Validation of Cartilage Thickness Calculations Using Indentation Analysis

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Matthew F. Koff ◽  
Le Roy Chong ◽  
Patrick Virtue ◽  
Dan Chen ◽  
Xioanan Wang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Measurement Techniques ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Mr Images ◽  
Data Set ◽  
Data Points ◽  
Indentation Analysis ◽  
Thickness Measurements

Different methods have been used to cross-validate cartilage thickness measurements from magnetic resonance images (MRIs); however, a majority of these methods rely on interpolated data points, regional mean and/or maximal thickness, or surface mean thickness for data analysis. Furthermore, the accuracy of MRI cartilage thickness measurements from commercially available software packages has not necessarily been validated and may lead to an under- or overestimation of cartilage thickness. The goal of this study was to perform a matching point-to-point validation of indirect cartilage thickness calculations using a magnetic resonance (MR) image data set with direct cartilage thickness measurements using biomechanical indentation testing at the same anatomical locations. Seven bovine distal femoral condyles were prepared and a novel phantom filled with dilute gadolinium solution was rigidly attached to each specimen. High resolution MR images were acquired, and thickness indentation analysis of the cartilage was performed immediately after scanning. Segmentation of the MR data and cartilage thickness calculation was performed using semi-automated software. Registration of MR and indentation data was performed using the fluid filled phantom. The inter- and intra-examiner differences of the measurements were also determined. A total of 105 paired MRI-indentation thickness data points were analyzed, and a significant correlation between them was found (r=0.88, p<0.0001). The mean difference (±std. dev.) between measurement techniques was 0.00±0.23 mm, with Bland–Altman limits of agreement of 0.45 mm and −0.46 mm. The intra- and inter-examiner measurement differences were 0.03±0.22 mm and 0.05±0.24 mm, respectively. This study validated cartilage thickness measurements from MR images with thickness measurements from indentation by using a novel phantom to register the image-based and laboratory-based data sets. The accuracy of the measurements was comparable to previous cartilage thickness validation studies in literature. The results of this study will aid in validating a tool for clinical evaluation of in-vivo cartilage thickness.

Visualization of Cartilage from Knee Joint Magnetic Resonance Images and Quantitative Assessment to Study the Effect of Age, Gender and Body Mass Index (BMI) in Progressive Osteoarthritis (OA)

2019 ◽  
Vol 15 (6) ◽  
pp. 565-572
Author(s):  
Mallikarjunaswamy Shivagangadharaiah Matada ◽  
Mallikarjun Sayabanna Holi ◽  
Rajesh Raman ◽  
Sujana Theja Jayaramu Suvarna
Keyword(s):  
Body Mass Index ◽  
Magnetic Resonance ◽  
Knee Joint ◽  
Body Mass ◽  
3D Visualization ◽  
Cartilage Degradation ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Knee Joints ◽  
Data Set

Background: Osteoarthritis (OA) is a degenerative disease of joint cartilage affecting the elderly people around the world. Visualization and quantification of cartilage is very much essential for the assessment of OA and rehabilitation of the affected people. Magnetic Resonance Imaging (MRI) is the most widely used imaging modality in the treatment of knee joint diseases. But there are many challenges in proper visualization and quantification of articular cartilage using MRI. Volume rendering and 3D visualization can provide an overview of anatomy and disease condition of knee joint. In this work, cartilage is segmented from knee joint MRI, visualized in 3D using Volume of Interest (VOI) approach. Methods: Visualization of cartilage helps in the assessment of cartilage degradation in diseased knee joints. Cartilage thickness and volume were quantified using image processing techniques in OA affected knee joints. Statistical analysis is carried out on processed data set consisting of 110 of knee joints which include male (56) and female (54) of normal (22) and different stages of OA (88). The differences in thickness and volume of cartilage were observed in cartilage in groups based on age, gender and BMI in normal and progressive OA knee joints. Results: The results show that size and volume of cartilage are found to be significantly low in OA as compared to normal knee joints. The cartilage thickness and volume is significantly low for people with age 50 years and above and Body Mass Index (BMI) equal and greater than 25. Cartilage volume correlates with the progression of the disease and can be used for the evaluation of the response to therapies. Conclusion: The developed methods can be used as helping tool in the assessment of cartilage degradation in OA affected knee joint patients and treatment planning.

