Expanding access to magnetic resonance education through open-source web tutorials.

This study presents a tool that introduces the fundamental concepts of magnetic resonance (MR) by integrating related science, technology, engineering, arts, and mathematical (STEAM) topics in the form of games to improve the access to MR education.

Repeatability of image quality in very low-field MRI.

BACKGROUND: Very low-field MR has emerged as a promising complementary device to high-field MRI scanners, offering several advantages. One of the key benefits is that very low-field scanners are generally more portable and affordable to purchase and maintain, making them an attractive option for medical facilities looking to reduce costs. Very low-field MRI systems also have lower RF power deposition, making them safer and less likely to cause tissue heating or other safety concerns. They are also simpler to maintain, as they do not require cooling agents such as liquid helium. However, these portable MR scanners are impacted by temperature, lower magnetic field strength, and inhomogeneity, resulting in images with lower signal-to-noise ratio (SNR) and higher geometric distortions. It is essential to investigate and tabulate the variations in these parameters to establish bounds so that subsequent in vivo studies and deployment of these portable systems can be well informed. PURPOSE: The aim of this work is to investigate the repeatability of image quality metrics such as SNR and geometrical distortion at 0.05 T over 10 days and three sessions per day. METHODS: We acquired repeatability data over 10 days with three sessions per day. The measurements included temperature, humidity, transmit frequency, off-resonance maps, and 3D turbo spin echo (TSE) images of an in vitro phantom. This resulted in a protocol with 11 sequences. We also acquired a 3 T data set for reference. The image quality metrics included computing SNR and eccentricity (to assess geometrical distortion) to investigate the repeatability of 0.05 T image quality. The image reconstruction included drift correction, k-space filtering, and off-resonance correction. We computed the experimental parameters' coefficient of variation (CV) and the resulting image quality metrics to assess repeatability. We have explored the impact of electromagnetic interference (EMI) on image quality in very low-field MRI. The investigation involved varying both the distance and amplitude of the EMI-producing coil from the signal generator to analyze their effects on image quality. RESULTS: The range of temperature measured during the study was within 1.5 °C. The off-resonance maps acquired before and after the 3D TSE showed similar hotspots and were changed mainly by a global constant. The SNR measurements were highly repeatable across sessions and over the 10 days, quantified by a CV of 6.7%. The magnetic field inhomogeneity effects quantified by eccentricity showed a CV of 13.7%, but less than 5.1% in two of the three sessions over 10 days. The use of conjugate phase reconstruction mitigated geometrical distortion artifacts. Temperature and humidity did not significantly affect SNR or mean frequency drift within the ranges of these environmental factors investigated. The EMI experiment showed that as the amplitude increased the SNR decreased, and concurrently the root mean square of the background increased with a rise in EMI amplitude or a reduction in distance. CONCLUSIONS: We found that humidity and temperature in the range investigated did not impact SNR or frequency. Based on the CV values computed session-wise and for the overall study, our findings indicate high repeatability for SNR and magnetic field homogeneity.

Automated detection of motion artifacts in brain MR images using deep learning.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T1-weighted brain images. We leveraged a 2D convolutional neural network (CNN) trained on motion-synthesized data for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed 93% agreement with the labeling of a radiologist a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is aimed at inline automatic detection of motion artifacts, accelerating part of the time-consuming quality assessment (QA) process and augmenting expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

Superconducting magnet designs and MRI accessibility: A review.

Presently, magnetic resonance imaging (MRI) magnets must deliver excellent magnetic field (B0 ) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity requirements but require considerable superconducting material. These designs result in large, heavy, and costly systems that aggravate as field strength increases. Furthermore, the tight temperature tolerance of niobium titanium magnets adds instability to the system and requires operation at liquid helium temperature. These issues are crucial factors in the disparity of MR density and field strength use across the globe. Low-income settings show reduced access to MRI, especially to high field strengths. This article summarizes the proposed modifications to MRI superconducting magnet design and their impact on accessibility, including compact, reduced liquid helium, and specialty systems. Reducing the amount of superconductor inevitably entails shrinking the magnet size, resulting in higher field inhomogeneity. This work also reviews the state-of-the-art imaging and reconstruction methods to overcome this issue. Finally, we summarize the current and future challenges and opportunities in the design of accessible MRI.

Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

A framework for advancing sustainable magnetic resonance imaging access in Africa.

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings.

