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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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z‐spectra and amide proton transfer (APT) contrast in brain tumors. Methods: High‐grade glioma patients were scanned using an RF saturation‐encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation‐transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch‐McConnell equations and evaluated in brain tumors. Results: The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%–33% of the saturated signal in the gadolinium‐enhancing tumor (13%–20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium‐enhancing tumor was significantly higher than that of the normal‐appearing white matter (10.1% vs. 8.3% at 1 μT and 11.2% vs. 7.8% at 1.5 μT). Conclusion: The RF saturation‐encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z‐spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel MR physics‐driven, deep‐learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. Methods: A neural network was designed to predict a direct water saturation and MTC‐dominated signal at a certain CEST frequency offset using a few high‐frequency offset features in the Z‐spectrum. The accuracy, scan‐rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. Results: The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190‐fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR‐based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. Conclusion: The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross‐sectional studies with low scan‐rescan variability. [ABSTRACT FROM AUTHOR]

Purpose: To develop a unified deep‐learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. Methods: The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross‐linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization‐transfer ratio asymmetry effect was evaluated in MTC‐MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test–retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep‐learning framework. Results: Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC‐MRF dictionary or a training data set reduced the computation time by 181‐fold, without compromising MRF profile accuracy. The recurrent neural network–based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC‐MRF framework for tissue‐parameter quantification, the test–retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. Conclusion: Bloch simulator–driven, deep‐learning MTC‐MRF can provide robust and repeatable multiple‐tissue parameter quantification in a clinically feasible scan time on a 3T scanner. [ABSTRACT FROM AUTHOR]

Amide proton transfer (APT) imaging, a variant of chemical exchange saturation transfer MRI, has shown promise in detecting ischemic tissue acidosis following impaired aerobic metabolism in animal models and in human stroke patients due to the sensitivity of the amide proton exchange rate to changes in pH within the physiological range. Recent studies have demonstrated the possibility of using APT‐MRI to detect acidosis of the ischemic penumbra, enabling the assessment of stroke severity and risk of progression, monitoring of treatment progress, and prognostication of clinical outcome. This paper reviews current APT imaging methods actively used in ischemic stroke research and explores the clinical aspects of ischemic stroke and future applications for these methods. [ABSTRACT FROM AUTHOR]

Purpose: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging. Methods: Pseudo‐randomized RF saturation parameters and relaxation delay times were applied in an MR fingerprinting framework to generate transient‐state signal evolutions for different MTC parameters. Prospectively compressed sensing–accelerated (four‐fold) MR fingerprinting images were acquired from 6 healthy volunteers at 3 T. A convolutional neural network framework in an unsupervised fashion was designed to solve an inverse problem of a two‐pool MTC Bloch equation, and was compared with a conventional Bloch equation–based fitting approach. The MTC images synthesized by the convolutional neural network architecture were used for amide proton transfer and nuclear Overhauser enhancement imaging as a reference baseline image. Results: The fully unsupervised learning scheme incorporated with the two‐pool exchange model learned a set of unique features that can describe the MTC–MR fingerprinting input, and allowed only small amounts of unlabeled data for training. The MTC parameter values estimated by the unsupervised learning method were in excellent agreement with values estimated by the conventional Bloch fitting approach, but dramatically reduced computation time by ~1000‐fold. Conclusion: Given the considerable time efficiency compared to conventional Bloch fitting, unsupervised learning‐based MTC–MR fingerprinting could be a powerful tool for quantitative MTC and CEST/nuclear Overhauser enhancement imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop prospectively accelerated 3D CEST imaging using compressed sensing (CS), combined with a saturation scheme based on time‐interleaved parallel transmission. Methods: A variable density pseudo‐random sampling pattern with a centric elliptical k‐space ordering was used for CS acceleration in 3D. Retrospective CS studies were performed with CEST phantoms to test the reconstruction scheme. Prospectively CS‐accelerated 3D‐CEST images were acquired in 10 healthy volunteers and 6 brain tumor patients with an acceleration factor (RCS) of 4 and compared with conventional SENSE reconstructed images. Amide proton transfer weighted (APTw) signals under varied RF saturation powers were compared with varied acceleration factors. Results: The APTw signals obtained from the CS with acceleration factor of 4 were well‐preserved as compared with the reference image (SENSE R = 2) both in retrospective phantom and prospective healthy volunteer studies. In the patient study, the APTw signals were significantly higher in the tumor region (gadolinium [Gd]‐enhancing tumor core) than in the normal tissue (p <.001). There was no significant APTw difference between the CS‐accelerated images and the reference image. The scan time of CS‐accelerated 3D APTw imaging was dramatically reduced to 2:10 minutes (in‐plane spatial resolution of 1.8 × 1.8 mm2; 15 slices with 4‐mm slice thickness) as compared with SENSE (4:07 minutes). Conclusion: Compressed sensing acceleration was successfully extended to 3D‐CEST imaging without compromising CEST image quality and quantification. The CS‐based CEST imaging can easily be integrated into clinical protocols and would be beneficial for a wide range of applications. [ABSTRACT FROM AUTHOR]

