MyMedR

Displaying publications 181 - 200 of 255 in total

Abstract:

Sort:

MIARS: a medical image retrieval system

Mueen A, Zainuddin R, Baba MS

J Med Syst, 2010 Oct;34(5):859-64.
PMID: 20703623 DOI: 10.1007/s10916-009-9300-y

The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.

Matched MeSH terms: Image Processing, Computer-Assisted
Image noise variance estimation using the mixed Lagrange time-delay autoregressive model

Sim KS, Tso CP, Law KK

Microsc Res Tech, 2008 Apr;71(4):315-24.
PMID: 18172898 DOI: 10.1002/jemt.20558

The mixed Lagrange time-delay estimation autoregressive (MLTDEAR) model is proposed as a solution to estimate image noise variance. The only information available to the proposed estimator is a corrupted image and the nature of additive white noise. The image autocorrelation function is calculated and used to obtain the MLTDEAR model coefficients; the relationship between the MLTDEAR and linear prediction models is utilized to estimate the model coefficients. The forward-backward prediction is then used to obtain the predictor coefficients; the MLTDEAR model coefficients and prior samples of zero-offset autocorrelation values are next used to predict the power of the noise-free image. Furthermore, the fundamental performance limit of the signal and noise estimation, as derived from the Cramer-Rao inequality, is presented.

Matched MeSH terms: Image Processing, Computer-Assisted
Mixed lagrange time delay estimation autoregressive Wiener filter application for real-time SEM image enhancement

Sim KS, Law KK, Tso CP

Microsc Res Tech, 2007 Nov;70(11):919-27.
PMID: 17661362

A new filter is developed for the enhancement of scanning electron microscope (SEM) images. A mixed Lagrange time delay estimation auto-regression (MLTDEAR)-based interpolator is used to provide an estimate of noise variance to a standard Wiener filter. A variety of images are captured and the performance of the filter is shown to surpass the conventional noise filters. As all the information required for processing is extracted from a single image, this method is not constrained by image registration requirements and thus can be applied in real-time in cases where specimen drift is presented in the SEM image.

Matched MeSH terms: Image Processing, Computer-Assisted
Enhancement of seminal stains using background correction algorithm with colour filters

Lee WC, Khoo BE, Abdullah AFL

Forensic Sci Int, 2016 06;263:1-9.
PMID: 27061146 DOI: 10.1016/j.forsciint.2016.03.046

Evidence in crime scenes available in the form of biological stains which cannot be visualized during naked eye examination can be detected by imaging their fluorescence using a combination of excitation lights and suitable filters. These combinations selectively allow the passage of fluorescence light emitted from the targeted stains. However, interference from the fluorescence generated by many of the surface materials bearing the stains often renders it difficult to visualize the stains during forensic photography. This report describes the use of background correction algorithm (BCA) to enhance the visibility of seminal stain, a biological evidence that fluoresces. While earlier reports described the use of narrow band-pass filters for other fluorescing evidences, here, we utilize BCA to enhance images captured using commonly available colour filters, yellow, orange and red. Mean-based contrast adjustment was incorporated into BCA to adjust the background brightness for achieving similarity of images' background appearance, a crucial step for ensuring success while implementing BCA. Experiment results demonstrated the effectiveness of our proposed colour filters' approach using the improved BCA in enhancing the visibility of seminal stains in varying dilutions on selected surfaces.

Matched MeSH terms: Image Processing, Computer-Assisted
ADA-SR: Activity detection and analysis using security robots for reliable workplace safety

Zhang G, Jing W, Tao H, Rahman MA, Salih SQ, Al-Saffar A, et al.

