Efficient Generation of Transcriptomic Profiles by Random Composite Measurements.

RNA profiles are an informative phenotype of cellular and tissue states but can be costly to generate at massive scale. Here, we describe how gene expression levels can be efficiently acquired with random composite measurements-in which abundances are combined in a random weighted sum. We show (1) that the similarity between pairs of expression profiles can be approximated with very few composite measurements; (2) that by leveraging sparse, modular representations of gene expression, we can use random composite measurements to recover high-dimensional gene expression levels (with 100 times fewer measurements than genes); and (3) that it is possible to blindly recover gene expression from composite measurements, even without access to training data. Our results suggest new compressive modalities as a foundation for massive scaling in high-throughput measurements and new insights into the interpretation of high-dimensional data.

Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning.

Following their success in numerous imaging and computer vision applications, deep-learning (DL) techniques have emerged as one of the most prominent strategies for accelerated MRI reconstruction. These methods have been shown to outperform conventional regularized methods based on compressed sensing (CS). However, in most comparisons, CS is implemented with two or three hand-tuned parameters, while DL methods enjoy a plethora of advanced data science tools. In this work, we revisit [Formula: see text]-wavelet CS reconstruction using these modern tools. Using ideas such as algorithm unrolling and advanced optimization methods over large databases that DL algorithms utilize, along with conventional insights from wavelet representations and CS theory, we show that [Formula: see text]-wavelet CS can be fine-tuned to a level close to DL reconstruction for accelerated MRI. The optimized [Formula: see text]-wavelet CS method uses only 128 parameters compared to >500,000 for DL, employs a convex reconstruction at inference time, and performs within <1% of a DL approach that has been used in multiple studies in terms of quantitative quality metrics.

On the sparsity of fitness functions and implications for learning.

Fitness functions map biological sequences to a scalar property of interest. Accurate estimation of these functions yields biological insight and sets the foundation for model-based sequence design. However, the fitness datasets available to learn these functions are typically small relative to the large combinatorial space of sequences; characterizing how much data are needed for accurate estimation remains an open problem. There is a growing body of evidence demonstrating that empirical fitness functions display substantial sparsity when represented in terms of epistatic interactions. Moreover, the theory of Compressed Sensing provides scaling laws for the number of samples required to exactly recover a sparse function. Motivated by these results, we develop a framework to study the sparsity of fitness functions sampled from a generalization of the NK model, a widely used random field model of fitness functions. In particular, we present results that allow us to test the effect of the Generalized NK (GNK) model's interpretable parameters-sequence length, alphabet size, and assumed interactions between sequence positions-on the sparsity of fitness functions sampled from the model and, consequently, the number of measurements required to exactly recover these functions. We validate our framework by demonstrating that GNK models with parameters set according to structural considerations can be used to accurately approximate the number of samples required to recover two empirical protein fitness functions and an RNA fitness function. In addition, we show that these GNK models identify important higher-order epistatic interactions in the empirical fitness functions using only structural information.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging.

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

Fast and motion-robust saturation transfer MRI with inherent B0 correction using rosette trajectories and compressed sensing.

PURPOSE: To implement rosette readout trajectories with compressed sensing reconstruction for fast and motion-robust CEST and magnetization transfer contrast imaging with inherent correction of B0 inhomogeneity. METHODS: A pulse sequence was developed for fast saturation transfer imaging using a stack of rosette trajectories with a higher sampling density near the k-space center. Each rosette lobe was segmented into two halves to generate dual-echo images. B0 inhomogeneities were estimated using the phase difference between the images and corrected subsequently. The rosette-based imaging was evaluated in comparison to a fully sampled Cartesian trajectory and demonstrated on CEST phantoms (creatine solutions and egg white) and healthy volunteers at 3 T. RESULTS: Compared with the conventional Cartesian acquisition, compressed sensing reconstructed rosette images provided image quality with overall higher contrast-to-noise ratio and significantly faster readout time. Accurate B0 map estimation was achieved from the rosette acquisition with a negligible bias of 0.01 Hz between the rosette and dual-echo Cartesian gradient echo B0 maps, using the latter as ground truth. The water-saturation spectra (Z-spectra) and amide proton transfer weighted signals obtained from the rosette-based sequence were well preserved compared with the fully sampled data, both in the phantom and human studies. CONCLUSIONS: Fast, motion-robust, and inherent B0-corrected CEST and magnetization transfer contrast imaging using rosette trajectories could improve subject comfort and compliance, contrast-to-noise ratio, and provide inherent B0 homogeneity information. This work is expected to significantly accelerate the translation of CEST-MRI into a robust, clinically viable approach.

