Whole-brain steady-state CEST at 3 T using MR Multitasking

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets

PURPOSE

To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

METHODS

Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi-pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U-NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient-like cases.

RESULTS

Traditional multi-pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U-NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z-spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e-4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology.

CONCLUSION

The U-NET architecture effectively quantifies CEST maps from undersampled Z-spectra at various undersampling levels.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking

PURPOSE

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework.

METHODS

A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach.

RESULTS

In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time.

CONCLUSION

The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior

OBJECTIVE

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications.

METHODS

A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data.

RESULTS

In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images.

CONCLUSION AND SIGNIFICANCE

The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

Non-invasive mapping of brown adipose tissue activity with magnetic resonance imaging

Thermogenic brown adipose tissue (BAT) has a positive impact on whole-body metabolism. However, in vivo mapping of BAT activity typically relies on techniques involving ionizing radiation, such as [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) and computed tomography (CT). Here we report a noninvasive metabolic magnetic resonance imaging (MRI) approach based on creatine chemical exchange saturation transfer (Cr-CEST) contrast to assess in vivo BAT activity in rodents and humans. In male rats, a single dose of the β3-adrenoceptor agonist (CL 316,243) or norepinephrine, as well as cold exposure, triggered a robust elevation of the Cr-CEST MRI signal, which was consistent with the [18F]FDG PET and CT data and 1H nuclear magnetic resonance measurements of creatine concentration in BAT. We further show that Cr-CEST MRI detects cold-stimulated BAT activation in humans (both males and females) using a 3T clinical scanner, with data-matching results from [18F]FDG PET and CT measurements. This study establishes Cr-CEST MRI as a promising noninvasive and radiation-free approach for in vivo mapping of BAT activity.

Repurposing of the PET Probe Prototype PiB for Label and Radiation-Free CEST MRI Molecular Imaging of Amyloid

Positron emission tomography (PET) probes are specific and sensitive while suffering from radiation risk. It is worthwhile to explore the chemical emission saturation transfer (CEST) effects of the probe prototypes and repurpose them for CEST imaging to avoid radiation. In this study, we used 11C-PiB as an example of a PET probe for detecting amyloid and tested the feasibility of repurposing this PET probe prototype, PiB, for CEST imaging. After optimizing the parameters through preliminary phantom experiments, we used APP/PS1 transgenic mice and age-matched C57 mice for in vivo CEST magnetic resonance imaging (MRI) of amyloid. Furthermore, the pathological assessment was conducted on the same brain slices to evaluate the correlation between the CEST MRI signal abnormality and β-amyloid deposition detected by immunohistochemical staining. In our results, the Z-spectra revealed an apparent CEST effect that peaked at approximately 6 ppm. APP/PS1 mice as young as 9 months injected with PiB showed a significantly higher CEST effect compared to the control groups. The hyperintense region was correlated with the Aβ deposition shown by pathological staining. In conclusion, repurposing the PET probe prototype for CEST MRI imaging is feasible and enables label- and radiation-free detection of the amyloid while maintaining the sensitivity and specificity of the ligand. This study opens the door to developing CEST probes based on PET probe prototypes.

Joint K-space and Image-space Parallel Imaging (KIPI) for accelerated chemical exchange saturation transfer acquisition

PURPOSE

To develop an auto-calibrated technique by joint K-space and Image-space Parallel Imaging (KIPI) for accelerated CEST acquisition.

THEORY AND METHODS

The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto-calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k-space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression. The signal leakage due to the T₂ -decay filtering in k-space compromises the SENSE reconstruction, which can be corrected by the artifact suppression algorithm of KIPI. The 2D and 3D imaging experiments were done on the phantom, healthy volunteer, and brain tumor patient with a 3T scanner.

RESULTS

The proposed KIPI method was evaluated by retrospectively undersampled data with variable AFs and compared against existing parallel imaging methods (SENSE/auto, GRAPPA, and ESPIRiT). KIPI enabled CEST frames with random AFs to achieve similar image quality, eliminated the strong aliasing artifacts, and generated significantly smaller errors than the other methods (p < 0.01). The KIPI method permitted an AF up to 12-fold in both phase-encoding and slice-encoding directions for 3D CEST source images, achieving an overall 8.2-fold speedup in scan time.

CONCLUSION

KIPI is a novel auto-calibrated parallel imaging method that enables variable AFs for different CEST frames, achieves a significant reduction in scan time, and does not compromise the accuracy of CEST maps.

Free-breathing 3D CEST MRI of human liver at 3.0 T

PURPOSE

To develop a novel 3D abdominal CEST MRI technique at 3 T using MR multitasking, which enables entire-liver coverage with free-breathing acquisition.

METHODS

k-Space data were continuously acquired with repetitive steady-state CEST (ss-CEST) modules. The stack-of-stars acquisition pattern was used for k-space sampling. MR multitasking was used to reconstruct motion-resolved 3D CEST images of 53 frequency offsets with entire-liver coverage and 2.0 × 2.0 × 6.0 mm3 spatial resolution. The total scan time was 9 min. The sensitivity of amide proton transfer (APT)-CEST (magnetization transfer asymmetry [MTRasym ] at 3.5 ppm) and glycogen CEST (glycoCEST) (mean MTRasym around 1.0 ppm) signals generated with the proposed method were tested with fasting experiments.

RESULTS

Both APT-CEST and glycoCEST signals showed high sensitivity between post-fasting and post-meal acquisitions. APT-CEST and glycoCEST MTRasym signals from post-mean scans were significantly increased (APT-CEST: -0.019 ± 0.017 in post-fasting scans, 0.014 ± 0.021 in post-meal scans, p < 0.01; glycoCEST: 0.003 ± 0.009 in post-fasting scans, 0.027 ± 0.021 in post-meal scans, p < 0.01).

CONCLUSION

The proposed 3D abdominal steady-state CEST method using MR multitasking can generate CEST images of the entire liver during free breathing.

Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking

PURPOSE

To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping.

METHODS

MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T₁ /T₂ /T_1ρ mapping in the brain was investigated.

RESULTS

For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T₁ /T₂ /T1_ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T₁ /T₂ /T_1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T₁ /T₂ /T_1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance.

CONCLUSION

Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.