In vivo T1ρ and T2 mapping of articular cartilage in osteoarthritis of the knee using 3T MRI

SummaryObjectiveEvaluation and treatment of patients with early stages of osteoarthritis (OA) is dependent upon an accurate assessment of the cartilage lesions. However, standard cartilage dedicated magnetic resonance (MR) techniques are inconclusive in quantifying early degenerative changes. The objective of this study was to determine the ability of MR T1rho (T1ρ) and T2 mapping to detect cartilage matrix degeneration between normal and early OA patients.MethodSixteen healthy volunteers (mean age 41.3) without clinical or radiological evidence of OA and 10 patients (mean age 55.9) with OA were scanned using a 3Tesla (3T) MR scanner. Cartilage volume and thickness, and T1ρ and T2 values were compared between normal and OA patients. The relationship between T1ρ and T2 values, and Kellgren–Lawrence scores based on plain radiographs and the cartilage lesion grading based on MR images were studied.ResultsThe average T1ρ and T2 values were significantly increased in OA patients compared with controls (52.04±2.97ms vs 45.53±3.28ms with P=0.0002 for T1ρ, and 39.63±2.69ms vs 34.74±2.48ms with P=0.001 for T2). Increased T1ρ and T2 values were correlated with increased severity in radiographic and MR grading of OA. T1ρ has a larger range and higher effect size than T2, 3.7 vs 3.0.ConclusionOur results suggest that both in vivo T1ρ and T2 relaxation times increase with the degree of cartilage degeneration. T1ρ relaxation time may be a more sensitive indicator for early cartilage degeneration than T2. The ability to detect early cartilage degeneration prior to morphologic changes may allow us to critically monitor the course of OA and injury progression, and to evaluate the success of treatment to patients with early stages of OA

Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

The standard diagnostic technique for assessing osteoporosis is dual X-ray absorptiometry (DXA) measuring bone mass parameters. In this study, a combination of DXA and trabecular structure parameters (acquired by computed tomography [CT]) most accurately predicted the biomechanical strength of the proximal femur and allowed for a better prediction than DXA alone. An automated 3D segmentation algorithm was applied to determine specific structure parameters of the trabecular bone in CT images of the proximal femur. This was done to evaluate the ability of these parameters for predicting biomechanical femoral bone strength in comparison with bone mineral content (BMC) and bone mineral density (BMD) acquired by DXA as standard diagnostic technique. One hundred eighty-seven proximal femur specimens were harvested from formalin-fixed human cadavers. BMC and BMD were determined by DXA. Structure parameters of the trabecular bone (i.e., morphometry, fuzzy logic, Minkowski functionals, and the scaling index method [SIM]) were computed from CT images. Absolute femoral bone strength was assessed with a biomechanical side-impact test measuring failure load (FL). Adjusted FL parameters for appraisal of relative bone strength were calculated by dividing FL by influencing variables such as body height, weight, or femoral head diameter. The best single parameter predicting FL and adjusted FL parameters was apparent trabecular separation (morphometry) or DXA-derived BMC or BMD with correlations up to r = 0.802. In combination with DXA, structure parameters (most notably the SIM and morphometry) added in linear regression models significant information in predicting FL and all adjusted FL parameters (up to R adj = 0.872) and allowed for a significant better prediction than DXA alone. A combination of bone mass (DXA) and structure parameters of the trabecular bone (linear and nonlinear, global and local) most accurately predicted absolute and relative femoral bone strength

Heterogeneous Spatial and Strength Adaptation of the Proximal Femur to Physical Activity: A Within-Subject Controlled Cross-Sectional Study

Physical activity (PA) enhances proximal femur bone mass, as assessed using projectional imaging techniques. However, these techniques average data over large volumes obscuring spatially heterogeneous adaptations. The current study used quantitative computed tomography, statistical parameter mapping, and subject-specific finite element (FE) modeling to explore spatial adaptation of the proximal femur to PA. In particular, we were interested in adaptation occurring at the superior femoral neck and improving strength under loading from a fall onto the greater trochanter. High/long jump athletes (n=16) and baseball pitchers (n=16) were utilized as within-subject controlled models as they preferentially load their takeoff leg and leg contralateral to their throwing arm, respectively. Controls (n=15) were included, but did not show any dominant-to-nondominant (D-to-ND) leg differences. Jumping athletes showed some D-to-ND leg differences, but less than pitchers. Pitchers had 5.8% (95% CI, 3.9–7.6%) D-to-ND leg differences in total hip volumetric bone mineral density (vBMD), with increased vBMD in the cortical compartment of the femoral neck, and trochanteric cortical and trabecular compartments. Voxel-based morphometry analyses and cortical bone mapping showed pitchers had D-to-ND leg differences within the regions of the primary compressive trabeculae, inferior femoral neck, and greater trochanter, but not the superior femoral neck. FE modeling revealed pitchers had 4.1% (95%CI, 1.4–6.7%) D-to-ND leg differences in ultimate strength under single-leg stance loading, but no differences in ultimate strength to a fall onto the greater trochanter. These data indicate the asymmetrical loading associated with baseball induces proximal femur adaptation in regions associated with weight bearing and muscle contractile forces, and increases strength under single-leg stance loading. However, there were no benefits evident at the superior femoral neck and no measurable improvement in ultimate strength to common injurious loading during aging (i.e. fall onto the greater trochanter) raising questions as to how to better target these variables with PA

Spinal cord grey matter segmentation challenge

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication

A coarse-to-fine approach to prostate boundary segmentation in ultrasound images

BACKGROUND: In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented. METHODS: A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation. RESULTS: A total average similarity of 98.76%(± 0.68) with gold standards was achieved. CONCLUSION: The proposed approach represents a robust and accurate approach to prostate segmentation