Work, 2021;68(3):935-943.
PMID: 33612535 DOI: 10.3233/WOR-203427

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Land use change using geospatial techniques in upper prek thnot watershed in Cambodia

Norin Khorn, Mohd Hasmadi Ismail, Norizah Kamarudin, Siti Nurhidayu

Pertanika Journal of Science & Technology, 2020;28(3):879-892.
MyJurnal

Monitoring of land use change is crucial for sustainable resource management and development planning. Up-to-date land use change information is important to understand its pattern and identify the drivers. Remote sensing and geographic information system (GIS) have proven as a useful tool to measure and analyze land use changes. Recent advances in remote sensing technology with digital image processing provide unprecedented possibilities for detecting changes in land use over large areas, with less costs and processing time. Thus, the objective of this study was to assess the land use changes in upper Prek Thnot watershed in Cambodia from 2006 until 2018. Geospatial tools such as remote sensing and GIS were used to process and produce land use maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8. The post-classification comparison was conducted for analysing the land use changes. Results show forest area was greatly decreased by 1,162.06 km2 (33.67%) which was converted to rubber plantation (10.55 km2 ), wood shrub (37.65 km2 ), agricultural land (1,099.71 km2 ), built-up area (17.76 km2 ), barren land (3.65 km2 ), and water body (14.69 km2 ). Agricultural land increased by 1,258.99 km2 (36.48%), while wood shrub declined by 161.88 km2 (4.69%). Rubber plantation, built-up area, barren land, and water bodies were increased by 10.55 km2 (0.31%), 33.64 km2 (0.97%), 4.87 km2 (0.14%) and 15.89 km2 (0.46%), respectively. The decrease of forest and wood shrub had resulted due to population growth (1.8% from 2008 to 2019) and land conversion for agricultural purposes. Hence, this study may provide vital information for wise sustainable watershed’s land management, especially for further study on the effect of land use change on runoff in this area.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation

Dong X, Xu S, Liu Y, Wang A, Saripan MI, Li L, et al.

Cancer Imaging, 2020 Aug 01;20(1):53.
PMID: 32738913 DOI: 10.1186/s40644-020-00331-0

BACKGROUND: Convolutional neural networks (CNNs) have been extensively applied to two-dimensional (2D) medical image segmentation, yielding excellent performance. However, their application to three-dimensional (3D) nodule segmentation remains a challenge.
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Small scale imaging using ultrasonic tomography

Zakaria, Z., Fazalul Rahiman, M.H., Abdul Rahim, R., Megat Ali, M.S.A., Baharuddin, M.Y., Jahidin, A.H.

Journal of Nuclear and Related Technologies, 2009;2009(1):266-281.
MyJurnal

Ultrasound technology progressed through the 1960’s from simple A-mode and B-mode scans to today’s M-mode and Doppler two dimensional (2-D) and even three dimensional (3-D) systems. Modern ultrasound imaging has its roots in sonar technology after it was first described by Lord John Rayleigh over 100 years ago on the interaction of acoustic waves with media. Tomography technique was developed as a diagnostic tool in the medical area since the early of 1970’s. This research initially focused on how to retrieve a cross sectional images from living or non-living things. After a decade, the application of tomography systems span into the industrial area. However, the long exposure time of medical radiation-based method cannot tolerate the dynamic changes in industrial process two phase liquid/gas flow system.. An alternative system such as a process tomography system, can give information on the nature of the flow regime characteristic. The overall aim of this paper is to investigate the use of a small scale ultrasonic tomography method based on ultrasonic transmission mode tomography for online monitoring of liquid/gas flow in pipe/vessel system through ultrasonic transceivers application. This non-invasive technique applied sixteen transceivers as the sensing elements to cover the pipe/vessel cross section. The paper also details the transceivers selection criteria, hardware setup, the electronic measurement circuit and also the image reconstruction algorithm applied. The system was found capable of visualizing the internal characteristics and provides the concentration profile for the corresponding liquid and gas phases.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Fluorescence multispectral imaging-based diagnostic system for atherosclerosis

Ho CS, Horiuchi T, Taniguchi H, Umetsu A, Hagisawa K, Iwaya K, et al.

Biomed Eng Online, 2016 Aug 20;15(1):98.
PMID: 27542354 DOI: 10.1186/s12938-016-0220-z

Composition of atherosclerotic arterial walls is rich in lipids such as cholesterol, unlike normal arterial walls. In this study, we aimed to utilize this difference to diagnose atherosclerosis via multispectral fluorescence imaging, which allows for identification of fluorescence originating from the substance in the arterial wall.