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing.

PURPOSE: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in-plane resolution but typically has long TEs. Half-sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half-sinc acquisitions while maintaining SNR and TSC. METHODS: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full-sinc (TE = 0.73 ms, scan time ≈ 5 min) and half-sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half-sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. RESULTS: TSC was significantly higher from half-sinc than from full-sinc acquisitions, ex vivo and in vivo. Ex vivo, half-sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half-sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. CONCLUSION: 2D sodium MRI with half-sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

Radial compressed sensing imaging improves the velocity detection limit of single cell tracking time-lapse MRI.

PURPOSE: Time-lapse MRI enables tracking of single iron-labeled cells. Yet, due to temporal blurring, only slowly moving cells can be resolved. To study faster cells for example during inflammatory processes, accelerated acquisition is needed. METHODS: A rotating phantom system was developed to quantitatively measure the current maximum detectable speed of cells in time-lapse MRI. For accelerated cell tracking, an interleaved radial acquisition scheme was applied to phantom and murine brain in vivo time-lapse MRI experiments at 9.4 T. Detection of iron-labeled cells was evaluated in fully sampled and undersampled reconstructions with and without compressed sensing. RESULTS: The rotating phantom system enabled ultra-slow rotation of phantoms and a velocity detection limit of full-brain Cartesian time-lapse MRI of up to 172 µm/min was determined. Both phantom and in vivo measurements showed that single cells can be followed dynamically using radial time-lapse MRI. Higher temporal resolution of undersampled reconstructions reduced geometric distortion, the velocity detection limit was increased to 1.1 mm/min in vitro, and previously hidden fast-moving cells were recovered. In the mouse brain after in vivo labeling, a total of 42 ± 4 cells were counted in fully sampled, but only 7 ± 1 in undersampled images due to streaking artifacts. Using compressed sensing 33 ± 4 cells were detected. CONCLUSION: Interleaved radial time-lapse MRI permits retrospective reconstruction of both fully sampled and accelerated images, enables single cell tracking at higher temporal resolution and recovers cells hidden before due to blurring. The velocity detection limit as determined with the rotating phantom system increased two- to three-fold compared to previous results.

Multidimensional compressed sensing to advance 23 Na multi-quantum coherences MRI.

PURPOSE: Sodium (23 Na) multi-quantum coherences (MQC) MRI was accelerated using three-dimensional (3D) and a dedicated five-dimensional (5D) compressed sensing (CS) framework for simultaneous Cartesian single (SQ) and triple quantum (TQ) sodium imaging of in vivo human brain at 3.0 and 7.0 T. THEORY AND METHODS: 3D 23 Na MQC MRI requires multi-echo paired with phase-cycling and exhibits thus a multidimensional space. A joint reconstruction framework to exploit the sparsity in all imaging dimensions by extending the conventional 3D CS framework to 5D was developed. 3D MQC images of simulated brain, phantom and healthy brain volunteers obtained from 3.0 T and 7.0 T were retrospectively and prospectively undersampled. Performance of the CS models were analyzed by means of structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR) and signal quantification of tissue sodium concentration and TQ/SQ ratio. RESULTS: It was shown that an acceleration of three-fold, leading to less than 2 × 10 $$ 2\times 10 $$ min of scan time with a resolution of 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ at 3.0 T, are possible. 5D CS improved SSIM by 3%, 5%, 1% and reduced RMSE by 50%, 30%, 8% for in vivo SQ, TQ, and TQ/SQ ratio maps, respectively. Furthermore, for the first time prospective undersampling enabled unprecedented high resolution from 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ to 6 × 6 × 10 mm 3 $$ 6\times 6\times 10\;{\mathrm{mm}}^3 $$ MQC images of in vivo human brain at 7.0 T without extending acquisition time. CONCLUSION: 5D CS proved to allow up to three-fold acceleration retrospectively on 3.0 T data. 2-fold acceleration was demonstrated prospectively at 7.0 T to reach higher spatial resolution of 23 Na MQC MRI.

