3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T

SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting

Background

Magnetic resonance fingerprinting (MRF) is a rapid imaging technique for simultaneous mapping of multiple tissue properties such as T1 and T2 relaxation times. However, conventional pattern matching reconstruction and iterative low rank reconstruction methods may not take full advantage of the spatiotemporal content of MRF data and can require significant computational resources with long reconstruction times. Deep learning reconstruction using a three-dimensional (3D) convolutional neural network (CNN)-based method may enable high-quality, rapid MRF reconstruction. Evaluation of such proposed deep learning reconstruction methods for MRF is needed to clarify whether deep learning techniques adapted from other MR image reconstruction problems will yield benefits when employed in MRF applications. The objective of this study is to design and evaluate a novel deep learning framework (SuperMRF) that directly transforms undersampled parameter-weighted 3D Cartesian MRF data into quantitative T1 and T2 maps, bypassing traditional pattern-matching in MRF.

Methods

In contrast to conventional MRF where only the temporal evolution of each voxel is used for quantification, SuperMRF exploits both two-dimensional spatial and one-dimensional temporal information with a 3D CNN for reconstruction. Controlled simulation experiments were performed using reference parameter maps from in vivo knee scans of healthy volunteers. To evaluate the robustness to noise, we trained our network using clean data and tested it on simulated noisy data. Conventional inner product-based pattern matching and state-of-the-art iterative low rank reconstruction techniques were used for comparison. The performance of all methods was evaluated with respect to structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized mean squared error (NMSE). Prospective real-world MRF scans were performed in four volunteer subjects using the trained network from simulations and cartilage and muscle T1 and T2 values were compared between conventional pattern matching, low rank reconstruction, and SuperMRF.

Results

SuperMRF estimated accurate T1 and T2 mapping in a highly accelerated scan (15× undersampling in k-space with a 20-fold reduction in the number of acquired MRF frames) with low error (NMSE of 5%) and high resemblance (SSIM of 94%) to reference quantitative maps. SuperMRF was observed to be superior to the conventional and low rank MRF reconstruction methods in terms of NMSE, SSIM, and robustness to noise. In prospective real-world data, SuperMRF provided comparable T1 and T2 maps as compared to low rank MRF. The only significantly different cartilage and muscle values in prospective data across the three reconstruction methods were those from conventional MRF T2.

Conclusions

Our results demonstrate that the proposed SuperMRF can achieve rapid, robust reconstruction with reduced frames in addition to k-space undersampling, outperforming the conventional and state-of-the-art reconstruction methods in simulation and providing comparable results to low rank reconstruction in prospective real-world subjects.

Simultaneous Bilateral T1, T2, and T1ρ Relaxation Mapping of Hip Joint With 3D-MRI Fingerprinting

BACKGROUND

Three-dimensional MR fingerprinting (3D-MRF) has been increasingly used to assess cartilage degeneration, particularly in the knee joint, by looking into multiple relaxation parameters. A comparable 3D-MRF approach can be adapted to assess cartilage degeneration for the hip joint, with changes to accommodate specific challenges of hip joint imaging.

PURPOSE

To demonstrate the feasibility and repeatability of 3D-MRF in the bilateral hip jointly we map proton density (PD), T1, T2, T1ρ, and ∆B1+ in clinically feasible scan times.

STUDY TYPE

Prospective.

SUBJECTS

Eight healthy subjects, three patients with mild osteoarthritis (OA), and one of the OA patients had femoral acetabular impingement (FAI). A National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine (NIST/ISMRM) system phantom was also used.

FIELD STRENGTH/SEQUENCE

3 T, 3D-MRF sequence for bilateral hip joint mapping. Reference sequences include Volume Interpolated Breath-hold Examination (VIBE) for T1 mapping, and magnetization-prepared fast low-angle shot (TFL) for T2 and T1ρ mapping.

ASSESSMENT

The signal-to-noise ratio (SNR), repeatability, scan time, and accuracy of T1, T2, and T1ρ maps of 3D-MRF sequence were evaluated on a NIST/ISMRM phantom and human subjects. Differences in the parametric maps between OA and healthy subjects were assessed.

STATISTICAL TESTS

Regression, Bland-Altman, Kruskal-Wallis, and Wilcoxon tests were used to assess for accuracy, repeatability, and subregional variation. The P-value <0.05 indicated statistically significant.

