Machine learning-augmented and microspectroscopy-informed multiparametric MRI for the non-invasive prediction of articular cartilage composition.

BACKGROUND: Articular cartilage degeneration is the hallmark change of osteoarthritis, a severely disabling disease with high prevalence and considerable socioeconomic and individual burden. Early, potentially reversible cartilage degeneration is characterized by distinct changes in cartilage composition and ultrastructure, while the tissue's morphology remains largely unaltered. Hence, early degenerative changes may not be diagnosed by clinical standard diagnostic tools. METHODS: Against this background, this study introduces a novel method to determine the tissue composition non-invasively. Our method involves quantitative MRI parameters (i.e., T1, T1ρ, T2 and [Formula: see text] maps), compositional reference measurements (i.e., microspectroscopically determined local proteoglycan [PG] and collagen [CO] contents) and machine learning techniques (i.e., artificial neural networks [ANNs] and multivariate linear models [MLMs]) on 17 histologically grossly intact human cartilage samples. RESULTS: Accuracy and precision were higher in ANN-based predictions than in MLM-based predictions and moderate-to-strong correlations were found between measured and predicted compositional parameters. CONCLUSION: Once trained for the clinical setting, advanced machine learning techniques, in particular ANNs, may be used to non-invasively determine compositional features of cartilage based on quantitative MRI parameters with potential implications for the diagnosis of (early) degeneration and for the monitoring of therapeutic outcomes.

Correlation Between Sarcopenia and Growth Rate of the Future Liver Remnant After Portal Vein Embolization in Patients with Colorectal Liver Metastases.

PURPOSE: To investigate whether sarcopenia and myosteatosis correlate with the degree of hypertrophy (DH) and kinetic growth rate (KiGR) of the future liver remnant (FLR) in patients with colorectal liver metastases undergoing portal vein embolization (PVE) in preparation for right hepatectomy. MATERIALS AND METHODS: Forty-two patients were included. Total liver volume and FLR volume were measured before and 2-4 weeks after PVE. KiGR of the FLR was calculated. Sarcopenia was assessed using the total psoas muscle volume (PMV), the psoas muscle cross-sectional area (PMCS) and the total skeletal muscle index (L3SMI) at the level of 3rd lumbar vertebra. Degree of myosteatosis was assessed by mean muscle attenuation at L3 (L3MA). Correlations between muscle indices and DH and KiGR were assessed using simple linear regression analyses. RESULTS: Mean DH was 8.9 ± 5.7%, and mean KiGR was 3.6 ± 2.3. Mean PMV was 55.56 ± 14.19 cm3/m3, mean PMCS was 8.76 ± 2.3 cm2/m2, mean L3SMI was 45.6 ± 9.89 cm2/m2, and mean L3MA was 27.9 ± 18.6 HU. There was a strong positive correlation between PMV and DH (R = 0.503, p = 0.001) and PMV and KiGR (R = 0.545, p < 0.001). Furthermore, there was a moderate correlation between PMCS and KiGR (R = 0.389, p = 0.014). L3SMI and L3MA were neither associated with DH (p = 0.390 and p = 0.768, respectively) nor with KiGR (p = 0.188 and p = 0.929, respectively). CONCLUSION: We identified a positive correlation between PMV and PMCS, as markers for sarcopenia, and the KiGR of the FLR after PVE. PMV and PMCS might therefore aid to identify patients who are poor candidates for FLR augmentation using PVE alone.

Human articular cartilage mechanosensitivity is related to histological degeneration - a functional MRI study.

OBJECTIVE: To investigate changes in response to sequential pressure-controlled loading and unloading in human articular cartilage of variable histological degeneration using serial T1ρ mapping. METHOD: We obtained 42 cartilage samples of variable degeneration from the medial femoral condyles of 42 patients undergoing total knee replacement. Samples were placed in a standardized artificial knee joint within an MRI-compatible whole knee-joint compressive loading device and imaged before (δ0), during (δld1, δld2, δld3, δld4, δld5) and after (δrl1, δrl2, δrl3, δrl4, δrl5) pressure-controlled loading to 0.663 ± 0.021 kN (94% body weight) using serial T1ρ mapping (spin-lock multigradient echo sequence; 3.0T MRI system [Achieva, Philips]). Reference assessment included histology (Mankin scoring) and conventional biomechanics (Tangent stiffness). We dichotomized sample into intact (n = 21) and degenerative (n = 21) based on histology and analyzed data using Mann Whitney, Kruskal Wallis, one-way ANOVA tests and Spearman's correlation, respectively. RESULTS: At δ0, we found no significant differences between intact and degenerative samples, while the response-to-loading patterns were distinctly different. In intact samples, T1ρ increases were consistent and non-significant, while in degenerative samples, T1ρ increases were significantly higher (P = 0.004, δ0 vs δld1, δ0 vs δld3), yet undulating and variable. With unloading, T1ρ increases subsided, yet were persistently elevated beyond δ0. CONCLUSION: Cartilage mechanosensitivity is related to histological degeneration and assessable by serial T1ρ mapping. Unloaded, T1ρ characteristics are not significantly different in intact vs degenerative cartilage, while load bearing is organized in intact cartilage and disorganized in degenerative cartilage.