scholarly journals Patterns of Femoral Cartilage Thickness are Different in Asymptomatic and Osteoarthritic Knees and Can be Used to Detect Disease-Related Differences Between Samples

2013 ◽  
Vol 135 (10) ◽  
Author(s):  
Julien Favre ◽  
Sean F. Scanlan ◽  
Jenifer C. Erhart-Hledik ◽  
Katerina Blazek ◽  
Thomas P. Andriacchi
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Spatial Variations ◽  
Knee Oa ◽  
Femoral Cartilage ◽  
Femoral Cartilage Thickness ◽  
Multiple Determination ◽  
Kendall Correlation ◽  
Thickness Measurements

Measures of mean cartilage thickness over predefined regions in the femoral plate using magnetic resonance imaging have provided important insights into the characteristics of knee osteoarthritis (OA), however, this quantification method suffers from the limited ability to detect OA-related differences between knees and loses potentially important information regarding spatial variations in cartilage thickness. The objectives of this study were to develop a new method for analyzing patterns of femoral cartilage thickness and to test the following hypotheses: (1) asymptomatic knees have similar thickness patterns, (2) thickness patterns differ with knee OA, and (3) thickness patterns are more sensitive than mean thicknesses to differences between OA conditions. Bi-orthogonal thickness patterns were extracted from thickness maps of segmented magnetic resonance images in the medial, lateral, and trochlea compartments. Fifty asymptomatic knees were used to develop the method and establish reference asymptomatic patterns. Another subgroup of 20 asymptomatic knees and three subgroups of 20 OA knees each with a Kellgren/Lawrence grade (KLG) of 1, 2, and 3, respectively, were selected for hypotheses testing. The thickness patterns were similar between asymptomatic knees (coefficient of multiple determination between 0.8 and 0.9). The thickness pattern alterations, i.e., the differences between the thickness patterns of an individual knee and reference asymptomatic thickness patterns, increased with increasing OA severity (Kendall correlation between 0.23 and 0.47) and KLG 2 and 3 knees had significantly larger thickness pattern alterations than asymptomatic knees in the three compartments. On average, the number of significant differences detected between the four subgroups was 4.5 times greater with thickness pattern alterations than mean thicknesses. The increase was particularly marked in the medial compartment, where the number of significant differences between subgroups was 10 times greater with thickness pattern alterations than mean thickness measurements. Asymptomatic knees had characteristic regional thickness patterns and these patterns were different in medial OA knees. Assessing the thickness patterns, which account for the spatial variations in cartilage thickness and capture both cartilage thinning and swelling, could enhance the capacity to detect OA-related differences between knees.

scholarly journals Hippocampal layers on high resolution magnetic resonance images: real or imaginary?

1999 ◽  
Vol 195 (1) ◽  
pp. 131-135
Author(s):  
U. C. WIESHMANN ◽  
M. R. SYMMS ◽  
J. P. MOTTERSHEAD ◽  
D. G. MACMANUS ◽  
G. J. BARKER ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Granule Cell ◽  
Cell Layer ◽  
Magnetic Resonance Images ◽  
Granule Cell Layer ◽  
Stratum Radiatum ◽  
7 Tesla ◽  
Mr Images ◽  
Dark Line

