Marco Barbieri

Muscle strength declines with aging at a faster rate compared with muscle mass, suggesting that not only muscle quantity but also muscle quality and architecture are age‐dependent. This study tested the hypothesis that quantitative MRI (qMRI)‐derived biomarkers of muscle quality (fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], fat fraction [FF], and T₂ relaxation time) and architecture (fascicle length) could improve the prediction of skeletal muscle strength over muscle mass alone. We recruited 24 adults (12 female, age range 30–79 years). Muscle mass was estimated as the volume and cross‐sectional area (CSA) of the quadriceps. FA, RD, and AD parameters, together with fascicle length for the rectus femoris (RF) and vastus lateralis (VL), were derived from diffusion tensor imaging (DTI), and muscle‐T₂ was calculated from a multi‐echo spin echo sequence. FF was determined using the Dixon approach. CSA values were combined with FF to calculate the lean CSA. Isometric, eccentric, and concentric knee extension torques were measured for the left and right leg using an isokinetic dynamometer. The univariable assessment of torque was performed using a linear regression. The statistical significance of adding qMRI parameters to the torque prediction models was tested using a mixed‐effect regression. The best univariable predictor of isometric, eccentric, and concentric torque was lean CSA. Adding FA, RF fascicle length, and VL fascicle length to the model improved the prediction of concentric torque compared with CSA alone. The addition of FA, T₂, RD, RF fascicle length, and VL fascicle length improved the prediction of eccentric torque over CSA alone. The addition of FF was not significant within the model. Our results confirmed the hypothesis that the inclusion of qMRI parameters of muscle composition and architecture leads to higher R² coefficients for the prediction of muscle strength compared with models solely based on muscle quantity. These observations support the utility of qMRI for future research on sarcopenia prediction and management.

Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. Neural Networks (NNs) accelerate reconstruction but require significant resources for training. We propose an FPGA-based NN for real-time brain parameter reconstruction from MRF data. Training the NN takes an estimated 200 seconds, significantly faster than standard CPU-based training, which can be up to 250 times slower. This method could enable real-time brain analysis on mobile devices, revolutionizing clinical decision-making and telemedicine.

Background: Post‐traumatic osteoarthritis (PTOA) often follows anterior cruciate ligament reconstruction (ACLR), leading to early cartilage degradation. Change in mean T₂ fails to capture subject‐specific spatial–temporal variations, highlighting the need for robust quantitative methods for early PTOA detection and monitoring.Purpose/Hypothesis: Develop and apply 3D T₂ cluster analysis to ACLR and healthy knees over 2.5 years.Study Type: Longitudinal case–control study.Subjects: ACLR and contralateral knees of 15 subjects (9 male/6 female, 37.7 ± 10 years) and right knee of 15 matched controls (9 male/6 female, 37.1 ± 12 years) were scanned at 3 weeks, 3, 9, 18, and 30 months post‐ACLR.Sequence: 3 T Quantitative double echo steady state sequence.Assessment: “T₂ cluster analysis” was developed, incorporating registration and thresholding methods to identify and quantify elevated T₂ regions (T₂ clusters, T₂C) in femoral cartilage. Percentage of cartilage covered by T₂ clusters (T₂C_%), mean cluster size (T₂C_size), the number of clusters (T₂C_count), and ΔT₂Mean (change in mean femoral cartilage T₂ relative to visit 1) were computed for all knees.Statistical Tests: A linear mixed model assessed knee, time, and knee‐time interaction effects on each outcome metric (P < 0.05), with effect sizes (η_p²) describing the sensitivity of these effects to longitudinal changes.Results: T₂C_% (η_p² = 0.22), T₂C_size, (η_p² = 0.14), and T₂C_count (η_p² = 0.51) showed significant and systematic difference between knees (ACLR > contralateral > control). T₂C_% (η_p² = 0.24), T₂C_size (η_p² = 0.17), and T₂C_count (η_p² = 0.11) showed significant longitudinal change across all knees. Specifically, ACLR knees exhibited a significant increase in T₂C_% (η_p² = 0.21), T₂C_size (η_p² = 0.13), and a decrease in T₂C_count (η_p² = 0.07) with time. ΔT₂Mean showed significant difference between knees (η_p² = 0.15), increase with time (η_p² = 0.04), with no significant knee‐time interaction (η_p² = 0.00, P = 0.772 [contralateral], P = 0.482 [control]).Conclusion: T₂C metrics are more sensitive than ΔT₂Mean for longitudinal monitoring of femoral cartilage post ACLR. Our findings suggest potential merging of T₂ clusters overtime, forming larger areas of cartilage degradation in ACLR knees.Level of Evidence: 1Technical Efficacy: Stage 2