Multiparametric MRI and Computational Modelling in the Assessment of Human Articular Cartilage Properties: A Comprehensive Approach.

Quantitative magnetic resonance imaging (qMRI) is a promising approach to detect early cartilage degeneration. However, there is no consensus on which cartilage component contributes to the tissue's qMRI signal properties. T1, T1ρ, and T2⁎ maps of cartilage samples (n = 8) were generated on a clinical 3.0-T MRI system. All samples underwent histological assessment to ensure structural integrity. For cross-referencing, a discretized numerical model capturing distinct compositional and structural tissue properties, that is, fluid fraction (FF), proteoglycan (PG) and collagen (CO) content and collagen fiber orientation (CFO), was implemented. In a pixel-wise and region-specific manner (central versus peripheral region), qMRI parameter values and modelled tissue parameters were correlated and quantified in terms of Spearman's correlation coefficient ρs. Significant correlations were found between modelled compositional parameters and T1 and T2⁎, in particular in the central region (T1: ρs ≥ 0.7 [FF, CFO], ρs ≤ -0.8 [CO, PG]; T2⁎: ρs ≥ 0.67 [FF, CFO], ρs ≤ -0.71 [CO, PG]). For T1ρ, correlations were considerably weaker and fewer (0.16 ≤ ρs ≤ -0.15). QMRI parameters are characterized in their biophysical properties and their sensitivity and specificity profiles in a basic scientific context. Although none of these is specific towards any particular cartilage constituent, T1 and T2⁎ reflect actual tissue compositional features more closely than T1ρ.

Non-invasive T1ρ mapping of the human cartilage response to loading and unloading.

OBJECTIVE: To define the physiological response to sequential loading and unloading in histologically intact human articular cartilage using serial T1ρ mapping, as T1ρ is considered to indicate the tissue's macromolecular content. METHOD: 18 macroscopically intact cartilage-bone samples were obtained from the central lateral femoral condyles of 18 patients undergoing total knee replacement. Serial T1ρ mapping was performed on a clinical 3.0-T MRI system using a modified prostate coil. Spin-lock multiple gradient-echo sequences prior to, during and after standardized indentation loading (displacement controlled, strain 20%) were used to obtain seven serial T1ρ maps: unloaded (δ0), quasi-statically loaded (indentation1-indentation3) and under subsequent relaxation (relaxation1-relaxation3). After manual segmentation, zonal and regional regions-of-interest were defined. ROI-specific relative changes were calculated and statistically assessed using paired t-tests. Histological (Mankin classification) and biomechanical (unconfined compression) evaluations served as references. RESULTS: All samples were histologically and biomechanically grossly intact (Mankin sum: 1.8 ± 1.2; Young's Modulus: 0.7 ± 0.4 MPa). Upon loading, T1ρ consistently increased throughout the entire sample thickness, primarily subpistonally (indentation1 [M ± SD]: 9.5 ± 7.8% [sub-pistonal area, SPA] vs 4.2 ± 5.8% [peri-pistonal area, PPA]; P < 0.001). T1ρ further increased with ongoing loading (indentation3: 14.1 ± 8.1 [SPA] vs 7.7 ± 5.9% [PPA]; P < 0.001). Even upon unloading (i.e., relaxation), T1ρ persistently increased in time. CONCLUSION: Serial T1ρ-mapping reveals distinct and complex zonal and regional changes in articular cartilage as a function of loading and unloading. Thereby, longitudinal adaptive processes in hyaline cartilage become evident, which may be used for the tissue's non-invasive functional characterization by T1ρ.

Optimized RF shielding techniques for simultaneous PET/MR.

PURPOSE: Integration of positron emission tomography (PET) and magnetic resonance (MR) in a merged device requires shielding of the PET detector electronics. The authors demonstrate that a multiple shell setup exhibits advantages over a single shell shielding enclosure. METHODS: A spherical shell model is used to derive analytical results. Numerical computations of the electromagnetic fields with a commercial software package (FEKO) and experiments are used to confirm our findings. RESULTS: Replacing a single shell enclosure by a multilayer approach barely changes its behavior toward low frequency magnetic fields (MR gradient fields), while improving shielding at high frequencies (proton resonant frequency) by orders of magnitude. CONCLUSIONS: For a given required shielding factor at the proton resonant frequency, the eddy currents induced by the MR gradient fields may be reduced by employing a multiple shell setup.