QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

PURPOSE: To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. METHODS: QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. RESULTS: In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. CONCLUSION: QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping of T 1 , T 2 * , proton density and the field inhomogeneity. However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner. In this work, we propose a faster version of Joint MAPLE which retains the mapping performance of the original version. Coil compression, random slice selection, parameter-specific learning rates and transfer learning are synergistically combined in the proposed framework. It speeds-up the reconstruction time up to 700 times than the original version and processes a whole-brain MEMFA dataset in 21 minutes on average, which originally requires ~260 hours for Joint MAPLE. The mapping performance of the proposed framework is ~2-fold better than the standard and the state-of-the-art evaluated reconstruction techniques on average in terms of the root mean squared error.

DeepEMC-T2 mapping: Deep learning-enabled T2 mapping based on echo modulation curve modeling.

PURPOSE: Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes. METHODS: DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T2 mapping was evaluated in seven experiments. RESULTS: Compared to the reference, DeepEMC-T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T2 mapping all enabled more accurate T2 estimation. CONCLUSIONS: DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.

Ligamentum teres lesions are associated with compositional and structural hip cartilage degenerative change: region-specific cartilage degeneration.

OBJECTIVES: To investigate the association between magnetic resonance imaging (MRI)-based ligamentum teres lesions (LTL) and structural hip degeneration. METHODS: Bilateral 3-T hip MRIs of participants (n = 93 [36 men]; mean age ( ± SD) 51 years ± 15.4) recruited from the community and the orthopedic clinic of a single medical center were included. Clinical and imaging data acquired included hip disability and osteoarthritis outcome scores, semi-quantitative scoring of hip osteoarthritis on MRI (SHOMRI) scores on fluid-sensitive sequences, and cartilage T1ρ/T2 compositional sequences. An MRI-based LTL scoring system, incorporating continuity, thickening, and signal intensity, ranging from 0 (normal) to 4 (complete tear) was constructed. Hip morphological features associated with LTL, based on functional or anatomical relationships to LT, were defined. Relationships between MRI-LT scores and SHOMRI, global/regional cartilage T1ρ/T2, and proposed morphological abnormalities and LTL were explored by mixed effects linear and logistic regression models. RESULTS: In 82 (46.1%) hips, no pain was documented; 118 (63.4%) and 68 (36.6%) hips were graded as KL-grade ≤ 1 and ≥ 2, respectively. Compared to MRI-LT score = 0 (normal), score = 4 (complete tear) revealed significantly worse subchondral bony degenerative changes for bone marrow lesions (SHOMRI-BML) and subchondral cysts (SHOMRI-sc) (p < 0.001, p = 0.015, respectively). Global acetabular T1ρ, femoral T2 were significantly increased for abnormal MRI-LT scores (p-range = 0.005-0.032). Regional analyses revealed significantly increased T1ρ/T2 in central acetabular/increased T2 in off-central femoral regions (p-range = 0.005-0.046). Pulvinar effusion-synovitis, shallow fovea, and foveal osteophytes were significantly associated with abnormal LT MRI findings (p-range = < 0.001-0.044). CONCLUSION: MRI abnormalities of LT are associated with worse SHOMRI-sc/BML scores, indicative of hip osteoarthritis and higher T1ρ and T2 that differ by region. Pulvinar effusion-synovitis and changes in femoral head morphology are associated with LTL. CLINICAL RELEVANCE STATEMENT: Abnormal ligamentum teres findings identified via MRI are associated with structural degenerative changes of the hip joint and alterations in acetabular and femoral cartilage compositions show spatial differences in relation to LTL. KEY POINTS: The clinical significance of common ligamentum teres lesions (LTL) on MRI is not well understood. LTL identified by an MRI-based scoring system is associated with worse biomarkers, indicating more advanced degenerative hip changes. Effusion-synovitis signal at pulvinar, shallow fovea capitis, and foveal osteophytes are associated with LTL on imaging.

