic delay.

    Matched MeSH terms: Magnetic Resonance Imaging
  2. Khalil A, Rahimi A, Luthfi A, Azizan MM, Satapathy SC, Hasikin K, et al.
    Front Public Health, 2021;9:752509.
    PMID: 34621723 DOI: 10.3389/fpubh.2021.752509
    A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.
    Matched MeSH terms: Magnetic Resonance Imaging
  3. Yong CW, Lai KW, Murphy BP, Hum YC
    Curr Med Imaging, 2021;17(8):981-987.
    PMID: 33319690 DOI: 10.2174/1573405616666201214122409
    BACKGROUND: Osteoarthritis (OA) is a common degenerative joint inflammation that may lead to disability. Although OA is not lethal, this disease will remarkably affect patient's mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression.

    INTRODUCTION: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder-decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation.

    METHODS: This study trained and compared 10 encoder-decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from the Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on physical specifications and segmentation performances.

    RESULTS: LadderNet has the least trainable parameters with the model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC.

    CONCLUSION: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images, while LadderNet served as an alternative if there are hardware limitations during production.

    Matched MeSH terms: Magnetic Resonance Imaging
  4. Ramli NM, Bae YJ
    Neuroimaging Clin N Am, 2023 Feb;33(1):43-56.
    PMID: 36404046 DOI: 10.1016/j.nic.2022.07.002
    MR imaging is essential in diagnosing viral encephalitis. Clinical features, cerebrospinal fluid analysis and pathogen confirmation by polymerase chain reaction can be supported by assessing imaging features. MR imaging patterns with typical locations can identify pathogens such as temporal lobe for herpes simplex virus type 1; bilateral thalami for Japanese encephalitis and influenza virus ; and brainstem for enterovirus and rabies. In this article, we have reviewed representative viral encephalitis and its MR imaging patterns. In addition, we also presented acute viral encephalitis without typical MR imaging patterns, such as dengue and varicella-zoster virus encephalitis.
    Matched MeSH terms: Magnetic Resonance Imaging
  5. Singhania U, Tripathy B, Hasan MK, Anumbe NC, Alboaneen D, Ahmed FRA, et al.
    Front Public Health, 2021;9:751536.
    PMID: 34708019 DOI: 10.3389/fpubh.2021.751536
    Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
    Matched MeSH terms: Magnetic Resonance Imaging
  6. Guo C, Dong J, Deng L, Cheng K, Xu Y, Zhu H, et al.
    Molecules, 2023 May 25;28(11).
    PMID: 37298809 DOI: 10.3390/molecules28114332
    The quality of Panax Linn products available in the market is threatened by adulteration with different Panax species, such as Panax quinquefolium (PQ), Panax ginseng (PG), and Panax notoginseng (PN). In this paper, we established a 2D band-selective heteronuclear single quantum coherence (bs-HSQC) NMR method to discriminate species and detect adulteration of Panax Linn. The method involves selective excitation of the anomeric carbon resonance region of saponins and non-uniform sampling (NUS) to obtain high-resolution spectra in less than 10 min. The combined strategy overcomes the signal overlap limitation in 1H NMR and the long acquisition time in traditional HSQC. The present results showed that twelve well-separated resonance peaks can be assigned in the bs-HSQC spectra, which are of high resolution, good repeatability, and precision. Notably, the identification accuracy of species was found to be 100% for all tests conducted in the present study. Furthermore, in combination with multivariate statistical methods, the proposed method can effectively determine the composition proportion of adulterants (from 10% to 90%). Based on the PLS-DA models, the identification accuracy was greater than 80% when composition proportion of adulterants was 10%. Thus, the proposed method may provide a fast, practical, and effective analysis technique for food quality control or authenticity identification.