Abstract Current chemical exchange saturation transfer (CEST) neuroimaging protocols typically acquire CEST-weighted images, and, as such, do not essentially provide quantitative proton-specific exchange rates (or brain pH) and concentrations. We developed a dictionary-free MR fingerprinting (MRF) technique to allow CEST parameter quantification with a reduced data set. This was accomplished by subgrouping proton exchange models (SPEM), taking amide proton transfer (APT) as an example, into two-pool (water and semisolid macromolecules) and three-pool (water, semisolid macromolecules, and amide protons) models. A variable radiofrequency saturation scheme was used to generate unique signal evolutions for different tissues, reflecting their CEST parameters. The proposed MRF-SPEM method was validated using Bloch-McConnell equation-based digital phantoms with known ground-truth, which showed that MRF-SPEM can achieve a high degree of accuracy and precision for absolute CEST parameter quantification and CEST phantoms. For in-vivo studies at 3 T, using the same model as in the simulations, synthetic Z-spectra were generated using rates and concentrations estimated from the MRF-SPEM reconstruction and compared with experimentally measured Z-spectra as the standard for optimization. The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping. Highlights • A new MR fingerprinting concept was proposed to allow CEST quantification. • A varied RF saturation was designed to generate CEST signal evolutions. • Synthetic CEST MRI was used for validation of in-vivo CEST quantification. • The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the dependence of magnetization transfer ratio asymmetry at 3.5 ppm (MTRasym(3.5 ppm)), quantitative amide proton transfer (APT#), and nuclear Overhauser enhancement (NOE#) signals or contrasts on experimental imaging parameters. Methods: Modified Bloch equation‐based simulations using 2‐pool and 5‐pool exchange models and in vivo rat brain tumor experiments at 4.7T were performed with varied RF saturation power levels, saturation lengths, and relaxation delays. The MTRasym(3.5 ppm), APT#, and NOE# contrasts between tumor and normal tissues were compared among different experimental parameters. Results: The MTRasym(3.5 ppm) image contrasts between tumor and normal tissues initially increased with the RF saturation length, and the maxima occurred at 1.6−2 s under relatively high RF saturation powers (>2.1 μT) and at a longer saturation length under relatively low RF saturation powers (<1.3 μT). The APT# contrasts also increased with the RF saturation length but peaked at longer RF saturation lengths relative to MTRasym(3.5 ppm). The NOE# contrasts were either positive or negative, depending on the experimental parameters applied. Conclusion: Tumor MTRasym(3.5 ppm), APT#, and NOE# contrasts can be maximized at different saturation parameters. The maximum MTRasym(3.5 ppm) contrast can be obtained with a relatively longer RF saturation length (several seconds) at a relatively lower RF saturation power. [ABSTRACT FROM AUTHOR]