Improving the lesion appearance on FLAIR images synthetized from quantitative MRI: a fast, hybrid approach.

OBJECTIVE: The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology. MATERIALS AND METHODS: We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer. RESULTS: All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets. DISCUSSION: The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort.

Simultaneous Multiparameter Mapping of the Liver in a Single Breath-Hold or Respiratory-Triggered Acquisition Using Multi-Inversion Spin and Gradient Echo MRI.

BACKGROUND: Quantitative parametric mapping is an increasingly important tool for noninvasive assessment of chronic liver disease. Conventional parametric mapping techniques require multiple breath-held acquisitions and provide limited anatomic coverage. PURPOSE: To investigate a multi-inversion spin and gradient echo (MI-SAGE) technique for simultaneous estimation of T1, T2, and T2* of the liver. STUDY TYPE: Prospective. SUBJECTS: Sixteen research participants, both adult and pediatric (age 17.5 ± 4.6 years, eight male), with and without known liver disease (seven asymptomatic healthy controls, two fibrotic liver disease, five steatotic liver disease, and two fibrotic and steatotic liver disease). FIELD STRENGTH/SEQUENCE: 1.5 T, single breath-hold and respiratory triggered MI-SAGE, breath-hold modified Look-Locker inversion recovery (MOLLI, T1 mapping), breath-hold gradient and spin echo (GRASE, T2 mapping), and multiple gradient echo (mGRE, T2* mapping) sequences. ASSESSMENT: Agreement between hepatic T1, T2, and T2* estimated using MI-SAGE and conventional parametric mapping sequences was evaluated. Repeatability and reproducibility of MI-SAGE were evaluated using a same-session acquisition and second-session acquisition. STATISTICAL TESTS: Bland-Altman analysis with bias assessment and limits of agreement (LOA) and intraclass correlation coefficients (ICC). RESULTS: Hepatic T1, T2, and T2* estimates obtained using the MI-SAGE technique had mean biases of 72 (LOA: -22 to 166) msec, -3 (LOA: -10 to 5) msec, and 2 (LOA: -5 to 8) msec (single breath-hold) and 36 (LOA: -43 to 120) msec, -3 (LOA: -17 to 11) msec, and 4 (LOA: -3 to 11) msec (respiratory triggered), respectively, in comparison to conventional acquisitions using MOLLI, GRASE, and mGRE. All MI-SAGE estimates had strong repeatability and reproducibility (ICC > 0.72). DATA CONCLUSION: Hepatic T1, T2, and T2* estimates obtained using an MI-SAGE technique were comparable to conventional methods, although there was a 12%/6% for breath-hold/respiratory triggered underestimation of T1 values compared to MOLLI. Both respiratory triggered and breath-hold MI-SAGE parameter maps demonstrated strong repeatability and reproducibility. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Advances in MRI of Chronic Pancreatitis.

MRI and MRCP play an essential role in diagnosing CP by imaging pancreatic parenchyma and ducts. Quantitative and semi-quantitative MR imaging offers potential advantages over conventional MR imaging, including simplicity of analysis, quantitative and population-based comparisons, and more direct interpretation of disease progression or response to drug therapy. Using parenchymal imaging techniques may provide quantitative metrics for determining the presence and severity of acinar cell loss and aid in diagnosing CP. Given that the parenchymal changes of CP precede the ductal involvement, there would be a significant benefit from developing a new MRI/MRCP based, more robust diagnostic criteria combining ductal and parenchymal findings.

ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization. This work introduces the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), a novel open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using deep learning (DL) models and enables MultiTask Learning (MTL) to perform related tasks in an integrated manner, targeting generalization in the MRI domain. METHODS: We conducted a comprehensive literature review and analyzed 12,479 GitHub repositories to assess the current landscape of AI frameworks for MRI. Subsequently, we demonstrate how ATOMMIC standardizes workflows and improves data interoperability, enabling effective benchmarking of various DL models across MRI tasks and datasets. To showcase ATOMMIC's capabilities, we evaluated twenty-five DL models on eight publicly available datasets, focusing on accelerated MRI reconstruction, segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and segmentation using MTL. RESULTS: ATOMMIC's high-performance training and testing capabilities, utilizing multiple GPUs and mixed precision support, enable efficient benchmarking of multiple models across various tasks. The framework's modular architecture implements each task through a collection of data loaders, models, loss functions, evaluation metrics, and pre-processing transformations, facilitating seamless integration of new tasks, datasets, and models. Our findings demonstrate that ATOMMIC supports MTL for multiple MRI tasks with harmonized complex-valued and real-valued data support while maintaining active development and documentation. Task-specific evaluations demonstrate that physics-based models outperform other approaches in reconstructing highly accelerated acquisitions. These high-quality reconstruction models also show superior accuracy in estimating quantitative parameter maps. Furthermore, when combining high-performing reconstruction models with robust segmentation networks through MTL, performance is improved in both tasks. CONCLUSIONS: ATOMMIC advances MRI reconstruction and analysis by leveraging MTL and ensuring consistency across tasks, models, and datasets. This comprehensive framework serves as a versatile platform for researchers to use existing AI methods and develop new approaches in medical imaging.

Dysfunction of the magnocellular subdivision of the visual thalamus in developmental dyslexia.

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus - the magnocellular LGN (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in-vivo, and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high-resolution magnetic resonance imaging (MRI) at high field strength (7 Tesla) to investigate the M-LGN in DD in-vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioral test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood-oxygen-level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e., rapid letter and number naming (RANln). The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with RANln performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood-oxygen-level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies.

Novel spin-lock time sampling strategies for improved reproducibility in quantitative T1ρ mapping.

This study aimed to optimize the sampling of spin-lock times (TSLs) in quantitative T1ρ mapping for improved reproducibility. Two new TSL sampling schemes were proposed: (i) reproducibility-guided random sampling (RRS) and (ii) reproducibility-guided optimal sampling (ROS). They were compared to the existing linear sampling (LS) and precision-guided sampling (PS) schemes for T1ρ reproducibility through numerical simulations, phantom experiments, and volunteer studies. Each study evaluated the four sampling schemes with three commonly used T1ρ preparations based on composite and balanced spin-locking. Additionally, the phantom and volunteer studies investigated the impact of B0 and B1 field inhomogeneities on T1ρ reproducibility, respectively. The reproducibility was assessed using the coefficient of variation (CoV) by repeating the T1ρ measurements eight times for phantom experiments and five times for volunteer studies. Numerical simulations resulted in lower mean CoVs for the proposed RRS (1.74%) and ROS (0.68%) compared to LS (2.93%) and PS (3.68%). In the phantom study, the mean CoVs were also lower for RRS (2.7%) and ROS (2.6%) compared to LS (4.1%) and PS (3.1%). Furthermore, the mean CoVs of the proposed RRS and ROS were statistically lower (P < 0.001) compared to existing LS and PS schemes at a B1 offset of 20%. In the volunteer study, consistently lower mean CoVs were observed in bilateral thigh muscles for RRS (9.3%) and ROS (9.2%) compared to LS (10.9%) and PS (10.2%), and the difference was more prominent at B0 offsets higher than 50 Hz. The proposed sampling schemes improve the reproducibility of quantitative T1ρ mapping by optimizing the selection of TSLs. This improvement is especially beneficial for longitudinal studies that track and monitor disease progression and treatment response.

Reducing thermal noise in high-resolution quantitative magnetic resonance imaging rotating frame relaxation mapping of the human brain at 3 T.

Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole-brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long-term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T-NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two-sample paired t-test and statistical significance was set at p less than 0.05 after Bonferroni correction for multiple comparisons. The use of T-NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high-spatial resolution for RAFF4. Moreover, T-NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T-NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high-resolution RFR mapping in scan times that are suitable for clinical settings.

Feasibility of 3D MRI fingerprinting for rapid knee cartilage T1, T2, and T1ρ mapping at 0.55T: Comparison with 3T.