    Matched MeSH terms: Magnetic Resonance Imaging
  7. Manan HA, Yahya N, Han P, Hummel T
    Brain Struct Funct, 2022 Jan;227(1):177-202.
    PMID: 34635958 DOI: 10.1007/s00429-021-02397-3
    Brain structural features of healthy individuals are associated with olfactory functions. However, due to the pathophysiological differences, congenital and acquired anosmia may exhibit different structural characteristics. A systematic review was undertaken to compare brain structural features between patients with congenital and acquired anosmia. A systematic search was conducted using PubMed/MEDLINE and Scopus electronic databases to identify eligible reports on anosmia and structural changes and reported according to PRISMA guidelines. Reports were extracted for information on demographics, psychophysical evaluation, and structural changes. Then, the report was systematically reviewed based on various aetiologies of anosmia in relation to (1) olfactory bulb, (2) olfactory sulcus, (3) grey matter (GM), and white matter (WM) changes. Twenty-eight published studies were identified. All studies reported consistent findings with strong associations between olfactory bulb volume and olfactory function across etiologies. However, the association of olfactory function with olfactory sulcus depth was inconsistent. The present study observed morphological variations in GM and WM volume in congenital and acquired anosmia. In acquired anosmia, reduced olfactory function is associated with reduced volumes and thickness involving the gyrus rectus, medial orbitofrontal cortex, anterior cingulate cortex, and cerebellum. These findings contrast to those observed in congenital anosmia, where a reduced olfactory function is associated with a larger volume and higher thickness in parts of the olfactory network, including the piriform cortex, orbitofrontal cortex, and insula. The present review proposes that the structural characteristics in congenital and acquired anosmia are altered differently. The mechanisms behind these changes are likely to be multifactorial and involve the interaction with the environment.
    Matched MeSH terms: Magnetic Resonance Imaging
  8. Wang D, Fu Y, Ashraf MA
    Open Med (Wars), 2015;10(1):425-433.
    PMID: 28352731 DOI: 10.1515/med-2015-0074
    Tagged Magnetic Resonance Imaging (MRI) is a noninvasive technique for examining myocardial function and deformation. Tagged MRI can also be used in quasi-static MR elastography to acquire strain maps of other biological soft tissues. Harmonic phase (HARP) provides automatic and rapid analysis of tagged MR images for the quantification and visualization of myocardial strain. We propose a new artifact reduction method in strain maps. Image intensity of the DC component is estimated and subtracted from spatial modulation of magnetization (SPAMM) tagged MR images. DC peak interference in harmonic phase extraction is greatly reduced after DC component subtraction. The proposed method is validated using both simulated and MR acquired tagged images. Strain maps are obtained with better accuracy and smoothness after DC component subtraction.
    Matched MeSH terms: Magnetic Resonance Imaging
  9. Chew SH, Achmad Sankala HB, Chew E, Md Arif MHB, Mohd Zain NR, Hashim H, et al.
    Mult Scler Relat Disord, 2023 Nov;79:104992.
    PMID: 37717306 DOI: 10.1016/j.msard.2023.104992
    BACKGROUND: Differentiating tumefactive demyelinating lesions (TDL) from neoplasms of the central nervous system continues to be a diagnostic dilemma in many cases.