Emerging ethical issues raised by highly portable MRI research in remote and resource-limited international settings.

Smaller, more affordable, and more portable MRI brain scanners offer exciting opportunities to address unmet research needs and long-standing health inequities in remote and resource-limited international settings. Field-based neuroimaging research in low- and middle-income countries (LMICs) can improve local capacity to conduct both structural and functional neuroscience studies, expand knowledge of brain injury and neuropsychiatric and neurodevelopmental disorders, and ultimately improve the timeliness and quality of clinical diagnosis and treatment around the globe. Facilitating MRI research in remote settings can also diversify reference databases in neuroscience, improve understanding of brain development and degeneration across the lifespan in diverse populations, and help to create reliable measurements of infant and child development. These deeper understandings can lead to new strategies for collaborating with communities to mitigate and hopefully overcome challenges that negatively impact brain development and quality of life. Despite the potential importance of research using highly portable MRI in remote and resource-limited settings, there is little analysis of the attendant ethical, legal, and social issues (ELSI). To begin addressing this gap, this paper presents findings from the first phase of an envisioned multi-staged and iterative approach for creating ethical and legal guidance in a complex global landscape. Section 1 provides a brief introduction to the emerging technology for field-based MRI research. Section 2 presents our methodology for generating plausible use cases for MRI research in remote and resource-limited settings and identifying associated ELSI issues. Section 3 analyzes core ELSI issues in designing and conducting field-based MRI research in remote, resource-limited settings and offers recommendations. We argue that a guiding principle for field-based MRI research in these contexts should be including local communities and research participants throughout the research process in order to create sustained local value. Section 4 presents a recommended path for the next phase of work that could further adapt these use cases, address ethical and legal issues, and co-develop guidance in partnership with local communities.

Low-Field MRI of Stroke: Challenges and Opportunities.

Stroke is a leading cause of death and disability worldwide. The reasons for increased stroke burden in developing countries are inadequately controlled risk factors resulting from poor public awareness and inadequate infrastructure. Computed tomography and MRI are common neuroimaging modalities used to assess stroke with diffusion-weighted MRI, in particular, being the recommended choice for acute stroke imaging. However, access to these imaging modalities is primarily restricted to major cities and high-income groups. In the case of stroke, the time-window of treatment to limit the damage is of a few hours and needs a point-of-care diagnosis. A low-cost MR system typically achieved at the ultra-low- and very-low-field would meet the need for a geographically accessible and portable solution. We review studies focused on accessible stroke imaging and recent developments in MR methodologies, including hardware, to image at low fields. We hypothesize that in the absence of a formal, rapid stroke triaging system, the value of timely on-site delivery of the scanner to the stroke patient can be significant. To this end, we discuss multiple recent hardware and methods developments in the low-field regime. Our review suggests a compelling need to explore further the trade-offs between high signal, contrast, and accessibility at low fields in low-income communities. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 6.

Accessible magnetic resonance imaging: A review.

The role of MRI in diagnostics, prognostics, and discoveries in basic sciences has been well established. However, access to this life-saving technology is largely restricted to countries in upper-middle to high-income groups. In this article, we collate recent global MR scanner density data and group them into six geographical regions based on the WHO classification. We then analyze these data with respect to demographic factors such as population size, life expectancy, the percentage of internet users, and World Bank income grouping. We map these demographic factors to five dimensions or characteristics of accessible MRI, adapting definitions from the healthcare literature. With this background, the study then reviews recent demonstrations of accessible MRI categorized based on main magnetic field strength. We describe demonstrated examples for each of these categories, ranging from ultralow-field to ultrahigh-field MRI. Lastly, we review MR methods and associated developments impacting accessible MRI such as increasing/augmenting MR awareness and local expertise, incorporating hardware-cognizant methods, rapid quantitative imaging, and leveraging innovations from adjacent fields. Level of Evidence: 5 Technical Efficacy Stage: 6 J. Magn. Reson. Imaging 2019.

Gadolinium Doping in Zirconia-Toughened Alumina Systems and Their Structural, Mechanical, and Aging Behavior Repercussions.