Low-field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal-to-noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.55T system we performed repeatability and accuracy experiments with agar-gel phantoms. Additionally, five healthy volunteers (age 32 ± 4 years old, 2 females) were scanned at 3T and 0.55T. The MRI acquisition protocols include a stack-of-stars T1ρ-enabled MRF sequence, a VIBE sequence with variable flip angles (VFA) for T1 mapping, and fat-suppressed turbo flash (TFL) sequences for T2 and T1ρ mappings. Double-Echo steady-state (DESS) sequence was also used for cartilage segmentation. Acquisitions were performed at two different field strengths, 0.55T and 3T, with the same sequences but protocols were slightly different to accommodate differences in signal-to-noise ratio and relaxation times. Cartilage segmentation was done using five compartments. T1, T2, and T1ρ values were measured in the knee cartilage using both MRF and conventional relaxometry sequences. The MRF sequence demonstrated excellent repeatability in a test-retest experiment with model agar-gel phantoms, as demonstrated with correlation and Bland-Altman plots. Underestimation of T1 values was observed on both field strengths, with the average global difference between reference values and the MRF being 151 ms at 0.55T and 337 ms at 3T. At 0.55T, MRF measurements presented significant biases but strong correlations with the reference measurements. Although a larger error was present in T1 measurements, MRF measurements trended similarly to the conventional measurements for human subjects and model agar-gel phantoms.

Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis.

PURPOSE: To assess the potential for accelerating continuous-wave (CW) T1ρ dispersion measurement with compressed sensing approach via studying the effect that the data reduction has on the ability to detect differences between intact and degenerated articular cartilage with different spin-lock amplitudes and to assess quantitative bias due to acceleration. METHODS: Osteochondral plugs (n = 27, 4 mm diameter) from femur (n = 14) and tibia (n = 13) regions from human cadaver knee joints were obtained from commercial biobank (Science Care, USA) under Ethical permission 134/2015. MRI of specimens was performed at 9.4T with magnetization prepared radial balanced SSFP (bSSFP) readout sequence, and the CWT1ρ relaxation time maps were computed from the measured data. The relaxation time maps were evaluated in the cartilage zones for different acceleration factors. For reference, Osteoarthritis Research Society International (OARSI) grading and biomechanical measurements were performed and correlated with the MRI findings. RESULTS: Four-fold acceleration of CWT1ρ dispersion measurement by compressed sensing approach was feasible without meaningful loss in the sensitivity to osteoarthritic (OA) changes within the articular cartilage. Differences were significant between intact and OA groups in the superficial and transitional zones, and CWT1ρ correlated moderately with the reference measurements (0.3 < r < 0.7). CONCLUSION: CWT1ρ was able to differentiate between intact and OA cartilage even with four-fold acceleration. This indicates that acceleration of CWT1ρ dispersion measurement by compressed sensing approach is feasible with negligible loss in the sensitivity to osteoarthritic changes in articular cartilage.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T.

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

Association of MRI findings with paraspinal muscles fat infiltration at lower lumbar levels in patients with chronic low back pain: a multicenter prospective study.

