Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction.

PURPOSE: To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep-DSP). METHODS: Deep-DSP is proposed to directly predict EMI-free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U-Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI-free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI-free MR signals from data acquired by MRI receive and sensing coils during the MR signal-acquisition window. This strategy was evaluated on an ultralow-field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole-body scanner with incomplete RF shielding. RESULTS: Deep-DSP accurately predicted EMI-free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. CONCLUSION: Deep-DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient-friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep-DSP framework is computationally simple and efficient.

Cholecystokinin receptor antagonist challenge elicits brain-wide functional connectome modulation with micronetwork hippocampal decreased calcium transients.

The cortical distribution and functional role of cholecystokinin (CCK) are largely unknown. Here, a CCK receptor antagonist challenge paradigm was developed to assess functional connectivity and neuronal responses. Structural-functional magnetic resonance imaging and calcium imaging were undertaken in environmental enrichment (EE) and standard environment (SE) groups (naïve adult male mice, n = 59, C57BL/B6J, P = 60). Functional connectivity network-based statistics and pseudo-demarcation Voronoi tessellations to cluster calcium signals were used to derive region of interest metrics based on calcium transients, firing rate, and location. The CCK challenge elicited robust changes to structural-functional networks, decreased neuronal calcium transients, and max firing rate (5 s) of dorsal hippocampus in SE mice. However, the functional changes were not observed in EE mice, while the decreased neuronal calcium transients and max firing rate (5 s) were similar to SE mice. Decreased gray matter alterations were observed in multiple brain regions in the SE group due to CCK challenge, while no effect was observed in the EE group. The networks most affected by CCK challenge in SE included within isocortex, isocortex to olfactory, isocortex to striatum, olfactory to midbrain, and olfactory to thalamus. The EE group did not experience network changes in functional connectivity due to CCK challenge. Interestingly, calcium imaging revealed a significant decrease in transients and max firing rate (5 s) in the dorsal CA1 hippocampus subregion after CCK challenge in EE. Overall, CCK receptor antagonists affected brain-wide structural-functional connectivity within the isocortex, in addition to eliciting decreased neuronal calcium transients and max firing rate (5 s) in CA1 of the hippocampus. Future studies should investigate the CCK functional networks and how these processes affect isocortex modulation. Significance Statement  Cholecystokinin is a neuropeptide predominately found in the gastrointestinal system. Albeit abundantly expressed in neurons, the role and distribution of cholecystokinin are largely unknown. Here, we demonstrate cholecystokinin affects brain-wide structural-functional networks within the isocortex. In the hippocampus, the cholecystokinin receptor antagonist challenge decreases neuronal calcium transients and max firing rate (5 s) in CA1. We further demonstrate that mice in environmental enrichment do not experience functional network changes to the CCK receptor antagonist challenge. Environmental enrichment may afford protection to the alterations observed in control mice due to CCK. Our results suggest that cholecystokinin is distributed throughout the brain, interacts in the isocortex, and demonstrates an unexpected functional network stability for enriched mice.

Hippocampus Modulates Vocalizations Responses at Early Auditory Centers.

Despite its prominence in learning and memory, hippocampal influence in early auditory processing centers remains unknown. Here, we examined how hippocampal activity modulates sound-evoked responses in the auditory midbrain and thalamus using optogenetics and functional MRI (fMRI) in rodents. Ventral hippocampus (vHP) excitatory neuron stimulation at 5 Hz evoked robust hippocampal activity that propagates to the primary auditory cortex. We then tested 5 Hz vHP stimulation paired with either natural vocalizations or artificial/noise acoustic stimuli. vHP stimulation enhanced auditory responses to vocalizations (with a negative or positive valence) in the inferior colliculus, medial geniculate body, and auditory cortex, but not to their temporally reversed counterparts (artificial sounds) or broadband noise. Meanwhile, pharmacological vHP inactivation diminished response selectivity to vocalizations. These results directly reveal the large-scale hippocampal participation in natural sound processing at early centers of the ascending auditory pathway. They expand our present understanding of hippocampus in global auditory networks.

Parallel imaging reconstruction using spatial nulling maps.