A series of Gd3+ dopings in zirconia-toughened alumina (ZTA) systems were undertaken to explore the resultant structural, morphological, hydrothermal aging, and mechanical behavior and imaging contrast abilities. The results from the characterization techniques demonstrate the significance of Gd3+ in preserving the structural stability of ZTA systems. ZTA undergoes phase degradation with 10 wt % Gd3+ at 1400 °C, while the 100 wt % Gd3+ yields GdAlO3 even at 1200 °C. Gd3+ doping at the intermediate level preserves the structural stability of ZTA systems until 1400 °C. Gd3+ occupies the ZrO2 lattice, and its gradual accumulation induces tetragonal ZrO2 (t-ZrO2) to cubic ZrO2 (c-ZrO2) phase transition. α-Al2O3 crystallizes at 1200 °C and remains unperturbed except for its reaction with the free Gd3+ ions to yield GdAlO3. Aging studies and mechanical tests signify the impeccable role of Gd3+ in ZTA systems to resist phase degradation. Further, the imaging contrast ability of ZTA systems due to Gd3+ doping is verified from the in vitro magnetic resonance imaging (MRI) tests.

Compressed sensing to accelerate magnetic resonance spectroscopic imaging: evaluation and application to 23Na-imaging of mouse hearts.

BACKGROUND: Magnetic Resonance Spectroscopic Imaging (MRSI) has wide applicability for non-invasive biochemical assessment in clinical and pre-clinical applications but suffers from long scan times. Compressed sensing (CS) has been successfully applied to clinical 1H MRSI, however a detailed evaluation of CS for conventional chemical shift imaging is lacking. Here we evaluate the performance of CS accelerated MRSI, and specifically apply it to accelerate 23Na-MRSI on mouse hearts in vivo at 9.4 T. METHODS: Synthetic phantom data representing a simplified section across a mouse thorax were used to evaluate the fidelity of the CS reconstruction for varying levels of under-sampling, resolution and signal-to-noise ratios (SNR). The amplitude of signals arising from within a compartment, and signal contamination arising from outside the compartment relative to noise-free Fourier-transformed (FT) data were determined. Simulation results were subsequently verified experimentally in phantoms and in three mouse hearts in vivo. RESULTS: CS reconstructed MRSI data are scaled linearly relative to absolute signal intensities from the fully-sampled FT reconstructed case (R(2) > 0.8, p-value < 0.001). Higher acceleration factors resulted in a denoising of the reconstructed spectra, but also in an increased blurring of compartment boundaries, particularly at lower spatial resolutions. Increasing resolution and SNR decreased cross-compartment contamination and yielded signal amplitudes closer to the FT data. Proof-of-concept high-resolution, 3-fold accelerated 23Na-amplitude maps of murine myocardium could be obtained within ~23 mins. CONCLUSIONS: Relative signal amplitudes (i.e. metabolite ratios) and absolute quantification of metabolite concentrations can be accurately determined with up to 5-fold under-sampled, CS-reconstructed MRSI. Although this work focused on murine cardiac 23Na-MRSI, the results are equally applicable to other nuclei and tissues (e.g., 1H MRSI in brain). Significant reduction in MRSI scan time will reduce the burden on the subject, increase scanner throughput, and may open new avenues for (pre-) clinical metabolic studies.

Acceleration of conventional data acquisition in dynamic contrast enhancement: comparing keyhole approaches with compressive sensing.

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) has become a valuable clinical tool for cancer diagnosis and prognosis. DCE MRI provides pharmacokinetic parameters dependent on the extravasation of small molecular contrast agents, and thus high temporal resolution and/or spatial resolution is required for accurate estimation of parameters. In this article we investigate the efficacy of 2 undersampling approaches to speed up DCE MRI: a conventional keyhole approach and compressed sensing-based imaging. Data reconstructed from variants of these methods has been compared with the full k-space reconstruction with respect to data quality and pharmacokinetic parameters Ktrans and ve. Overall, compressive sensing provides better data quality and reproducible parametric maps than key-hole methods with higher acceleration factors. In particular, an undersampling mask based on a priori precontrast data showed high fidelity of reconstructed data and parametric maps up to 5× acceleration.

Compressed sensing MRI: a review.

