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The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metrics of quantitative gradient-recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B 0 ) magnetic field inhomogeneities directly from the gradient-recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel-by-voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B 0 -inhomogeneities contributions to multigradient-echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with low signal-to-noise ratio (SNR ~ 50-100), where DANSE-generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time-dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications., (© 2022 John Wiley & Sons Ltd.)

Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data., Methods: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B 0 -inhomogeneity-corrected R 2 ∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R 2 ∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B 0 -inhomogeneity-corrected quantitative R 2 ∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay., Results: We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R 2 ∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models., Conclusion: Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R 2 ∗ maps, while LEARN-BIO directly performs motion- and B 0 -inhomogeneity-corrected R 2 ∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer's disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis., Objective: To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus., Methods: Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17)., Results: In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as "Dark Matter", and the other, with relatively preserved neurons, "Viable Tissue". Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration., Conclusion: In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

Non-heme iron is an important element supporting the structure and functioning of biological tissues. Imbalance in non-heme iron can lead to different neurological disorders. Several MRI approaches have been developed for iron quantification relying either on the relaxation properties of MRI signal or measuring tissue magnetic susceptibility. Specific quantification of the non-heme iron can, however, be constrained by the presence of the heme iron in the deoxygenated blood and contribution of cellular composition. The goal of this paper is to introduce theoretical background and experimental MRI method allowing disentangling contributions of heme and non-heme irons simultaneously with evaluation of tissue neuronal density in the iron-rich basal ganglia. Our approach is based on the quantitative Gradient Recalled Echo (qGRE) MRI technique that allows separation of the total R2 * metric characterizing decay of GRE signal into tissue-specific (R2t * ) and the baseline blood oxygen level-dependent (BOLD) contributions. A combination with the QSM data (also available from the qGRE signal phase) allowed further separation of the tissue-specific R2t * metric in a cell-specific and non-heme-iron-specific contributions. It is shown that the non-heme iron contribution to R2t * relaxation can be described with the previously developed Gaussian Phase Approximation (GPA) approach. qGRE data were obtained from 22 healthy control participants (ages 26-63 years). Results suggest that the ferritin complexes are aggregated in clusters with an average radius about 100nm comprising approximately 2600 individual ferritin units. It is also demonstrated that the concentrations of heme and non-heme iron tend to increase with age. The strongest age effect was seen in the pallidum region, where the highest age-related non-heme iron accumulation was observed., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R 2 * maps from multi-gradient recalled echo (mGRE) MRI data., Methods: RoAR trains a convolutional neural network (CNN) to generate quantitative R 2 ∗ maps free from field inhomogeneity artifacts by adopting a self-supervised learning strategy given (a) mGRE magnitude images, (b) the biophysical model describing mGRE signal decay, and (c) preliminary-evaluated F-function accounting for contribution of macroscopic B0 field inhomogeneities. Importantly, no ground-truth R 2 * images are required and F-function is only needed during RoAR training but not application., Results: We show that RoAR preserves all features of R 2 * maps while offering significant improvements over existing methods in computation speed (seconds vs. hours) and reduced sensitivity to noise. Even for data with SNR = 5 RoAR produced R 2 * maps with accuracy of 22% while voxel-wise analysis accuracy was 47%. For SNR = 10 the RoAR accuracy increased to 17% vs. 24% for direct voxel-wise analysis., Conclusions: RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude-only mGRE data and eliminate their effect on R 2 ∗ measurements. RoAR training is based on the biophysical model and does not require ground-truth R 2 * maps. Since RoAR utilizes signal information not just from individual voxels but also accounts for spatial patterns of the signals in the images, it reduces the sensitivity of R 2 * maps to the noise in the data. These features plus high computational speed provide significant benefits for the potential usage of RoAR in clinical settings., (© 2020 International Society for Magnetic Resonance in Medicine.)