OBJECTIVE: In chronic low back pain (CLBP), the relationship between spinal pathologies and paraspinal muscles fat infiltration remains unclear. This study aims to evaluate the relationship between MRI findings and paraspinal muscles morphology and fat infiltration in CLBP patients by quantitative MRI. METHODS: All the CLBP patients were enrolled from July 2021 to December 2022 in four medical institutions. The cross-sectional area (CSA) and proton density fat fraction (PDFF) of the multifidus (MF) and erector spinae (ES) muscles at the central level of the L4/5 and L5/S1 intervertebral discs were measured. MRI findings included degenerative lumbar spondylolisthesis (DLS), intervertebral disc degeneration (IVDD), facet arthrosis, disc bulge or herniation, and disease duration. The relationship between MRI findings and the paraspinal muscles PDFF and CSA in CLBP patients was analyzed. RESULTS: A total of 493 CLBP patients were included in the study (198 females, 295 males), with an average age of 45.68 ± 12.91 years. Our research indicates that the number of MRI findings are correlated with the paraspinal muscles PDFF at the L4/5 level, but is not significant. Moreover, the grading of IVDD is the primary factor influencing the paraspinal muscles PDFF at the L4-S1 level (BES at L4/5=1.845, P < 0.05); DLS was a significant factor affecting the PDFF of MF at the L4/5 level (B = 4.774, P < 0.05). After including age, gender, and Body Mass Index (BMI) as control variables in the multivariable regression analysis, age has a significant positive impact on the paraspinal muscles PDFF at the L4-S1 level, with the largest AUC for ES PDFF at the L4/5 level (AUC = 0.646, cut-off value = 47.5), while males have lower PDFF compared to females. BMI has a positive impact on the ES PDFF only at the L4/5 level (AUC = 0.559, cut-off value = 24.535). CONCLUSION: The degree of paraspinal muscles fat infiltration in CLBP patients is related to the cumulative or synergistic effects of multiple factors, especially at the L4/L5 level. Although age and BMI are important factors affecting the degree of paraspinal muscles PDFF in CLBP patients, their diagnostic efficacy is moderate.

Quantitative Magnetic Resonance Imaging (qMRI) of the Small Bowel in Crohn's Disease: State-of-the-Art and Future Directions.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract in which repeated episodes of acute inflammation may lead to long-term bowel damage. Cross-sectional imaging is used in conjunction with endoscopy to diagnose and monitor disease and detect complications. Magnetic resonance imaging (MRI) has demonstrable utility in evaluating inflammatory activity. However, subjective interpretation of conventional MR sequences is limited in its ability to fully phenotype the underlying histopathological processes in chronic disease. In particular, conventional MRI can be confounded by the presence of mural fibrosis and muscle hypertrophy, which can mask or sometimes mimic inflammation. Quantitative MRI (qMRI) methods provide a means to better differentiate mural inflammation from fibrosis and improve quantification of these processes. qMRI may also provide more objective measures of disease activity and enable better tailoring of treatment. Here, we review quantitative MRI methods for imaging the small bowel in CD and consider the path to their clinical translation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Advanced Myocardial MRI Tissue Characterization Combining Contrast Agent-Free T1-Rho Mapping With Fully Automated Analysis.

BACKGROUND: Myocardial T1-rho (T1ρ) mapping is a promising method for identifying and quantifying myocardial injuries without contrast agents, but its clinical use is hindered by the lack of dedicated analysis tools. PURPOSE: To explore the feasibility of clinically integrated artificial intelligence-driven analysis for efficient and automated myocardial T1ρ mapping. STUDY TYPE: Retrospective. POPULATION: Five hundred seventy-three patients divided into a training (N = 500) and a test set (N = 73) including ischemic and nonischemic cases. FIELD STRENGTH/SEQUENCE: Single-shot bSSFP T1ρ mapping sequence at 1.5 T. ASSESSMENT: The automated process included: left ventricular (LV) wall segmentation, right ventricular insertion point detection and creation of a 16-segment model for segmental T1ρ value analysis. Two radiologists (20 and 7 years of MRI experience) provided ground truth annotations. Interobserver variability and segmentation quality were assessed using the Dice coefficient with manual segmentation as reference standard. Global and segmental T1ρ values were compared. Processing times were measured. STATISTICAL TESTS: Intraclass correlation coefficients (ICCs) and Bland-Altman analysis (bias ±2SD); Paired Student's t-tests and one-way ANOVA. A P value <0.05 was considered significant. RESULTS: The automated approach significantly reduced processing time (3 seconds vs. 1 minute 51 seconds ± 22 seconds). In the test set, automated LV wall segmentation closely matched manual results (Dice 81.9% ± 9.0) and closely aligned with interobserver segmentation (Dice 82.2% ± 6.5). Excellent ICCs were achieved on a patient basis (0.94 [95% CI: 0.91 to 0.96]) with bias of -0.93 cm2 ± 6.60. There was no significant difference in global T1ρ values between manual (54.9 msec ± 4.6; 95% CI: 53.8 to 56.0 msec, range: 46.6-70.9 msec) and automated processing (55.4 msec ± 5.1; 95% CI: 54.2 to 56.6 msec; range: 46.4-75.1 msec; P = 0.099). The pipeline demonstrated a high level of agreement with manual-derived T1ρ values at the patient level (ICC = 0.85; bias +0.52 msec ± 5.18). No significant differences in myocardial T1ρ values were found between methods across the 16 segments (P = 0.75). DATA CONCLUSION: Automated myocardial T1ρ mapping shows promise for the rapid and noninvasive assessment of heart disease. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Signal drift in diffusion MRI of the brain: effects on intravoxel incoherent motion parameter estimates.