Compressed sensing (CS) is a mathematical framework that reconstructs data from highly undersampled measurements. To gain acceleration in acquisition time, CS has been applied to MRI and has been demonstrated on diverse MRI methods. This review discusses the important requirements to qualify MRI to become an optimal application of CS, namely, sparsity, pseudo-random undersampling, and nonlinear reconstruction. By utilizing concepts of transform sparsity and compression, CS allows acquisition of only the important coefficients of the signal during the acquisition. A priori knowledge of MR images specifically related to transform sparsity is required for the application of CS. In this paper, Section I introduces the fundamentals of CS and the idea of CS as applied to MRI. The requirements for application of CS to MRI is discussed in Section II, while the various acquisition techniques, reconstruction techniques, the advantages of combining CS and parallel imaging, and sampling mask design problems are discussed in Section III. Numerous applications of CS in MRI due to its ability to improve imaging speed are reviewed in section IV. Clinical evaluations of some of the CS applications recently published are discussed in Section V. Section VI provides information on available open source software that could be used for CS implementations.

Tailored magnetic resonance fingerprinting of post-operative pediatric brain tumor patients.

PURPOSE: Brain and spinal cord tumors are the second most common cancer in children and account for one out of four cancers diagnosed. However, the long acquisition times associated with acquiring both data types prohibit using quantitative MR (qMR) in pediatric imaging protocols. This study aims to demonstrate the tailored magnetic resonance fingerprinting's (TMRF) ability to simultaneously provide quantitative maps (T1, T2) and multi-contrast qualitative images (T1 weighted, T1 FLAIR, T2 weighted) rapidly in pediatric brain tumor patients. METHODS: In this work, we imaged five pediatric patients with brain tumors (resected/residual) using TMRF at 3 T. We compared the TMRF-derived T2 weighted images with those from the vendor-supplied sequence (as the gold standard, GS) for healthy and pathological tissue signal intensities. The relaxometric maps from TMRF were subjected to a region of interest (ROI) analysis to differentiate between healthy and pathological tissues. We performed the Wilcoxon rank sum test to check for significant differences between the two tissue types. RESULTS: We found significant differences (p < 0.05) in both T1 and T2 ROI values between the two tissue types. A strong correlation was found between the TMRF-based T2 weighted and GS signal intensities for the healthy (correlation coefficient, r = 0.99) and pathological tissues (r = 0.88). CONCLUSION: The TMRF implementation provides the two relaxometric maps and can potentially save ~2 min if it replaces the T2-weighted imaging in the current protocol.

Tailored magnetic resonance fingerprinting.

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T1 weighted, T1 fluid attenuated inversion recovery (FLAIR), T2 weighted, water, and fat), and quantitative imaging (T1 and T2 maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T1 and T2 values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min-1 which is higher compared to GS (0.32 min-1) and MRF (0.90 min-1).

Magnetic resonance fingerprinting based thermometry (MRFT): application toex vivoimaging near DBS leads.

The purpose of this study is to demonstrate the first work ofT1-based magnetic resonance thermometry using magnetic resonance fingerprinting (dubbed MRFT). We compared temperature estimation of MRFT with proton resonance frequency shift (PRFS) thermometry onex vivobovine muscle. We demonstrated MRFT's feasibility in predicting temperature onex vivobovine muscles with deep brain stimulation (DBS) lead.B0maps generated from MRFT were compared with gold standardB0maps near the DBS lead. MRFT and PRFS estimated temperatures were compared in the presence of motion. All experiments were performed on a 3 Tesla whole-body GE Premier system with a 21-channel receive head coil (GE Healthcare, Milwaukee, WI). Four fluoroptic probes were used to measure the temperature at the center of a cold muscle (probe 1), the room temperature water bottle (probe 2), and the center and periphery of the heated muscle (probes 3 and 4). We selected regions of interest (ROIs) around the location of the probes and used simple linear regression to generate the temperature sensitivity calibration equations that convertT1maps and Δsmaps to temperature maps. We then repeated the same setup and compared MRFT and PRFS thermometry temperature estimation with gold standard probe measurements. For the MRFT experiment on DBS lead, we taped the probe to the tip of the DBS lead and used a turbo spin echo sequence to induce heating near the lead. We selected ROIs around the tip of the lead to compare MRFT temperature estimation with probe measurements and compared with PRFS temperature estimation. Vendor-suppliedB0mapping sequence was acquired to compare with MRFT-generatedB0maps. We found strong linear relationships (R2> 0.958) betweenT1and temperature and Δsand temperatures in our temperature sensitivity calibration experiment. MRFT and PRFS thermometry both accurately predict temperature (RMSE < 1.55 °C) compared to probe measurements. MRFT estimated temperature near DBS lead has a similar trend as the probe temperature. BothB0maps show inhomogeneities around the lead. MRFT estimated temperature is less sensitive to motion.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

Compressive sensing could accelerate 1H MR metabolic imaging in the clinic.