RESULTS

A 3D-MRF sequence sensitive to PD, T1, T2, T1ρ, and ∆B1+ within 15 minutes, achieving high SNR and low test-retest coefficient of variance (T1: 3.36%, T2: 3.99%, T1ρ: 5.93%). Mild hip OA patients, including one with mild OA and FAI, showed elevation of 29.4 ± 9% (T2) and 32.4 ± 4.4% (T1ρ) in femoral lateral compartment of the hip joint compared to healthy controls.

DATA CONCLUSION

3D-MRF may be a feasible approach for simultaneous, quantitative mapping of bilateral hip joint cartilage in healthy and mild OA patients.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

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.

Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications

Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

Biomarkers of Cartilage Composition

Magnetic resonance imaging (MRI) has significantly advanced the understanding of osteoarthritis (OA) because it enables visualization of noncalcified tissues. Cartilage is avascular and nurtured by diffusion, so it has a very low turnover and limited capabilities of repair. Consequently, prevention of structural and detection of premorphological damage is key in maintaining cartilage health. The integrity of cartilage composition and ultrastructure determines its mechanical properties but is not accessible to morphological imaging. Therefore, various techniques of compositional MRI with and without use of intravenous contrast medium have been developed. Spin-spin relaxation time (T2) and spin-lattice relaxation time constant in rotating frame (T1rho) mapping, the most studied cartilage biomarkers, were included in the recent standardization effort by the Quantitative Imaging Biomarkers Alliance (QIBA) that aims to make compositional MRI of cartilage clinically feasible and comparable. Additional techniques that are less frequently used include ultrashort echo time with T2*, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), glycosaminoglycan concentration by chemical exchange-dependent saturation transfer (gagCEST), sodium imaging, and diffusion-weighted MRI.

A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging

Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm3 resolution and bilateral lower leg maps with 1.6 mm isotropic resolution can be acquired using the proposed 7-min acquisition sequence and the 3-min-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.

Quantification of Synovial Fluid Using Magnetic Resonance Fingerprinting Multicomponent Imaging in the Articular Cartilage of the Knee

RATIONALE AND OBJECTIVES

The purpose of this study was to verify the feasibility of magnetic resonance fingerprinting (MRF)-derived synovial fluid fraction (SFF) mapping for quantifying subvoxel-sized cartilage defects.

MATERIALS AND METHODS

MRF was performed on a 3-Tesla scanner and used to derive T2 and SFF maps. An ex vivo experiment was performed using bovine bone; different numbers of holes (4, 6, 8, 10, and 12) were drilled separately on the articular surface, and SFF values were compared among the drilled areas. In a clinical study, 16 osteoarthritis patients underwent sagittal 3D fast spinecho (FSE) and MRF scanning, and knee cartilage segmentation was performed on each image. For morphologic analysis, fluid-excluded images of the SFF (FEISFF) and T2 maps (FEIT2) were generated using the cartilage segmentations, and the whole-organ magnetic resonance imaging score (WORMS) of each FEI and 3D FSE image were compared using the kappa coefficient. For quantitative analysis, intact cartilage volumes in the SFF (VSFF) and T2 maps (VT2) were calculated, and their correlations with reference to the actual cartilage volume on 3D FSE images (V3D) were evaluated.

RESULTS

In the ex vivo experiment, the SFF value increased as the number of holes increased. The kappa coefficients of the WORMS were 0.80 and 0.64 in the SFF and T2 maps, respectively, and substantial to almost perfect agreement was observed in the medial tibiofemoral joint. The V3D-VSFF and V3D-VT2 correlation coefficients differed by 0.03 or more in the medial tibiofemoral joint.

CONCLUSION

The MRF-derived SFF map can feasibly evaluate small, invisible cartilage defects and quantify cartilage volumes.

Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification

In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diagnosis takes place in clinical practice, whereas classification is a process to stratify participants with OA in clinical research. For both purposes, there is an important opportunity for imaging, especially with MRI. The needs and challenges differ for early-stage OA diagnosis versus classification. Although it fulfils the need of high sensitivity and specificity for making a correct diagnosis, implementation of MRI in clinical practice is challenged by long acquisition times and high costs. For classification in clinical research, more advanced MRI protocols can be applied, such as quantitative, contrast-enhanced, or hybrid techniques, as well as advanced image analysis methods including 3D morphometric assessments of joint tissues and artificial intelligence approaches. It is necessary to follow a step-wise and structured approach that comprises, technical validation, biological validation, clinical validation, qualification, and cost-effectiveness, before new imaging biomarkers can be implemented in clinical practice or clinical research.