Purpose To quantify amide protein transfer (APT) effects in acidic ischemic lesions and assess the spatial-temporal relationship among diffusion, perfusion, and pH deficits in acute stroke patients. Methods Thirty acute stroke patients were scanned at 3 T. Quantitative APT (APT#) effects in acidic ischemic lesions were measured using an extrapolated semisolid magnetization transfer reference signal technique and compared with commonly used MTRasym(3.5ppm) or APT-weighted parameters. Results The APT# images showed clear pH deficits in the ischemic lesion, whereas the MTRasym(3.5ppm) signals were slightly hypointense. The APT# contrast between acidic ischemic lesions and normal tissue in acute stroke patients was more than three times larger than MTRasym(3.5ppm) contrast (−1.45 ± 0.40% for APT# versus −0.39 ± 0.52% for MTRasym(3.5ppm), P < 4.6 × 10−4). Hypoperfused and acidic areas without an apparent diffusion coefficient abnormality were observed and assigned to an ischemic acidosis penumbra. Hypoperfused areas at normal pH were also observed and assigned to benign oligemia. Hyperintense APT signals were observed in a hemorrhage area in one case. Conclusions The quantitative APT study using the extrapolated semisolid magnetization transfer reference signal approach enhances APT MRI sensitivity to pH compared with conventional APT-weighted MRI, allowing more reliable delineation of an ischemic acidosis in the penumbra. Magn Reson Med 78:871-880, 2017. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Purpose To evaluate the reliability of four CEST imaging metrics for brain tumors, at varied saturation power levels and magnetic field strengths (3−9.4 Tesla (T)). Methods A five-pool proton exchange model (free water, semisolid, amide, amine, and NOE-related protons) was used for the simulations. For the in vivo study, eight glioma-bearing rats were scanned at 4.7 T. The CEST ratio (CESTR), CESTR normalized with the reference value (CESTRnr), inverse Z-spectrum-based (MTRRex), and apparent exchange-related relaxation (AREX) were compared. Results The simulated CEST signal intensities using MTRRex and AREX were substantially increased at relatively high radiofrequency (RF) saturation powers at 3 T and 4.7 T, whereas CESTR and CESTRnr metrics remained relatively stable. There were tremendously high MTRRex and AREX signals around the water frequency at all field strengths because of the small denominators. In the rat tumor study at 4.7 T, both CESTR and CESTRnr showed clear contrasts in the tumor with respect to the normal tissue across all saturation power levels (0.5−3 μT), whereas the AREX showed negligible to negative insignificant contrasts. Conclusions CEST metrics must be carefully selected based on the different experimental settings. CESTR and CESTRnr are more reliable at 3 T (a clinical field strength) and 4.7 T. Magn Reson Med 77:1853-1865, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose To quantify pure chemical exchange-dependent saturation transfer (CEST) related amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) signals in a rat glioma model and to investigate the mixed effects of water content and water T1 on APT and NOE imaging signals. Methods Eleven U87 tumor-bearing rats were scanned at 4.7 T. A relatively accurate mathematical approach, based on extrapolated semisolid magnetization-transfer reference signals, was used to remove the concurrent effects of direct water saturation and semisolid magnetization-transfer. Pure APT and NOE signals, in addition to the commonly used magnetization-transfer-ratio asymmetry at 3.5 ppm, MTRasym(3.5ppm), were assessed. Results The measured APT signal intensity of the tumor (11.06%, much larger than the value reported in the literature) was the major contributor (approximately 80.6%) to the MTRasym(3.5ppm) contrast between the tumor and the contralateral brain region. Both the water content ([water proton]) and water T1 (T1w) were increased in the tumor, but there were no significant correlations among APT, NOE, or MTRasym(3.5ppm) signals and T1w/[water proton]. Conclusion The effect of increasing T1w on the CEST signal in the tumor was mostly eliminated by the effect of increasing water content, and the observed APT-weighted hyperintensity in the tumor should be dominated by the increased amide proton concentration. Magn Reson Med 77:855-863, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose To evaluate the feasibility of accelerated chemical-exchange-saturation-transfer (CEST) imaging using a combination of compressed sensing (CS) and sensitivity encoding (SENSE) at 3 Tesla. Theory and Methods Two healthy volunteers and six high-grade glioma patients were recruited. Raw CEST image k-space data were acquired (with varied radiofrequency saturation power levels for the healthy volunteer study), and a sequential CS and SENSE reconstruction (CS-SENSE) was assessed. The MTRasym(3.5 ppm) signals were compared with varied CS-SENSE acceleration factors. Results In the healthy volunteer study, a CS-SENSE acceleration factor of R = 2 × 2 (CS × SENSE) was achieved without compromising the reconstructed MTRasym(3.5 ppm) image quality. The MTRasym(3.5 ppm) signals obtained from the CS-SENSE reconstruction with R = 2 × 2 were well preserved compared with the reference image (R = 2 for only SENSE). In the glioma patient study, the MTRasym(3.5 ppm) signals were significantly higher in the tumor region (Gd-enhancing tumor core) than in the normal-appearing white matter ( P < 0.001). There was no significant MTRasym(3.5 ppm) difference between the reference image and CS-SENSE-reconstructed image in the acceleration factor of R = 2 × 2. Conclusion Combining the SENSE technique with CS (R = 2 × 2) enables considerable acceleration of CEST image acquisition and potentially has a wide range of clinical applications. Magn Reson Med 77:779-786, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the use of three extrapolated semisolid magnetization transfer reference (EMR) methods to quantify amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) signals in human glioma. Methods: Eleven patients with high-grade glioma were scanned at 3 Tesla. aEMR² (asymmetric magnetization-transfer or MT model to fit two-sided, wide-offset data), sEMR2 (symmetric MT model to fit two-sided, wide-offset data), and sEMR¹ (symmetric MT model to fit one-sided, wide-offset data) were assessed. ZEMR and experimental data at 3.5 ppm and -3.5 ppm were subtracted to calculate the APT and NOE signals (APT# and NOE#), respectively. Results: The aEMR2 and sEMR¹ models provided quite similar APT# signals, while the sEMR2 provided somewhat lower APT# signals. The aEMR2 had an erroneous NOE# quantification. Calculated APT# signal intensities of glioma (~4%), much larger than the values reported previously, were significantly higher than those of edema and normal tissue. Compared with normal tissue, gadolinium-enhancing tumor cores were consistently hyperintense on the APT# maps and slightly hypointense on the NOE# maps. Conclusion: The sEMR¹ model is the best choice for accurately quantifying APT and NOE signals. The APT-weighted hyperintensity in the tumor was dominated by the APT effect, and the MT asymmetry at 3.5 ppm is a reliable and valid metric for APT imaging of gliomas at 3T. [ABSTRACT FROM AUTHOR]