Matched MeSH terms: Image Processing, Computer-Assisted
Generalized classification modeling of activated sludge process based on microscopic image analysis

Khan MB, Nisar H, Ng CA, Lo PK, Yap VV

Environ Technol, 2018 Jan;39(1):24-34.
PMID: 28278778 DOI: 10.1080/09593330.2017.1293166

The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

Matched MeSH terms: Image Processing, Computer-Assisted
Fetal heart rate monitoring device using condenser microphone sensor: Validation and comparison to standard devices

Ahmad HAB, El-Badawy IM, Singh OP, Hisham RB, Malarvili MB

Technol Health Care, 2018;26(4):573-579.
PMID: 29758955 DOI: 10.3233/THC-171067

BACKGROUND: Fetal heart rate (FHR) monitoring device is highly demanded to assess the fetus health condition in home environments. Conventional standard devices such as ultrasonography and cardiotocography are expensive, bulky and uncomfortable and consequently not suitable for long-term monitoring. Herein, we report a device that can be used to measure fetal heart rate in clinical and home environments.
METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.
RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext LivePhantom: Retrieving Virtual World Light Data to Real Environments

Kolivand H, Billinghurst M, Sunar MS

PLoS One, 2016;11(12):e0166424.
PMID: 27930663 DOI: 10.1371/journal.pone.0166424

To achieve realistic Augmented Reality (AR), shadows play an important role in creating a 3D impression of a scene. Casting virtual shadows on real and virtual objects is one of the topics of research being conducted in this area. In this paper, we propose a new method for creating complex AR indoor scenes using real time depth detection to exert virtual shadows on virtual and real environments. A Kinect camera was used to produce a depth map for the physical scene mixing into a single real-time transparent tacit surface. Once this is created, the camera's position can be tracked from the reconstructed 3D scene. Real objects are represented by virtual object phantoms in the AR scene enabling users holding a webcam and a standard Kinect camera to capture and reconstruct environments simultaneously. The tracking capability of the algorithm is shown and the findings are assessed drawing upon qualitative and quantitative methods making comparisons with previous AR phantom generation applications. The results demonstrate the robustness of the technique for realistic indoor rendering in AR systems.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model

Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K

Sensors (Basel), 2018 Aug 25;18(9).
PMID: 30149621 DOI: 10.3390/s18092799

Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

Matched MeSH terms: Image Processing, Computer-Assisted
Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals

Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al.

J Med Syst, 2019 May 28;43(7):205.
PMID: 31139932 DOI: 10.1007/s10916-019-1345-y

Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Comparison of automatic airway analysis function of Invivo5 and Romexis software

Kamaruddin N, Daud F, Yusof A, Aziz ME, Rajion ZA

PeerJ, 2019;7:e6319.
PMID: 30697493 DOI: 10.7717/peerj.6319

Background: Visualization and calculation of the airway dimensions are important because an increase of airway resistance may lead to life-threatening emergencies. The visualization and calculation of the airway are possible using radiography technique with their advance software. The aim of this study was to compare and to test the reliability of the measurement of the upper airway volume and minimum area using airway analysis function in two software.
Methods: The sample consisted of 11 cone-beam computed tomography (CBCT) scans data, evaluated using the Invivo5 (Anatomage) and Romexis (version 3.8.2.R, Planmeca) software which afford image reconstruction, and airway analysis. The measurements were done twice with one week gap between the two measurements. The measurement obtained was analyzed with t-tests and intraclass correlation coefficient (ICC), with confidence intervals (CI) was set at 95%.
Results: From the analysis, the mean reading of volume and minimum area is not significantly different between Invivo5 and Romexis. Excellent intrarater reliability values were found for the both measurement on both software, with ICC values ranging from 0.940 to 0.998.
Discussion: The results suggested that both software can be used in further studies to investigate upper airway, thereby contributing to the diagnosis of upper airway obstructions.

