A fast TDR-inversion technique for the reconstruction of spatial soil moisture content

International audienceSpatial moisture distribution in natural soil or other material is a valuably information for many applications. Standard measurement techniques give only mean or punctual results. Therefore a new inversion algorithm has been developed to derive moisture profiles along single TDR sensor-probes. The algorithm uses the full information content of TDR reflection data measured from one or both sides of an embedded probe. The system consisting of sensor probe and surrounded soil can be interpreted as a nonuniform transmission-line. The algorithm is based on the telegraph equations for nonuniform transmission-lines and an optimization approach to reconstruct the distribution of the capacitance and effective conductance along the transmission-line with high spatial resolution. The capacitance distribution can be converted into permittivity and water content by means of a capacitance model and dielectric mixing rules. Numerical investigations have been carried out to verify the accuracy of the inversion algorithm. Single- and double-sided time-domain reflection data were used to determine the capacitance and effective conductance profiles of lossless and lossy materials. The results show that single-sided reflection data are sufficient for lossless (or low-loss) cases. In case of lossy material two independent reflection measurements are required to reconstruct a reliable capacitance profile. The inclusion of an additional effective conductivity profile leads to an improved capacitance profile. The algorithm converges very fast and yields a capacitance profile within a sufficiently short time. The additional transformation to the water content requires no significant calculation time

A fast TDR-inversion technique for the reconstruction of spatial soil moisture content

Spatial moisture distribution in natural soil or other material is a valuably information for many applications. Standard measurement techniques give only mean or punctual results. Therefore a new inversion algorithm has been developed to derive moisture profiles along single TDR sensor-probes. The algorithm uses the full information content of TDR reflection data measured from one or both sides of an embedded probe. The system consisting of sensor probe and surrounded soil can be interpreted as a nonuniform transmission-line. The algorithm is based on the telegraph equations for nonuniform transmission-lines and an optimization approach to reconstruct the distribution of the capacitance and effective conductance along the transmission-line with high spatial resolution. The capacitance distribution can be converted into permittivity and water content by means of a capacitance model and dielectric mixing rules. Numerical investigations have been carried out to verify the accuracy of the inversion algorithm. Single- and double-sided time-domain reflection data were used to determine the capacitance and effective conductance profiles of lossless and lossy materials. The results show that single-sided reflection data are sufficient for lossless (or low-loss) cases. In case of lossy material two independent reflection measurements are required to reconstruct a reliable capacitance profile. The inclusion of an additional effective conductivity profile leads to an improved capacitance profile. The algorithm converges very fast and yields a capacitance profile within a sufficiently short time. The additional transformation to the water content requires no significant calculation time

Texture Features of Proton Density Fat Fraction Maps from Chemical Shift Encoding-Based MRI Predict Paraspinal Muscle Strength

Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water–fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2–L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R2adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R2adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study

Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.

BACKGROUND Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare. METHODS A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level. RESULTS On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05). CONCLUSIONS AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients. CRITICAL RELEVANCE STATEMENT Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions

T2-Weighted Dixon Turbo Spin Echo for Accelerated Simultaneous Grading of Whole-Body Skeletal Muscle Fat Infiltration and Edema in Patients With Neuromuscular Diseases

Objective The assessment of fatty infiltration and edema in the musculature of patients with neuromuscular diseases (NMDs) typically requires the separate performance of T-1-weighted and fat-suppressed T-2-weighted sequences. T-2-weighted Dixon turbo spin echo (TSE) enables the generation of T-2-weighted fat- and water-separated images, which can be used to assess both pathologies simultaneously. The present study examines the diagnostic performance of T-2-weighted Dixon TSE compared with the standard sequences in 10 patients with NMDs and 10 healthy subjects. Methods Whole-body magnetic resonance imaging was performed including T-1-weighted Dixon fast field echo, T-2-weighted short-tau inversion recovery, and T-2-weighted Dixon TSE. Fatty infiltration and intramuscular edema were rated by 2 radiologists using visual semiquantitative rating scales. To assess intermethod and interrater agreement, weighted Cohen's coefficients were calculated. Results The ratings of fatty infiltration showed high intermethod and high interrater agreement (T-1-weighted Dixon fast field echo vs T-2-weighted Dixon TSE fat image). The evaluation of edematous changes showed high intermethod and good interrater agreement (T-2-weighted short-tau inversion recovery vs T-2-weighted Dixon TSE water image). Conclusions T-2-weighted Dixon TSE imaging is an alternative for accelerated simultaneous grading of whole-body skeletal muscle fat infiltration and edema in patients with NMDs