Displaying publications 21 - 40 of 765 in total

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  1. Choong MK, Logeswaran R, Bister M
    J Med Syst, 2006 Jun;30(3):139-43.
    PMID: 16848126
    This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.
    Matched MeSH terms: Magnetic Resonance Imaging/methods*
  2. Huang SG, Samdin SB, Ting CM, Ombao H, Chung MK
    J Neurosci Methods, 2020 02 01;331:108480.
    PMID: 31760059 DOI: 10.1016/j.jneumeth.2019.108480
    BACKGROUND: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However, the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI.

    NEW METHOD: To overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm.

    RESULTS: The proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified.

    COMPARISON WITH EXISTING METHODS: We demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states.

    CONCLUSIONS: We propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.

    Matched MeSH terms: Magnetic Resonance Imaging*
  3. Bilal M, Shah JA, Qureshi IM, Kadir K
    Int J Biomed Imaging, 2018;2018:7803067.
    PMID: 29610569 DOI: 10.1155/2018/7803067
    Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. TheL1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated andin vivo2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison betweenk-tFOCUSS with MEMC and the proposed method.
    Matched MeSH terms: Magnetic Resonance Imaging; Magnetic Resonance Imaging, Cine
  4. Gandhamal A, Talbar S, Gajre S, Razak R, Hani AFM, Kumar D
    Comput Biol Med, 2017 Sep 01;88:110-125.
    PMID: 28711767 DOI: 10.1016/j.compbiomed.2017.07.008
    Knee osteoarthritis (OA) progression can be monitored by measuring changes in the subchondral bone structure such as area and shape from MR images as an imaging biomarker. However, measurements of these minute changes are highly dependent on the accurate segmentation of bone tissue from MR images and it is challenging task due to the complex tissue structure and inadequate image contrast/brightness. In this paper, a fully automated method for segmenting subchondral bone from knee MR images is proposed. Here, the contrast of knee MR images is enhanced using a gray-level S-curve transformation followed by automatic seed point detection using a three-dimensional multi-edge overlapping technique. Successively, bone regions are initially extracted using distance-regularized level-set evolution followed by identification and correction of leakages along the bone boundary regions using a boundary displacement technique. The performance of the developed technique is evaluated against ground truths by measuring sensitivity, specificity, dice similarity coefficient (DSC), average surface distance (AvgD) and root mean square surface distance (RMSD). An average sensitivity (91.14%), specificity (99.12%) and DSC (90.28%) with 95% confidence interval (CI) in the range 89.74-92.54%, 98.93-99.31% and 88.68-91.88% respectively is achieved for the femur bone segmentation in 8 datasets. For tibia bone, average sensitivity (90.69%), specificity (99.65%) and DSC (91.35%) with 95% CI in the range 88.59-92.79%, 99.50-99.80% and 88.68-91.88% respectively is achieved. AvgD and RMSD values for femur are 1.43 ± 0.23 (mm) and 2.10 ± 0.35 (mm) respectively while for tibia, the values are 0.95 ± 0.28 (mm) and 1.30 ± 0.42 (mm) respectively that demonstrates acceptable error between proposed method and ground truths. In conclusion, results obtained in this work demonstrate substantially significant performance with consistency and robustness that led the proposed method to be applicable for large scale and longitudinal knee OA studies in clinical settings.
    Matched MeSH terms: Magnetic Resonance Imaging/methods*
  5. Subudhi A, Acharya UR, Dash M, Jena S, Sabut S
    Comput Biol Med, 2018 12 01;103:116-129.
    PMID: 30359807 DOI: 10.1016/j.compbiomed.2018.10.016
    It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.
    Matched MeSH terms: Magnetic Resonance Imaging/methods*
  6. Gan HS, Sayuti KA, Ramlee MH, Lee YS, Wan Mahmud WMH, Abdul Karim AH
    Int J Comput Assist Radiol Surg, 2019 May;14(5):755-762.
    PMID: 30859457 DOI: 10.1007/s11548-019-01936-y
    PURPOSE: Manual segmentation is sensitive to operator bias, while semiautomatic random walks segmentation offers an intuitive approach to understand the user knowledge at the expense of large amount of user input. In this paper, we propose a novel random walks seed auto-generation (SAGE) hybrid model that is robust to interobserver error and intensive user intervention.