Rapid Whole-Knee Quantification of Cartilage Using T1, T2*, and TRAFF2 Mapping With Magnetic Resonance Fingerprinting

OBJECTIVE

Quantitative Magnetic Resonance Imaging (MRI) holds great promise for the early detection of cartilage deterioration. Here, a Magnetic Resonance Fingerprinting (MRF) framework is proposed for comprehensive and rapid quantification of T1, T2*, and TRAFF2 with whole-knee coverage.

METHODS

A MRF framework was developed to achieve quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame of reference ( TRAFF2) along with T1 and T2*. The proposed sequence acquires 65 measurements of 25 high-resolution slices, interleaved with 7 inversion pulses and 40 RAFF2 trains, for whole-knee quantification in a total acquisition time of 3:25 min. Comparison with reference T1, T2*, and TRAFF2 methods was performed in phantom and in seven healthy subjects at 3 T. Repeatability (test-retest) with and without repositioning was also assessed.

RESULTS

Phantom measurements resulted in good agreement between MRF and the reference with mean biases of -54, 2, and 5 ms for T1, T2*, and TRAFF2, respectively. Complete characterization of the whole-knee cartilage was achieved for all subjects, and, for the femoral and tibial compartments, a good agreement between MRF and reference measurements was obtained. Across all subjects, the proposed MRF method yielded acceptable repeatability without repositioning ( R2 ≥ 0.94) and with repositioning ( R2 ≥ 0.57) for T1, T2*, and TRAFF2.

SIGNIFICANCE

The short scan time combined with the whole-knee coverage makes the proposed MRF framework a promising candidate for the early assessment of cartilage degeneration with quantitative MRI, but further research may be warranted to improve repeatability after repositioning and assess clinical value in patients.

Three-dimensional multi-parameter brain mapping using MR fingerprinting

The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.

Application of quantitative T1, T2 and T2* mapping magnetic resonance imaging in cartilage degeneration of the shoulder joint

To investigate and compare the values of 3.0 T MRI T1, T2 and T2* mapping quantification techniques in evaluating cartilage degeneration of the shoulder joint. This study included 123 shoulder joints of 119 patients, which were scanned in 3.0 T MRI with axial Fat Suppression Proton Density Weighted Image (FS-PDWI), sagittal fat suppression T2 Weighted Image (FS-T2WI), coronal T1Weighted Image (T1WI), FS-PDWI, cartilage-specific T1, T2 and T2* mapping sequences. Basing on MRI images, the shoulder cartilage was classified into grades 0 1, 2, 3 and 4 according to the International Cartilage Regeneration & Joint Preservation Society (ICRS). The grading of shoulder cartilage was based on MRI images with ICRS as reference, and did not involve arthroscopy or histology.The T1, T2 and T2* relaxation values in the superior, middle and inferior bands of shoulder articular cartilage were measured at all grades, and the differences in various indicators between groups were analyzed and compared using a single-factor ANOVA test. The correlation between T1, T2 and T2* relaxation values and MRI-based grading was analyzed by SPSS software. There were 46 shoulder joints with MRI-based grade 0 in healthy control group (n = 46), while 49 and 28 shoulder joints with grade 1-2 (mild degeneration subgroup) and grade 3-4 (severe degeneration subgroup) in patient group (n = 73), accounting for 63.6% and 36.4%, respectively. The T1, T2 and T2* relaxation values of the superior, middle and inferior bands of shoulder articular cartilage were significantly and positively correlated with the MRI-based grading (P < 0.01). MRI-basedgrading of shoulder cartilage was markedly associated with age (r = 0.766, P < 0.01). With the aggravation of cartilage degeneration, T1, T2 and T2* relaxation values showed an upward trend (all P < 0.01), and T1, T2 and T2* mapping could distinguish cartilage degeneration at all levels (all P < 0.01). The T1, T2 and T2* relaxation values were significantly different between normal group and mild degeneration subgroup, normal group and severe degeneration subgroup, mild degeneration subgroup and severe degeneration subgroup (all P < 0.05). Quantitative T1, T2 and T2* mapping can quantify the degree of shoulder cartilage degeneration. All these MRI mapping quantification techniques can be used as critical supplementary sequences to assess shoulder cartilage degeneration, among which T2 mapping has the highest value.

Latest advancements in imaging techniques in OA

The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.