To evaluate whether the lines occasionally detected on clinical magnetic resonance (MR) images are genuine hippocampal layers, a formalin fixed hippocampal specimen was scanned using T2 weighted sequences at 7 Tesla (voxel dimensions 0.064×0.064×1 mm) and at 1.5 Tesla (voxel dimensions: 0.156×0.156×1 mm) and compared with the results of histological examination. In addition, a healthy volunteer was scanned with a T2 weighted sequence at 1.5 Tesla (voxel dimensions: 0.469×0.469×2 mm). On 7 Tesla images hippocampal layers and the granule cell layer of the dentate were visible. On 1.5 Tesla images of the specimen, the hippocampal layers were again identified, but the granule cell layer of the dentate was not detectable. On 1.5 Tesla images of the hippocampus in vivo, 3 layers could be distinguished in the hippocampus on some slices. These mainly represented the alveus, pyramidal cell layer and stratum radiatum. A dark line consisting of a few pixels possibly represented the dentate gyrus. Our results show that the lines occasionally detected on clinical MR images are likely to be real hippocampal layers. However, the resolution currently used in clinical imaging (typically 0.469×0.469×2 mm or lower) is not sufficient for the detection of all hippocampal layers. For the reliable detection of all hippocampal layers on MR images an increase by a factor of approximately 20 would be necessary.

scholarly journals Accuracy of 3D Cartilage Models Generated From MR Images Is Dependent on Cartilage Thickness: Laser Scanner Based Validation of In Vivo Cartilage

2009 ◽  
Vol 131 (12) ◽  
Author(s):  
Seungbum Koo ◽  
Nicholas J. Giori ◽  
Garry E. Gold ◽  
Chris O. Dyrby ◽  
Thomas P. Andriacchi
Keyword(s):  
Laser Scanning ◽  
Shape Matching ◽  
Three Dimensional ◽  
3D Models ◽  
Cartilage Thickness ◽  
Mr Images ◽  
Mr Image ◽  
Scanning Method ◽  
Thickness Measurements

Cartilage morphology change is an important biomarker for the progression of osteoarthritis. The purpose of this study was to assess the accuracy of in vivo cartilage thickness measurements from MR image-based 3D cartilage models using a laser scanning method and to test if the accuracy changes with cartilage thickness. Three-dimensional tibial cartilage models were created from MR images (in-plane resolution of 0.55 mm and thickness of 1.5 mm) of osteoarthritic knees of ten patients prior to total knee replacement surgery using a semi-automated B-spline segmentation algorithm. Following surgery, the resected tibial plateaus were laser scanned and made into 3D models. The MR image and laser-scan based models were registered to each other using a shape matching technique. The thicknesses were compared point wise for the overall surface. The linear mixed-effects model was used for statistical test. On average, taking account of individual variations, the thickness measurements in MRI were overestimated in thinner (<2.5 mm) regions. The cartilage thicker than 2.5 mm was accurately predicted in MRI, though the thick cartilage in the central regions was underestimated. The accuracy of thickness measurements in the MRI-derived cartilage models systemically varied according to native cartilage thickness.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Does the Modified Arrhenius Model Reliably Predict Area of Tissue Ablation After Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Pediatric Lesional Epilepsy?

2021 ◽  
Author(s):  
Kelsey D Cobourn ◽  
Imazul Qadir ◽  
Islam Fayed ◽  
Hepzibha Alexander ◽  
Chima O Oluigbo
Keyword(s):  
Magnetic Resonance ◽  
Linear Regression ◽  
Magnetic Resonance Images ◽  
Thermal Therapy ◽  
Invasive Technique ◽  
Hypothalamic Hamartoma ◽  
Arrhenius Model ◽  
Laser Interstitial Thermal Therapy ◽  
Accuracy And Precision