    METHODS: Knee image is first oversegmented to produce homogeneous superpixels. Then, a ranking model is developed to rank the superpixels according to their affinities to standard priors, wherein background superpixels would have lower ranking values. Finally, seed labels are generated on the background superpixel using Fuzzy C-Means method.

    RESULTS: SAGE has achieved better interobserver DSCs of 0.94 ± 0.029 and 0.93 ± 0.035 in healthy and OA knee segmentation, respectively. Good segmentation performance has been reported in femoral (Healthy: 0.94 ± 0.036 and OA: 0.93 ± 0.034), tibial (Healthy: 0.91 ± 0.079 and OA: 0.88 ± 0.095) and patellar (Healthy: 0.88 ± 0.10 and OA: 0.84 ± 0.094) cartilage segmentation. Besides, SAGE has demonstrated greater mean readers' time of 80 ± 19 s and 80 ± 27 s in healthy and OA knee segmentation, respectively.

    CONCLUSIONS: SAGE enhances the efficiency of segmentation process and attains satisfactory segmentation performance compared to manual and random walks segmentation. Future works should validate SAGE on progressive image data cohort using OA biomarkers.

    Matched MeSH terms: Magnetic Resonance Imaging/methods*
  7. Cheah WH
    Asia Pac J Clin Oncol, 2023 Apr;19(2):e80-e88.
    PMID: 35437926 DOI: 10.1111/ajco.13782
    Rectal cancer is common and accounts for more than one-third of colorectal tumors. It is associated with significant morbidity and mortality. Previously computed tomography scan is the key imaging modality in preoperative assessment to detect local invasion and distant metastasis. However, the advent of magnetic resonance imaging (MRI) has aided in local staging and prognosticates the outcome of rectal tumor. Here, the author briefly explains why rectal MRI has a comprehensive role and provides a simple and easy way in reporting an MRI rectal carcinoma, even for a non-radiologist.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  8. Kaplan E, Baygin M, Barua PD, Dogan S, Tuncer T, Altunisik E, et al.
    Med Eng Phys, 2023 May;115:103971.
    PMID: 37120169 DOI: 10.1016/j.medengphy.2023.103971
    PURPOSE: The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.

    MATERIALS AND METHOD: This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.

    RESULTS: We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.

    CONCLUSIONS: Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.

    Matched MeSH terms: Magnetic Resonance Imaging/methods
  9. Finsterer J
    Med J Malaysia, 2023 May;78(3):421-426.
    PMID: 37271853
    OBJECTIVES: Severe, acute, respiratory syndromecoronavirus- 2 (SARS-CoV-2) infections can be complicated by central nervous system (CNS) disease. One of the CNS disorders associated with Coronavirus Disease-19 (COVID- 19) is posterior reversible encephalopathy syndrome (PRES). This narrative review summarises and discusses previous and recent findings on SARS-CoV-2 associated PRES.

    METHODS: A literature search was carried out in PubMed and Google Scholar using suitable search terms and reference lists of articles found were searched for further articles.

    RESULTS: By the end of February 2023, 82 patients with SARS-CoV-2 associated PRES were recorded. The latency between the onset of COVID-19 and the onset of PRES ranged from 1 day to 70 days. The most common presentations of PRES were mental deterioration (n=47), seizures (n=46) and visual disturbances (n=18). Elevated blood pressure was reported on admission or during hospitalisation in 48 patients. The most common comorbidities were arterial hypertension, diabetes, hyperlipidemia and atherosclerosis. PRES was best diagnosed by multimodal cerebral magnetic resonance imaging (MRI). Complete recovery was reported in 35 patients and partial recovery in 21 patients, while seven patients died.

    CONCLUSIONS: PRES can be a CNS complication associated with COVID-19. COVID-19 patients with mental dysfunction, seizures or visual disturbances should immediately undergo CNS imaging through multimodal MRI, electroencephalography (EEG) and cerebrospinal fluid (CSF) studies in order not to miss PRES.