Articular cartilage (AC) is a specialized connective tissue that covers the ends of long bones and facilitates the load‐bearing of joints. It consists of chondrocytes distributed throughout an extracellular matrix and organized into three zones: superficial, middle, and deep. Nuclear magnetic resonance (NMR) techniques can be used to characterize this layered structure. In this study, devoted to a better understanding of the NMR response of this complex tissue, 20 specimens excised from femoral and tibial cartilage of bovine samples were analyzed by the low‐field single‐sided NMR‐MOUSE‐PM10. A multiparametric depth‐wise analysis was performed to characterize the laminar structure of AC and investigate the origin of the NMR dependence on depth. The depth dependence of the single parameters T₁, T₂, and D has been described in literature, but their simultaneous measurement has not been fully exploited yet, as well as the extent of their variability. A novel parameter, α, evaluated by applying a double‐quantum‐like sequence, has been measured. The significant decrease in T₁, T₂, and D from the middle to the deep zone is consistent with depth‐dependent composition and structure changes of the complex matrix of fibers confining and interacting with water. The α parameter appears to be a robust marker of the layered structure with a well‐reproducible monotonic trend across the zones. The discrimination of cartilage zones was reinforced by a multivariate principal component analysis statistical analysis. The large number of samples allowed for the identification of the smallest number of parameters or their combination able to classify samples. The first two components clustered the data according to the different zones, highlighting the sensitivity of the NMR parameters to the structural and compositional variations of AC. Using two parameters, the best result was obtained by considering T₁ and α. Single‐sided NMR devices, portable and low‐cost, provide information on NMR parameters related to tissue composition and structure.

Background: The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi‐Echo Spin‐Echo (MESE) sequences for measuring knee cartilage T₂ relaxation times. Mono‐exponential models are used in the OAI for T₂ fitting, which neglects stimulated echoes and B₁ inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset.Purpose: To assess how different fitting methods, including EPG‐based and exponential‐based approaches, affect the accuracy and reproducibility of cartilage T₂ in the OAI dataset.Study Type: Retrospective.Population: From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren–Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61 years).Field Strength/Sequence: 3‐T, two‐dimensional (2D) MESE sequence.Assessment: Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub‐regions, and co‐registered to MESE. T₂ maps were obtained using three EPG‐based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono‐exponential approaches (linear least squares, nonlinear least squares, and noise‐corrected exponential). Average T₂ values within sub‐regions were obtained. Pair‐wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's T₂ accuracy and repeatability varying signal‐to‐noise ratio (SNR) were assessed with simulations.Statistical Tests: Bland–Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at P‐value 100) compared to mono‐exponential methods (error > 7 msec). EPG‐based approaches had better reproducibility, with limits of agreement 1.5–5 msec narrower than exponential‐based methods. T₂ values from EPG methods were systematically 10–17 msec lower than those from mono‐exponential fitting.Data Conclusion: EPG modeling improved agreement and reproducibility of cartilage T₂ mapping in subjects from the OAI dataset.Evidence Level: 3Technical Efficacy: Stage 1

This work presents a deep learning approach for rapid and accurate muscle water T₂ with subject-specific fat T₂ calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T₂ data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T₂ map of muscle in clinical and research studies.

Purpose: To develop and validate a method for mapping for knee imaging using the quantitative Double‐Echo in Steady‐State (qDESS) exploiting the phase difference () between the two echoes acquired. Contrary to a two‐gradient‐echo (2‐GRE) method, depends only on the first echo time.Methods: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi‐GRE sequences. maps were calculated with the qDESS and the 2‐GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel‐wise difference maps, Bland–Altman (BA) analysis, and Lin's concordance coefficient (). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions.Results: The proposed method for measuring inhomogeneities from a qDESS acquisition provided maps that were in good agreement with those obtained using WASSR. values were 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2‐GRE method.Conclusion: The proposed method may allow B0 correction for qDESS mapping using an inherently co‐registered map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliary map by exploiting a qDESS acquisition that also provides measurements and high‐quality morphological imaging.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary‐based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the “curse of dimensionality”, which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy.In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR‐FISP, bSSFP, IR‐FISP‐B₁, and IR‐bSSFP‐B₁, the latter two designed to be more specific for parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary‐based and the NN approaches.In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR‐FISP MRF sequence exploiting both simulations and in vivo acquisitions.Results demonstrate that the NN approach outperforms the dictionary‐based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T₁, T₂, and M₀ maps that are in good agreement with those obtained with the original dictionary approach. k‐SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.