Purpose: To quantify amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) contributions to in vivo chemical exchange saturation transfer MRI signals in tumors. Theory and Methods: Two-pool (free water and semi-solid protons) and four-pool (free water, semi-solid, amide, and upfield NOE-related protons) tissue models combined with the super-Lorentzian lineshape for semi-solid protons were used to fit wide and narrow frequency-offset magnetization-transfer (MT) data, respectively. Extrapolated semi-solid MT signals at 3.5 and -3.5 ppm from water were used as reference signals to quantify APT and NOE, respectively. Six glioma-bearing rats were scanned at 4.7 Tesla. Quantitative APT and NOE signals were compared at three saturation power levels. Results: The observed APT signals were significantly higher in the tumor (center and rim) than in the contralateral normal brain tissue at all saturation powers, and were the major contributor to the APT-weighted image contrast (based on MT asymmetry analysis) between the tumor and the normal brain tissue. The NOE (a positive confounding factor) enhanced this APT-weighted image contrast. The fitted amide pool sizes were significantly larger, while the NOE-related pool sizes were significantly smaller in the tumor than in the normal brain tissue. Conclusion: The extrapolated semi-solid magnetization transfer reference provides a relatively accurate approach for quantitatively measuring pure APT and NOE signals. [ABSTRACT FROM AUTHOR]

Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qualitative MT ratio (MTR) metric commonly used in conventional MTC imaging is limited in the assessment of quantitative semisolid macromolecular proton exchange rates and concentrations. In addition, CEST signals measured by MTR asymmetry analysis are unavoidably contaminated by upfield nuclear Overhauser enhancement (NOE) signals of mobile and semisolid macromolecules. To address these issues, we developed an MTC-MR fingerprinting (MTC-MRF) technique to quantify tissue parameters, which further allows an estimation of accurate MTC signals at a certain CEST frequency offset. A pseudorandomized RF saturation scheme was used to generate unique MTC signal evolutions for different tissues and a supervised deep neural network was designed to extract tissue properties from measured MTC-MRF signals. Through detailed Bloch equation-based digital phantom and in vivo studies, we demonstrated that the MTC-MRF can quantify MTC characteristics with high accuracy and computational efficiency, compared to a conventional Bloch equation fitting approach, and provide baseline reference signals for CEST and NOE imaging. For validation, MTC-MRF images were synthesized using the tissue parameters estimated from the deep-learning method and compared with experimentally acquired MTC-MRF images as the reference standard. The proposed MTC-MRF framework can provide quantitative 3D MTC, CEST, and NOE imaging of the human brain within a clinically acceptable scan time. • A pseudorandomized RF saturation was designed to generate unique MTC signatures. • A deep-learning model was designed to quantify semisolid MTC and water parameters. • A synthetic MRI technique was used for validation of in vivo MTC quantification. • The proposed MTC-MRF framework allowed clean CEST/APT imaging. [ABSTRACT FROM AUTHOR]