    Matched MeSH terms: Magnetic Resonance Imaging/methods
  10. Goh JHL, Tan TL, Aziz S, Rizuana IH
    PMID: 35055581 DOI: 10.3390/ijerph19020759
    Digital breast tomosynthesis (DBT) is a fairly recent breast imaging technique invented to overcome the challenges of overlapping breast tissue. Ultrasonography (USG) was used as a complementary tool to DBT for the purpose of this study. Nonetheless, breast magnetic resonance imaging (MRI) remains the most sensitive tool to detect breast lesion. The purpose of this study was to evaluate diagnostic performance of DBT, with and without USG, versus breast MRI in correlation to histopathological examination (HPE). This was a retrospective study in a university hospital over a duration of 24 months. Findings were acquired from a formal report and were correlated with HPE. The sensitivity of DBT with or without USG was lower than MRI. However, the accuracy, specificity and PPV were raised with the aid of USG to equivalent or better than MRI. These three modalities showed statistically significant in correlation with HPE (p < 0.005, chi-squared). Generally, DBT alone has lower sensitivity as compared to MRI. However, it is reassuring that DBT + USG could significantly improve diagnostic performance to that comparable to MRI. In conclusion, results of this study are vital to centers which do not have MRI, as complementary ultrasound can accentuate diagnostic performance of DBT.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  11. Heo HY, Tee YK, Harston G, Leigh R, Chappell MA
    NMR Biomed, 2023 Jun;36(6):e4734.
    PMID: 35322482 DOI: 10.1002/nbm.4734
    Amide proton transfer (APT) imaging, a variant of chemical exchange saturation transfer MRI, has shown promise in detecting ischemic tissue acidosis following impaired aerobic metabolism in animal models and in human stroke patients due to the sensitivity of the amide proton exchange rate to changes in pH within the physiological range. Recent studies have demonstrated the possibility of using APT-MRI to detect acidosis of the ischemic penumbra, enabling the assessment of stroke severity and risk of progression, monitoring of treatment progress, and prognostication of clinical outcome. This paper reviews current APT imaging methods actively used in ischemic stroke research and explores the clinical aspects of ischemic stroke and future applications for these methods.
    Matched MeSH terms: Magnetic Resonance Imaging/methods
  12. Zubaidah NH, Liew NC
    Med J Malaysia, 2014 Feb;69(1):44-5.
    PMID: 24814632 MyJurnal
    Spontaneous calf haematoma is a rare condition and few case reports have been published in the English literature. Common conditions like deep vein thrombosis and traumatic gastrocnemius muscle tear need to be considered when a patient presents with unilateral calf swelling and tenderness. Ultrasound and Magnetic Resonance Imaging are essential for confirmation of diagnosis. The purpose of this paper is to report on a rare case of spontaneous calf hematoma and its diagnosis and management.
    Matched MeSH terms: Magnetic Resonance Imaging
  13. Low SF, Hanafiah M, Nurismah MI, Suraya A
    BMJ Case Rep, 2013;2013.
    PMID: 24057334 DOI: 10.1136/bcr-2013-200790
    The patella is an uncommon site for all primary and metastatic bone tumours and primary intra-osseous tumours of the patella are very rare. A majority of the patella tumours are benign. We report a patient with a sudden onset swelling and pain of the right knee following a staircase fall. The plain radiograph showed an expansile multiseptated patella lesion and it was further assessed with an MRI. The radiological findings and the initial histopathological features from a limited sample were suggestive of a primary aneurysmal bone cyst. However, the final histopathological diagnosis from a more adequate specimen was a giant cell tumour with a secondary aneurysmal bone cyst.
    Matched MeSH terms: Magnetic Resonance Imaging
  14. Hamid K, Yusoff A, Rahman M, Mohamad M, Hamid A
    Biomed Imaging Interv J, 2012 Apr;8(2):e13.