The goal of this study was to establish the feasibility of integrating focused ultrasound (FUS)-mediated delivery of 64 Cu-integrated gold nanoclusters ( 64 Cu-AuNCs) to the pons for in vivo quantification of the nanocluster brain uptake using positron emission tomography (PET) imaging. FUS was targeted at the pons for the blood-brain barrier (BBB) disruption in the presence of systemically injected microbubbles, followed by the intravenous injection of 64 Cu-AuNCs. The spatiotemporal distribution of the 64 Cu-AuNCs in the brain was quantified using in vivo microPET/CT imaging at different time points post injection. Following PET imaging, the accumulation of radioactivity in the pons was further confirmed using autoradiography and gamma counting, and the gold concentration was quantified using inductively coupled plasma-mass spectrometry (ICP-MS). We found that the noninvasive and localized BBB opening by the FUS successfully delivered the 64 Cu-AuNCs to the pons. We also demonstrated that in vivo real-time microPET/CT imaging was a reliable method for monitoring and quantifying the brain uptake of 64 Cu-AuNCs delivered by the FUS. This drug delivery platform that integrates FUS, radiolabeled nanoclusters, and PET imaging provides a new strategy for noninvasive and localized nanoparticle delivery to the pons with concurrent in vivo quantitative imaging to evaluate delivery efficiency. The long-term goal is to apply this drug delivery platform to the treatment of pontine gliomas., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: With the expanding applications of magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU), there is an urgent need for a convenient, reliable, and fast acoustic pressure field measurement method to aid treatment protocol design, ensure consistent and safe operation of the transducer, and facilitate regulatory approval of new techniques. Herein, we report a method for acoustic pressure field characterization of MR-HIFU systems with multi-element phased array transducers. This method integrates fiber-optic hydrophone measurements and electronic steering of the ultrasound beam with MRI-assisted HIFU focus alignment to the fiber tip., Methods: A clinical MR-HIFU system (Sonalleve V2, Profound Medical Inc., Mississauga, Canada) was used to assess the proposed method. A fiber-optic hydrophone was submerged in a degassed water bath, and the fiber tip location was traced using MRI. Subsequently, the nominal transducer focal point indicated on the MR-HIFU therapy planning software was positioned at the fiber tip, and the HIFU focus was electronically steered around the fiber tip within a 3D volume for 3D pressure field mapping, eliminating the need for an additional, expensive, and MRI-compatible 3D positioning stage. The peak positive and negative pressures were measured at the focus and validated using a standard hydrophone measurement setup outside the MRI magnet room., Results: We found that the initial MRI-assisted HIFU focus alignment had an average offset of 2.23 ± 1.33 mm from the fiber tip as identified by the 3D pressure field mapping. MRI guidance and electronic beam steering allowed 3D focus localization within ~ 1 h, i.e., faster than the typical time required using the standard laboratory setup (~ 3-4 h). Acoustic pressures measured using the proposed method were not significantly different from those obtained with the standard laboratory hydrophone measurements., Conclusions: In conclusion, our method offers a convenient, reliable, and fast acoustic pressure field characterization tool for MR-HIFU systems with phased array transducers., Competing Interests: N/AN/ADr. Ari Partanen is a paid consultant for Profound Medical Inc. The other authors have nothing to declare.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Purpose: With the expanding clinical application of magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU), acoustic field characterization of MR-HIFU systems is needed for facilitating regulatory approval and ensuring consistent and safe power output of HIFU transducers. However, the established acoustic field measurement techniques typically use equipment that cannot be used in a magnetic resonance imaging (MRI) suite, thus posing a challenge to the development and execution of HIFU acoustic field characterization techniques. In this study, we developed and characterized a technique for HIFU acoustic field calibration within the MRI magnet bore, and validated the technique with standard hydrophone measurements outside of the MRI suite., Methods: A clinical Philips MR-HIFU system (Sonalleve V2, Philips, Vantaa, Finland) was used to assess the proposed technique. A fiber-optic hydrophone with a long fiber was inserted through a 24-gauge angiocatheter and fixed inside a water tank that was placed on the HIFU patient table above the acoustic window. The long fiber allowed the hydrophone control unit to be placed outside of the magnet room. The location of the fiber tip was traced on MR images, and the HIFU focal point was positioned at the fiber tip using the MR-HIFU therapy planning software. To perform acoustic field mapping inside the magnet, the HIFU focus was positioned relative to the fiber tip using an MRI-compatible 5-axis robotic transducer positioning system embedded in the HIFU patient table. To perform validation measurements of the acoustic fields, the HIFU table was moved out of the MRI suite, and a standard laboratory hydrophone measurement setup was used to perform acoustic field measurements outside the magnetic field., Results: The pressure field scans along and across the acoustic beam path obtained inside the MRI bore were in good agreement with those obtained outside of the MRI suite. At the HIFU focus with varying nominal acoustic powers of 10-500 W, the peak positive pressure and peak negative pressure measured inside the magnet bore were 3.87-68.67 MPa and 3.56-12.06 MPa, respectively, while outside the MRI suite the corresponding pressures were 3.27-67.32 MPa and 3.06-12.39 MPa, respectively. There was no statistically significant difference (P > 0.05) between measurements inside the magnet bore and outside the MRI suite for the p + and p - at any acoustic power level. The spatial-peak pulse-average intensities (I SPPA ) for these powers were 312-17816 W/cm 2 and 220-15698 W/cm 2 for measurements inside and outside the magnet room, respectively. In addition, when the scanning step size of the HIFU focus was increased from 100 μm to 500 μm, the execution time for scanning a 4 × 4 mm 2 area decreased from 210 min to 10 min, the peak positive pressure decreased by 14%, the peak negative pressure decreased by 5%, and the lateral full width at half maximum dimension of pressure profiles increased from 1.15 mm to 1.55 mm., Conclusions: The proposed hydrophone measurement technique offers a convenient and reliable method for characterizing the acoustic fields of clinical MR-HIFU systems inside the magnet bore. The technique was validated for use by measurements outside the MRI suite using a standard hydrophone calibration technique. This technique can be a useful tool in MR-HIFU quality assurance and acoustic field assessment., (© 2017 American Association of Physicists in Medicine.)