PURPOSE: To retrospectively evaluate the fidelity of magnetic resonance (MR) spectroscopic imaging data preservation at a range of accelerations by using compressed sensing. MATERIALS AND METHODS: The protocols were approved by the institutional review board of the university, and written informed consent to acquire and analyze MR spectroscopic imaging data was obtained from the subjects prior to the acquisitions. This study was HIPAA compliant. Retrospective application of compressed sensing was performed on 10 clinical MR spectroscopic imaging data sets, yielding 600 voxels from six normal brain data sets, 163 voxels from two brain tumor data sets, and 36 voxels from two prostate cancer data sets for analysis. The reconstructions were performed at acceleration factors of two, three, four, five, and 10 and were evaluated by using the root mean square error (RMSE) metric, metabolite maps (choline, creatine, N-acetylaspartate [NAA], and/or citrate), and statistical analysis involving a voxelwise paired t test and one-way analysis of variance for metabolite maps and ratios for comparison of the accelerated reconstruction with the original case. RESULTS: The reconstructions showed high fidelity for accelerations up to 10 as determined by the low RMSE (< 0.05). Similar means of the metabolite intensities and hot-spot localization on metabolite maps were observed up to a factor of five, with lack of statistically significant differences compared with the original data. The metabolite ratios of choline to NAA and choline plus creatine to citrate did not show significant differences from the original data for up to an acceleration factor of five in all cases and up to that of 10 for some cases. CONCLUSION: A reduction of acquisition time by up to 80%, with negligible loss of information as evaluated with clinically relevant metrics, has been successfully demonstrated for hydrogen 1 MR spectroscopic imaging.

Tailored magnetic resonance fingerprinting for simultaneous non-synthetic and quantitative imaging: A repeatability study.

PURPOSE: The goals of this study include: (a) generating tailored magnetic resonance fingerprinting (TMRF) based non-synthetic imaging; (b) assessing the repeatability of TMRF and deep learning-based mapping of in vitro ISMRM/NIST phantom and in vivo brain data of healthy human subjects. METHODS: We have acquired qualitative images obtained from the vendor-supplied gold standard (GS), MRF (synthetic), and TMRF (non-synthetic) on one representative healthy human brain. We also acquired 30 datasets on the ISMRM/NIST phantom for the in vitro repeatability study on a GE Discovery 3T MR750w scanner using the TMRF sequence. We compared T1 and T2 maps generated from 30 ISMRM/NIST phantom datasets to the spin-echo (SE) based GS method as part of the in vitro repeatability study. R-squared coefficient of determination in a simple linear regression and Bland-Altman analysis were computed for 30 datasets of ISMRM/NIST phantom to assess the accuracy of in vitro quantitative TMRF data. The repeatability of T1 and T2 estimates by TMRF was evaluated by calculating the standard deviation (SD) divided by the average of 30 datasets for each sphere, respectively. We acquired 10 volunteers for the in vivo repeatability study on the same scanner using the same TMRF sequence. These volunteers were imaged five times with two runs per repetition, resulting in 100 in vivo datasets. Five contrasts, T1 and T2 maps of 10 human volunteers acquired over five repetitions, were evaluated in the in vivo repeatability study. We computed the intraclass correlation coefficient (ICC) of the signal-to-noise ratio (SNR), signal intensities, T1 and T2 relaxation times in white matter (WM), and gray matter (GM). RESULTS: The synthetic images generated from MRF show partial volume and flow artifacts compared to non-synthetic images obtained from TMRF images and the GS. In vitro studies show that TMRF estimates have less than 5% variations except sphere 14 in the T2 array (6.36%). TMRF and SE relaxometry measurements were strongly correlated; R2 values were 0.9958 and 0.9789 for T1 and T2 estimates, respectively. Based on the ICC values, SNR, mean intensity values, and relaxation times of WM and GM for the in vivo studies were consistent. T1 and T2 values of WM and GM were similar to previously published values. The mean ± SD of T1 and T2 for WM for ten subjects and five repeats are 992 ± 41 ms and 99 ± 6 ms, while the corresponding values for T1 and T2 for GM are 1598 ± 73 ms and 152 ± 14 ms. CONCLUSION: TMRF and deep learning-based reconstruction produce repeatable, non-synthetic multi-contrast images, and parametric maps simultaneously.