Matched MeSH terms: Image Processing, Computer-Assisted
A robust background correction algorithm for forensic bloodstain imaging using mean-based contrast adjustment

Lee WC, Khoo BE, Abdullah AFL

Sci Justice, 2016 May;56(3):201-209.
PMID: 27162018 DOI: 10.1016/j.scijus.2016.01.001

Background correction algorithm (BCA) is useful in enhancing the visibility of images captured in crime scenes especially those of untreated bloodstains. Successful implementation of BCA requires all the images to have similar brightness which often proves a problem when using automatic exposure setting in a camera. This paper presents an improved background correction algorithm (BCA) that applies mean-based contrast adjustment as a pre-correction step to adjust the mean brightness of images to be similar before implementing BCA. The proposed modification, namely mean-based adaptive BCA (mABCA) was tested on various image samples captured under different illuminations such as 385 nm, 415 nm and 458 nm. We also evaluated mABCA of two wavelengths (415 nm and 458 nm) and three wavelengths (415 nm, 380 nm and 458 nm) in enhancing untreated bloodstains on different surfaces. The proposed mABCA is found to be more robust in processing images captured in different brightness and thus overcomes the main issue faced in the original BCA.

Matched MeSH terms: Image Processing, Computer-Assisted
Automated measurement for image distortion analysis in 2D panoramic imaging of dental CBCT system: A phantom study

Rabba JA, Suhaimi FM, Mat Jafri MZ, Jaafar HA, Osman ND

Radiography (Lond), 2023 May;29(3):533-538.
PMID: 36913788 DOI: 10.1016/j.radi.2023.02.028

INTRODUCTION: The daily image quality assessment involves large datasets that consume a lot of time and effort. This study aims to evaluate a proposed automated calculator for image distortion analysis in 2-dimensional (2D) panoramic imaging mode for a dental cone beam computed tomography (CBCT) system in comparison with present manual calculations.
METHODS: A ball phantom was scanned using panoramic mode of the Planmeca ProMax 3D Mid CBCT unit (Planmeca, Helsinki, Finland) with standard exposure settings used in clinical practice (60 kV, 2 mA, and maximum FOV). An automated calculator algorithm was developed in MATLAB platform. Two parameters associated with panoramic image distortion such as balls diameter and distance between middle and tenth balls were measured. These automated measurements were compared with manual measurement using the Planmeca Romexis and ImageJ software.
RESULTS: The findings showed smaller deviation in distance difference measurements by proposed automated calculator (ranged 3.83 mm) as compared to manual measurements (ranged 5.00 for Romexis and 5.12 mm for ImageJ software). There was a significant difference (p

Matched MeSH terms: Image Processing, Computer-Assisted
Fulltext Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Wirza R, Nazir S, Khan HU, García-Magariño I, Amin R

J Healthc Eng, 2020;2020:8835544.
PMID: 32963749 DOI: 10.1155/2020/8835544

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

Matched MeSH terms: Image Processing, Computer-Assisted
Redness enhances perceived aggression, dominance and attractiveness in men's faces

Stephen ID, Oldham FH, Perrett DI, Barton RA

Evol Psychol, 2012 Aug 17;10(3):562-72.
PMID: 22947678

In a range of non-human primate, bird and fish species, the intensity of red coloration in males is associated with social dominance, testosterone levels and mate selection. In humans too, skin redness is associated with health, but it is not known whether--as in non-human species--it is also associated with dominance and links to attractiveness have not been thoroughly investigated. Here we allow female participants to manipulate the CIELab a* value (red-green axis) of skin to maximize the perceived aggression, dominance and attractiveness of photographs of men's faces, and make two findings. First, participants increased a* (increasing redness) to enhance each attribute, suggesting that facial redness is perceived as conveying similar information about a male's qualities in humans as it does in non-human species. Second, there were significant differences between trial types: the highest levels of red were associated with aggression, an intermediate level with dominance, and the least with attractiveness. These differences may reflect a trade-off between the benefits of selecting a healthy, dominant partner and the negative consequences of aggression.

Matched MeSH terms: Image Processing, Computer-Assisted
Physiological assessment of in vivo human knee articular cartilage using sodium MR imaging at 1.5 T

Hani AF, Kumar D, Malik AS, Razak R

Magn Reson Imaging, 2013 Sep;31(7):1059-67.
PMID: 23731535 DOI: 10.1016/j.mri.2013.01.007

Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage.

Matched MeSH terms: Image Processing, Computer-Assisted/methods

Filters

Please provide feedback to Administrator ([email protected])

External Links