GRASP reconstruction amplified with view-sharing and KWIC filtering reduces underestimation of peak velocity in highly-accelerated real-time phase-contrast MRI: A preliminary evaluation in pediatric patients with congenital heart disease.

PURPOSE: To develop a highly-accelerated, real-time phase contrast (rtPC) MRI pulse sequence with 40 fps frame rate (25 ms effective temporal resolution). METHODS: Highly-accelerated golden-angle radial sparse parallel (GRASP) with over regularization may result in temporal blurring, which in turn causes underestimation of peak velocity. Thus, we amplified GRASP performance by synergistically combining view-sharing (VS) and k-space weighted image contrast (KWIC) filtering. In 17 pediatric patients with congenital heart disease (CHD), the conventional GRASP and the proposed GRASP amplified by VS and KWIC (or GRASP + VS + KWIC) reconstruction for rtPC MRI were compared with respect to clinical standard PC MRI in measuring hemodynamic parameters (peak velocity, forward volume, backward volume, regurgitant fraction) at four locations (aortic valve, pulmonary valve, left and right pulmonary arteries). RESULTS: The proposed reconstruction method (GRASP + VS + KWIC) achieved better effective spatial resolution (i.e., image sharpness) compared with conventional GRASP, ultimately reducing the underestimation of peak velocity from 17.4% to 6.4%. The hemodynamic metrics (peak velocity, volumes) were not significantly (p > 0.99) different between GRASP + VS + KWIC and clinical PC, whereas peak velocity was significantly (p < 0.007) lower for conventional GRASP. RtPC with GRASP + VS + KWIC also showed the ability to assess beat-to-beat variation and detect the highest peak among peaks. CONCLUSION: The synergistic combination of GRASP, VS, and KWIC achieves 25 ms effective temporal resolution (40 fps frame rate), while minimizing the underestimation of peak velocity compared with conventional GRASP.

Fast and High-Resolution T2 Mapping Based on Echo Merging Plus k-t Undersampling with Reduced Refocusing Flip Angles (TEMPURA) as Methods for Human Renal MRI.

PURPOSE: To develop a highly accelerated multi-echo spin-echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T2 mapping. METHODS: TEMPURA merges several adjacent echoes into one k-space by either combining independent echoes or sharing one echo between k-spaces. The combined k-space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath-hold sequence; and a high-resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors. RESULTS: The fast TEMPURA method reduced the acquisition time from 3-5 min to one breath-hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory-triggered sequence (7.4%), but much lower than a sequence accelerated by purely k-t undersampling (21.8%). High-resolution TEMPURA reduced the in-plane voxel size from 3 × 3 to 1 × 1 mm2, resulting in improved visualization of the detailed anatomical structure. In vivo T2 measurements demonstrated good agreement (fast: MAPE = 1.3%-2.5%; high-resolution: MAPE = 2.8%-3.3%) and high correlation coefficients (fast: R = 0.85-0.98; high-resolution: 0.82-0.96) with the standardized method, outperforming k-t undersampling alone (MAPE = 3.3-4.5%, R = 0.57-0.59). CONCLUSION: TEMPURA provides fast and high-resolution renal T2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution.

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.

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

PURPOSE: To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD: A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS: The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION: The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

A decay-modeled compressed sensing reconstruction approach for non-Cartesian hyperpolarized 129Xe MRI.