    PMID: 22970069 MyJurnal DOI: 10.2349/biij.8.2.e13
    This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. MATERIALS #ENTITYSTARTX00026;
    Matched MeSH terms: Magnetic Resonance Imaging
  15. Bhugaloo A, Abdullah B, Siow Y, Ng Kh
    Biomed Imaging Interv J, 2006 Apr;2(2):e12.
    PMID: 21614224 MyJurnal DOI: 10.2349/biij.2.2.e12
    The primary objective of this study was to evaluate the specificity and sensitivity of diffusion weighted MR imaging (DWI) in the differentiation and characterisation between benign and malignant vertebral compression fractures compared with conventional T1 WI, T2 WI and fat suppressed contrast enhanced T1 WI in the Malaysian population.
    Matched MeSH terms: Diffusion Magnetic Resonance Imaging
  16. Anizaim AH, Zainuri DA, Zaini MF, Razak IA, Bakhtiar H, Arshad S
    PLoS One, 2020;15(11):e0241113.
    PMID: 33147247 DOI: 10.1371/journal.pone.0241113
    Two organometallic compounds known as (E)-1-ferrocenyl-(3-fluorophenyl)prop-2-en-1-one (Fc1) and (E)-1-ferrocenyl-(3-fluoro-4-methoxyphenyl)prop-2-en-1-one (Fc2) are designed and synthesized for application in dye-sensitized solar cell (DSSC) based on a donor-π-acceptor (D-π-A) architecture. By strategically introducing a methoxy group into the acceptor side of the compound, Fc2 which has adopted a D-π-A-AD structure are compared with the basic D-π-A structure of Fc1. Both compounds were characterized by utilizing the IR, NMR and UV-Vis methods. Target compounds were further investigated by X-ray analysis and studied computationally using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT) approaches to explore their potential performances in DSSCs. An additional methoxy group has been proven in enhancing intramolecular charge transfer (ICT) by improving the planarity of Fc2 backbone. This good electronic communication leads to higher HOMO energy level, larger dipole moment and better short-circuit current density (Jsc) values. Eventually, the presence of methoxy group in Fc2 has improved the conversion efficiency as in comparison to Fc1 under the same conditions.
    Matched MeSH terms: Magnetic Resonance Imaging
  17. Tee TY, Khoo CS, Raymond AA, Syazarina SO
    Neurology, 2019 08 06;93(6):e626-e627.
    PMID: 31383811 DOI: 10.1212/WNL.0000000000007905
    Matched MeSH terms: Magnetic Resonance Imaging
  18. Al-Shamasneh AR, Jalab HA, Palaiahnakote S, Obaidellah UH, Ibrahim RW, El-Melegy MT
    Entropy (Basel), 2018 May 05;20(5).
    PMID: 33265434 DOI: 10.3390/e20050344
    Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.
    Matched MeSH terms: Magnetic Resonance Imaging
  19. Kaur A, Ali R, Omar E, Hashim H
    J Radiol Case Rep, 2021 Jan;15(1):1-10.
    PMID: 33717402 DOI: 10.3941/jrcr.v15i1.3898
    A 46-year-old male presented with painless, recurrent bilateral ear discharge and an enlarging right temporal swelling. There were no neurological deficits. Imaging revealed an enhancing, soft tissue mass at the right infratemporal region involving the right temporalis muscle with a small, enhancing intradural component and associated hyperostosis of the greater wing of the right sphenoid bone. Tumour debulking of the right temporalis tumour was performed. Tumour invasion of the right temporalis muscle was noted intraoperatively. Histopathological result was consistent with fibrous meningioma WHO Grade 1 involving surgical resection margins. Follow-up MRI revealed residual right temporal extracranial component. Thus, plans were made for a second stage tumour debulking, however at time of writing, surgery had not been performed. This case highlights the differing appearances of the common meningioma occurring extracranially with elaboration of its differential diagnosis and management.
    Matched MeSH terms: Magnetic Resonance Imaging
  20. Zhen NB, Gaillard F, Walterfang M, Mohd Zain NR, Yoon CK
    Aust N Z J Psychiatry, 2021 02;55(2):224-225.
    PMID: 32720512 DOI: 10.1177/0004867420945786
    Matched MeSH terms: Magnetic Resonance Imaging
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