Abstract BACKGROUND Commercial magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) systems utilize a generalized Arrhenius model to estimate the area of tissue damage based on the power and time of ablation. However, the reliability of these estimates in Vivo remains unclear. OBJECTIVE To determine the accuracy and precision of the thermal damage estimate (TDE) calculated by commercially available MRgLITT systems using the generalized Arrhenius model. METHODS A single-center retrospective review of pediatric patients undergoing MRgLITT for lesional epilepsy was performed. The area of each lesion was measured on both TDE and intraoperative postablation, postcontrast T1 magnetic resonance images using ImageJ. Lesions requiring multiple ablations were excluded. The strength of the correlation between TDE and postlesioning measurements was assessed via linear regression. RESULTS A total of 32 lesions were identified in 19 patients. After exclusion, 13 pairs were available for analysis. Linear regression demonstrated a strong correlation between estimated and actual ablation areas (R2 = .97, P &lt; .00001). The TDE underestimated the area of ablation by an average of 3.92% overall (standard error (SE) = 4.57%), but this varied depending on the type of pathologic tissue involved. TDE accuracy and precision were highest in tubers (n = 3), with average underestimation of 2.33% (SE = 0.33%). TDE underestimated the lesioning of the single hypothalamic hamartoma in our series by 52%. In periventricular nodular heterotopias, TDE overestimated ablation areas by an average of 13% (n = 2). CONCLUSION TDE reliability is variably consistent across tissue types, particularly in smaller or periventricular lesions. Further investigation is needed to understand the accuracy of this emerging minimally invasive technique.

scholarly journals Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
A. Vargas-Olivares ◽  
O. Navarro-Hinojosa ◽  
M. Maqueo-Vicencio ◽  
L. Curiel ◽  
M. Alencastre-Miranda ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Segmentation Algorithm ◽  
Focal Region ◽  
Therapy Planning ◽  
Mr Images ◽  
Monitoring Method ◽  
Data Set

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

Reducing susceptibility artefacts in magnetic resonance images of the canine stifle following surgery for cranial cruciate ligament deficiency

2012 ◽  
Vol 25 (06) ◽  
pp. 488-497 ◽  
Author(s):  
J. Grierson ◽  
C. R. Lamb ◽  
F. H. David
Keyword(s):  
Magnetic Resonance ◽  
Tibial Plateau ◽  
Cruciate Ligament ◽  
Magnetic Resonance Images ◽  
Proton Density ◽  
Mr Images ◽  
Cranial Cruciate Ligament ◽  
Metallic Implants ◽  
Image Ranking ◽  
Canine Stifle

SummaryBackground: Magnetic resonance (MR) images of the postoperative canine stifle are adversely affected by susceptibility artefacts associated with metallic implants.Objectives: To determine empirically to what extent susceptibility artefacts could be reduced by modifications to MR technique.Methods: Three cadaveric limbs with a tibial plateau levelling osteotomy (TPLO), tibial tuberosity advancement (TTA), or extra-capsular stabilization (ECS) implant, respectively, were imaged at 1.5T. Series of proton density and T2-weighted images were acquired with different combinations of frequency-encoding gradient (FEG) direction and polarity, stifle flexion or extension, echo spacing (ES), and readout bandwidth (ROBW), and ranked. The highest rank (a rank of 1) corresponded to the smallest artefact.Results: Image ranking was affected by FEG polarity (p = 0.005), stifle flexion (p = 0.01), and ROBW (p = 0.0001). For TPLO and TTA implants, the highest ranked images were obtained with the stifle flexed, lateromedial FEG, and medial polarity for dorsal images, and craniocaudal FEG and caudal polarity for sagittal images. For the ECS implant, the highest ranked images were obtained with the stifle extended, a proximodistal FEG and proximal polarity for dorsal images, and craniocaudal FEG and cranial polarity for sagittal images.Clinical significance: Susceptibility artefacts in MR images of postoperative canine stifles do not preclude clinical evaluation of joints with ECS or TTA implants.Part of this study was presented at the Annual Meeting of the American College of Veterinary Radiology, Albuquerque, NM, October 2011.

scholarly journals Interobserver variability in quality assessment of magnetic resonance images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rafal Obuchowicz ◽  
Mariusz Oszust ◽  
Adam Piorkowski
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Interobserver Variability ◽  
Poor Performance ◽  
Magnetic Resonance Images ◽  
Perceptual Quality ◽  
Mr Images

Abstract Background The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. Methods For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss’ kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. Results The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. Conclusions The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

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