PURPOSE: Hyperpolarized 129Xe MRI benefits from non-Cartesian acquisitions that sample k-space efficiently and rapidly. However, their reconstructions are complex and burdened by decay processes unique to hyperpolarized gas. Currently used gridded reconstructions are prone to artifacts caused by magnetization decay and are ill-suited for undersampling. We present a compressed sensing (CS) reconstruction approach that incorporates magnetization decay in the forward model, thereby producing images with increased sharpness and contrast, even in undersampled data. METHODS: Radio-frequency, T1, and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ decay processes were incorporated into the forward model and solved using iterative methods including CS. The decay-modeled reconstruction was validated in simulations and then tested in 2D/3D-spiral ventilation and 3D-radial gas-exchange MRI. Quantitative metrics including apparent-SNR and sharpness were compared between gridded, CS, and twofold undersampled CS reconstructions. Observations were validated in gas-exchange data collected from 15 healthy and 25 post-hematopoietic-stem-cell-transplant participants. RESULTS: CS reconstructions in simulations yielded images with threefold increases in accuracy. CS increased sharpness and contrast for ventilation in vivo imaging and showed greater accuracy for undersampled acquisitions. CS improved gas-exchange imaging, particularly in the dissolved-phase where apparent-SNR improved, and structure was made discernable. Finally, CS showed repeatability in important global gas-exchange metrics including median dissolved-gas signal ratio and median angle between real/imaginary components. CONCLUSION: A non-Cartesian CS reconstruction approach that incorporates hyperpolarized 129Xe decay processes is presented. This approach enables improved image sharpness, contrast, and overall image quality in addition to up-to threefold undersampling. This contribution benefits all hyperpolarized gas MRI through improved accuracy and decreased scan durations.

Enhancing quality and speed in database-free neural network reconstructions of undersampled MRI with SCAMPI.

PURPOSE: We present SCAMPI (Sparsity Constrained Application of deep Magnetic resonance Priors for Image reconstruction), an untrained deep Neural Network for MRI reconstruction without previous training on datasets. It expands the Deep Image Prior approach with a multidomain, sparsity-enforcing loss function to achieve higher image quality at a faster convergence speed than previously reported methods. METHODS: Two-dimensional MRI data from the FastMRI dataset with Cartesian undersampling in phase-encoding direction were reconstructed for different acceleration rates for single coil and multicoil data. RESULTS: The performance of our architecture was compared to state-of-the-art Compressed Sensing methods and ConvDecoder, another untrained Neural Network for two-dimensional MRI reconstruction. SCAMPI outperforms these by better reducing undersampling artifacts and yielding lower error metrics in multicoil imaging. In comparison to ConvDecoder, the U-Net architecture combined with an elaborated loss-function allows for much faster convergence at higher image quality. SCAMPI can reconstruct multicoil data without explicit knowledge of coil sensitivity profiles. Moreover, it is a novel tool for reconstructing undersampled single coil k-space data. CONCLUSION: Our approach avoids overfitting to dataset features, that can occur in Neural Networks trained on databases, because the network parameters are tuned only on the reconstruction data. It allows better results and faster reconstruction than the baseline untrained Neural Network approach.

Free-breathing, fat-corrected T1 mapping of the liver with stack-of-stars MRI, and joint estimation of T1, PDFF, R 2 * , and B 1 + .

PURPOSE: Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat and B 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. THEORY AND METHODS: Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. RESULTS: The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. CONCLUSION: The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, and B 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI.

PURPOSE: To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks. THEORY AND METHODS: The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. RESULTS: The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min. CONCLUSION: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.

PURPOSE: To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. THEORY: We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. METHODS: We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization, PICS and other model-based approaches such as GraSP, MOBA. RESULTS: Compared to conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance in T 1 $$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. CONCLUSION: AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain T 2 mapping.

PURPOSE: To accelerate whole-brain quantitative T 2 $$ {\mathrm{T}}_2 $$ mapping in preclinical imaging setting. METHODS: A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms of T 2 $$ {\mathrm{T}}_2 $$ precision. The method was validated on a phantom containing a wide range of T 2 $$ {\mathrm{T}}_2 $$ and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and T 2 $$ {\mathrm{T}}_2 $$ estimation were assessed for 3 acceleration factors (6/8/10). RESULTS: The EPG-based dictionary gave robust estimations of a large range of T 2 $$ {\mathrm{T}}_2 $$ . A subspace dimension of 6 was the best compromise between T 2 $$ {\mathrm{T}}_2 $$ precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brain T 2 $$ {\mathrm{T}}_2 $$ maps, the detection and the monitoring of metastasis growth of less than 500 µ m 3 $$ \mu {\mathrm{m}}^3 $$ . CONCLUSION: Subspace-based reconstruction is suitable for 3D T 2 $$ {\mathrm{T}}_2 $$ mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 µ $$ \mu $$ m on the mouse brain, used here for monitoring metastases growth.