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: Magnetic Resonance Imaging/methods*
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*
Magnetic resonance angiographic evaluation of the intracranial vasculature has been predominantly carried out using conventional angiographic techniques such as time of flight and phase contrast sequences. These techniques have good spatial resolution but lack temporal resolution. Newer faster angiographic techniques have been developed to circumvent this limitation. Elliptical centric time-resolved imaging of contrast kinetics (EC-TRICKS) is one such technique which has combined the use of elliptical centric ordering of the k-space with multiphase 3D digital subtraction MR angiogram (MRA) to achieve excellent temporal resolution of the arterial and venous circulations. Its applications have been mainly in the peripheral vasculature. We report the use of this technique in a case of a high-flow, direct carotid-cavernous fistula to demonstrate its potential in intracranial MR angiography.
Medical Image Quality Assessment (IQA) plays an important role in assisting and evaluating the development of any new hardware, imaging sequences, pre-processing or post-processing algorithms. We have performed a quantitative analysis of the correlation between subjective and objective Full Reference - IQA (FR-IQA) on Magnetic Resonance (MR) images of the human brain, spine, knee and abdomen. We have created a MR image database that consists of 25 original reference images and 750 distorted images. The reference images were distorted with six types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur, DCT compression, JPEG compression and JPEG2000 compression, at various levels of distortion. Twenty eight subjects were chosen to evaluate the images resulting in a total of 21,700 human evaluations. The raw scores were then converted to Difference Mean Opinion Score (DMOS). Thirteen objective FR-IQA metrics were used to determine the validity of the subjective DMOS. The results indicate a high correlation between the subjective and objective assessment of the MR images. The Noise Quality Measurement (NQM) has the highest correlation with DMOS, where the mean Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are 0.936 and 0.938 respectively. The Universal Quality Index (UQI) has the lowest correlation with DMOS, where the mean PLCC and SROCC are 0.807 and 0.815 respectively. Student's T-test was used to find the difference in performance of FR-IQA across different types of distortion. The superior IQAs tested statistically are UQI for Rician noise images, Visual Information Fidelity (VIF) for Gaussian blur images, NQM for both DCT and JPEG compressed images, Peak Signal-to-Noise Ratio (PSNR) for JPEG2000 compressed images.
Matched MeSH terms: Magnetic Resonance Imaging/methods*; Magnetic Resonance Imaging/statistics & numerical data
Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.
Matched MeSH terms: Magnetic Resonance Imaging/methods*
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.
An effective and practical Image Quality Assessment (IQA) model is needed to assess the image quality produced from any new hardware or software in MRI. A highly competitive No Reference - IQA (NR - IQA) model called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) initially designed for natural images were modified to evaluate structural MR images. The BRISQUE model measures the image quality by using the locally normalized luminance coefficients, which were used to calculate the image features. The modified-BRISQUE model trained a new regression model using MR image features and Difference Mean Opinion Score (DMOS) from 775 MR images. Two types of benchmarks: objective and subjective assessments were used as performance evaluators for both original and modified-BRISQUE models. There was a high correlation between the modified-BRISQUE with both benchmarks, and they were higher than those for the original BRISQUE. There was a significant percentage improvement in their correlation values. The modified-BRISQUE was statistically better than the original BRISQUE. The modified-BRISQUE model can accurately measure the image quality of MR images. It is a practical NR-IQA model for MR images without using reference images.
Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.
Matched MeSH terms: Magnetic Resonance Imaging/methods*
T2 relaxation times (T2 times) are different between resting and exercised muscles and between muscles of healthy subjects and subjects with muscle pathology. However, studies specifically focusing on neck muscles are lacking. Furthermore, normative neck muscle T2 times are not well defined and methodology used to analyse T2 times in neck muscles is not robust. We analysed T2 times in key neck muscles and explored factors affecting variability between muscles. 20 healthy subjects were recruited. Two circular regions of interest (ROIs) were drawn in two mutually exclusive regions within neck muscles on T2 weighted images and values averaged. ROI measurements were performed by a co-investigator, supervised by a neuro-radiologist. For the first ten subjects, measurements were done from C1-T1. For the remaining subjects, ROIs were drawn at two pre-determined levels. Two MRIs were repeated at 31 degrees acquisition to evaluate the effect of muscle fibre orientation. ROI values were translated into T2 times. Results showed semispinalis capitis had the longest T2 times (range 46.88-51.42 ms), followed by splenius capitis (range 47.37-48.33 ms), trapezius (range 45.27-47.46 ms), levator scapulae (range 43.17-45.63 ms) and sternocleidomastoid (range 38.45-42.91 ms). T2 times did not vary along length of muscles and were unaffected by muscle fibre orientation (P > 0.05). T2 times of splenius capitis correlated significantly with age at C2/C3 and C5/C6 levels and trapezius at C7/T1 level. Gender did not influence relaxation times (P > 0.05). In conclusion, results of normative neck muscle T2 time values and factors influencing the T2 times could serve as a reference for future MR analysis of neck muscles. The methodology used may also be useful for related studies of neck muscles.