    OBJECTIVE: Our study aimed to examine and contrast the clinical and radiological characteristics of TDL, high-grade gliomas (HGG) and primary CNS lymphoma (CNSL).

    METHOD: This was a retrospective review of 66 patients (23 TDL, 31 HGG and 12 CNSL). Clinical and laboratory data were obtained. MRI brain at presentation were analyzed by two independent, blinded neuroradiologists.

    RESULTS: Patients with TDLs were younger and predominantly female. Sensorimotor deficits and ataxia were more common amongst TDL whereas headaches and altered mental status were associated with HGG and CNSL. Compared to HGG and CNSL, MRI characteristics supporting TDL included relatively smaller size, lack of or mild mass effect, incomplete peripheral rim enhancement, absence of central enhancement or restricted diffusion, lack of cortical involvement, and presence of remote white matter lesions on the index scan. Paradoxically, some TDLs may present atypically or radiologically mimic CNS lymphomas.

    CONCLUSION: Careful evaluation of clinical and radiological features helps in differentiating TDLs at first presentation from CNS neoplasms.

    Matched MeSH terms: Magnetic Resonance Imaging
  10. Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, et al.
    J Healthc Eng, 2022;2022:4138666.
    PMID: 35222885 DOI: 10.1155/2022/4138666
    Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
    Matched MeSH terms: Magnetic Resonance Imaging
  11. Dewiputri WI, Auer T
    Malays J Med Sci, 2013 Oct;20(5):5-15.
    PMID: 24643368
    Neurofeedback (NFB) allows subjects to learn how to volitionally influence the neuronal activation in the brain by employing real-time neural activity as feedback. NFB has already been performed with electroencephalography (EEG) since the 1970s. Functional MRI (fMRI), offering a higher spatial resolution, has further increased the spatial specificity. In this paper, we briefly outline the general principles behind NFB, the implementation of fMRI-NFB studies, the feasibility of fMRI-NFB, and the application of NFB as a supplementary therapy tool.
    Matched MeSH terms: Magnetic Resonance Imaging
  12. Abd Hamid AI, Yusoff AN, Mukari SZ, Mohamad M
    Malays J Med Sci, 2011 Apr;18(2):3-15.
    PMID: 22135581 MyJurnal
    In spite of extensive research conducted to study how human brain works, little is known about a special function of the brain that stores and manipulates information-the working memory-and how noise influences this special ability. In this study, Functional magnetic resonance imaging (fMRI) was used to investigate brain responses to arithmetic problems solved in noisy and quiet backgrounds.
    Matched MeSH terms: Magnetic Resonance Imaging
  13. Mohamed WN, Abdullah NN, Muda AS
    Malays J Med Sci, 2008 Jul;15(3):55-7.
    PMID: 22570590 MyJurnal
    We report a rare case of Arteriovenous malformation (AVM) of the scalp in a 30 year-old Malay gentleman who presented with painless forehead swelling since birth. Magnetic Resonance Imaging (MRI) and cerebral angiogram performed and the findings are discussed.
    Matched MeSH terms: Magnetic Resonance Imaging
  14. Ramli N, Nair SR, Ramli NM, Lim SY
    Clin Radiol, 2015 May;70(5):555-64.
    PMID: 25752581 DOI: 10.1016/j.crad.2015.01.005
    The purpose of this review is to illustrate the differentiating features of multiple-system atrophy from Parkinson's disease at MRI. The various MRI sequences helpful in the differentiation will be discussed, including newer methods, such as diffusion tensor imaging, MR spectroscopy, and nuclear imaging.
    Matched MeSH terms: Magnetic Resonance Imaging/methods*
  15. Zourmand A, Mirhassani SM, Ting HN, Bux SI, Ng KH, Bilgen M, et al.
    Biomed Eng Online, 2014;13:103.
    PMID: 25060583 DOI: 10.1186/1475-925X-13-103
    The phonetic properties of six Malay vowels are investigated using magnetic resonance imaging (MRI) to visualize the vocal tract in order to obtain dynamic articulatory parameters during speech production. To resolve image blurring due to the tongue movement during the scanning process, a method based on active contour extraction is used to track tongue contours. The proposed method efficiently tracks tongue contours despite the partial blurring of MRI images. Consequently, the articulatory parameters that are effectively measured as tongue movement is observed, and the specific shape of the tongue and its position for all six uttered Malay vowels are determined.Speech rehabilitation procedure demands some kind of visual perceivable prototype of speech articulation. To investigate the validity of the measured articulatory parameters based on acoustic theory of speech production, an acoustic analysis based on the uttered vowels by subjects has been performed. As the acoustic speech and articulatory parameters of uttered speech were examined, a correlation between formant frequencies and articulatory parameters was observed. The experiments reported a positive correlation between the constriction location of the tongue body and the first formant frequency, as well as a negative correlation between the constriction location of the tongue tip and the second formant frequency. The results demonstrate that the proposed method is an effective tool for the dynamic study of speech production.
    Matched MeSH terms: Magnetic Resonance Imaging*
  16. Amin H, Malik AS
    Neurosciences (Riyadh), 2013 Oct;18(4):330-44.
    PMID: 24141456
    Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.
    Matched MeSH terms: Magnetic Resonance Imaging*
  17. Faisal A, Parveen S, Badsha S, Sarwar H, Reza AW
    J Med Syst, 2013 Jun;37(3):9938.
    PMID: 23504472 DOI: 10.1007/s10916-013-9938-3
    An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.
    Matched MeSH terms: Magnetic Resonance Imaging*
  18. Gee TS, Ghani ARI, Idris B, Awang MS
    Med J Malaysia, 2012 Aug;67(4):438-41.
    PMID: 23082462 MyJurnal
    Matched MeSH terms: Magnetic Resonance Imaging*
  19. Nair SR, Ramli NM, Rahmat K, Mei-Ling ST
    Neurol India, 2012 Jul-Aug;60(4):426-8.
    PMID: 22954982 DOI: 10.4103/0028-3886.100712
    Matched MeSH terms: Magnetic Resonance Imaging; Diffusion Magnetic Resonance Imaging
  20. Ong KH, Ramachandram D, Mandava R, Shuaib IL
    Magn Reson Imaging, 2012 Jul;30(6):807-23.
    PMID: 22578927 DOI: 10.1016/j.mri.2012.01.007
    White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset.
    Matched MeSH terms: Magnetic Resonance Imaging/methods*
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