OBJECTIVES: Signal drift has been put forward as one of the fundamental confounding factors in diffusion MRI (dMRI) of the brain. This study characterizes signal drift in dMRI of the brain, evaluates correction methods, and exemplifies its impact on parameter estimation for three intravoxel incoherent motion (IVIM) protocols. MATERIALS AND METHODS: dMRI of the brain was acquired in ten healthy subjects using protocols designed to enable retrospective characterization and correction of signal drift. All scans were acquired twice for repeatability analysis. Three temporal polynomial correction methods were evaluated: (1) global, (2) voxelwise, and (3) spatiotemporal. Effects of acquisition order were simulated using estimated drift fields. RESULTS: Signal drift was around 2% per 5 min in the brain as a whole, but reached above 5% per 5 min in the frontal regions. Only correction methods taking spatially varying signal drift into account could achieve effective corrections. Altered acquisition order introduced both systematic changes and differences in repeatability in the presence of signal drift. DISCUSSION: Signal drift in dMRI of the brain was found to be spatially varying, calling for correction methods taking this into account. Without proper corrections, choice of protocol can affect dMRI parameter estimates and their repeatability.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. METHODS: We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. RESULTS AND CONCLUSIONS: Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

Evaluating age-and gender-related changes in brain volumes in normal adult using synthetic magnetic resonance imaging.

OBJECTIVE: Normal aging is associated with brain volume change, and brain segmentation can be performed within an acceptable scan time using synthetic magnetic resonance imaging (MRI). This study aimed to investigate the brain volume changes in healthy adult according to age and gender, and provide age- and gender-specific reference values using synthetic MRI. METHODS: A total of 300 healthy adults (141 males, median age 48; 159 females, median age 50) were underwent synthetic MRI on 3.0 T. Brain parenchymal volume (BPV), gray matter volume (GMV), white matter volume (WMV), myelin volume (MYV), and cerebrospinal fluid volume (CSFV) were calculated using synthetic MRI software. These volumes were normalized by intracranial volume to normalized GMV (nGMV), normalized WMV (nWMV), normalized MYV (nMYV), normalized BPV (nBPV), and normalized CSFV (nCSFV). The normalized brain volumes were plotted against age in both males and females, and a curve fitting model that best explained the age dependence of brain volume was identified. The normalized brain volumes were compared between different age and gender groups. RESULTS: The approximate curves of nGMV, nWMV, nCSFV, nBPV, and nMYV were best fitted by quadratic curves. The nBPV decreased monotonously through all ages in both males and females, while the changes of nCSFV showed the opposite trend. The nWMV and nMYV in both males and females increased gradually and then decrease with age. In early adulthood (20s), nWMV and nMYV in males were lower and peaked later than that in females (p < .005). The nGMV in both males and females decreased in the early adulthood until the 30s and then remains stable. A significant decline in nWMV, nBPV, and nMYV was noted in the 60s (Turkey test, p < .05). CONCLUSIONS: Our study provides age- and gender-specific reference values of brain volumes using synthetic MRI, which could be objective tools for discriminating brain disorders from healthy brains.