PURPOSE: To develop a robust parallel imaging reconstruction method using spatial nulling maps (SNMs). METHODS: Parallel reconstruction using null operations (PRUNO) is a k-space reconstruction method where a k-space nulling system is derived using null-subspace bases of the calibration matrix. ESPIRiT reconstruction extends the PRUNO subspace concept by exploiting the linear relationship between signal-subspace bases and spatial coil sensitivity characteristics, yielding a hybrid-domain approach. Yet it requires empirical eigenvalue thresholding to mask the coil sensitivity information and is sensitive to signal- and null-subspace division. In this study, we combine the concepts of null-subspace PRUNO and hybrid-domain ESPIRiT to provide a more robust reconstruction method that extracts null-subspace bases of calibration matrix to calculate image-domain SNMs. Multi-channel images are reconstructed by solving an image-domain nulling system formed by SNMs that contain both coil sensitivity and finite image support information, therefore, circumventing the masking-related procedure. The proposed method was evaluated with multi-channel 2D brain and knee data and compared to ESPIRiT. RESULTS: The proposed hybrid-domain method produced quality reconstruction highly comparable to ESPIRiT with optimal manual masking. It involved no masking-related manual procedure and was tolerant of the actual division of null- and signal-subspace. Spatial regularization could be also readily incorporated to reduce noise amplification as in ESPIRiT. CONCLUSION: We provide an efficient hybrid-domain reconstruction method using multi-channel SNMs that are calculated from coil calibration data. It eliminates the need for coil sensitivity masking and is relatively insensitive to subspace separation, therefore, presenting a robust parallel imaging reconstruction procedure in practice.

Pushing the limits of low-cost ultra-low-field MRI by dual-acquisition deep learning 3D superresolution.

PURPOSE: Recent development of ultra-low-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data. METHODS: A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T1 -weighted and T2 -weighted imaging were trained with 3D ULF image data sets synthesized from the high-resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3-mm acquisition resolution in healthy volunteers, young and old, as well as patients. RESULTS: The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5-mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI. CONCLUSION: The proposed dual-acquisition 3D superresolution approach advances ULF MRI for quality brain imaging through deep learning of high-field brain data. Such strategy can empower ULF MRI for low-cost brain imaging, especially in point-of-care scenarios or/and in low-income and mid-income countries.

Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.

PURPOSE: To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning. METHODS: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. RESULTS: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. CONCLUSION: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol-specific data.

Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI.

At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict, and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI-sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A convolution neural network model is trained using the EMI characterization data to relate EMI signals detected by EMI-sensing coils to EMI signals in the MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by the MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low-cost ultralow-field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal-to-noise ratios that were comparable with those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., spectral domain transfer function and external dynamic interference estimation and removal [EDITER] methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding-free MRI. The concept is relatively simple and is potentially applicable to other RF signal detection scenarios in the presence of external and/or internal EMI.

Environmental enrichment leads to behavioral circadian shifts enhancing brain-wide functional connectivity between sensory cortices and eliciting increased hippocampal spiking.

Environmental enrichment induces widespread neuronal changes, but the initiation of the cascade is unknown. We ascertained the critical period of divergence between environmental enriched (EE) and standard environment (SE) mice using continuous infrared (IR) videography, functional magnetic resonance imaging (fMRI), and neuron level calcium imaging. Naïve adult male mice (n = 285, C57BL/6J, postnatal day 60) were divided into SE and EE groups. We assessed the linear time-series of motion activity using a novel structural break test which examined the dataset for change in circadian and day-by-day motion activity. fMRI was used to map brain-wide response using a functional connectome analysis pipeline. Awake calcium imaging was performed on the dorsal CA1 pyramidal layer. We found the preeminent behavioral feature in EE was a forward shift in the circadian rhythm, prolongation of activity in the dark photoperiod, and overall decreased motion activity. The crepuscular period of dusk was seen as the critical period of divergence between EE and SE mice. The functional processes at dusk in EE included increased functional connectivity in the visual cortex, motor cortex, retrosplenial granular cortex, and cingulate cortex using seed-based analysis. Network based statistics found a modulated functional connectome in EE concentrated in two hubs: the hippocampal formation and isocortical network. These hubs experienced a higher node degree and significant enhanced edge connectivity. Calcium imaging revealed increased spikes per second and maximum firing rate in the dorsal CA1 pyramidal layer, in addition to location (anterior-posterior and medial-lateral) effect size differences between EE and SE. The emergence of functional-neuronal changes due to enrichment consisted of enhanced hippocampal-isocortex functional connectivity and CA1 neuronal increased spiking linked to a circadian shift during the dusk period. Future studies should explore the molecular consequences of enrichment inducing shifts in the circadian period.

Joint denoising of diffusion-weighted images via structured low-rank patch matrix approximation.

PURPOSE: To develop a joint denoising method that effectively exploits natural information redundancy in MR DWIs via low-rank patch matrix approximation. METHODS: A denoising method is introduced to jointly reduce noise in DWI dataset by exploiting nonlocal self-similarity as well as local anatomical/structural similarity within multiple 2D DWIs acquired with the same anatomical geometry but different diffusion directions. Specifically, for each small 3D reference patch sliding within 2D DWI, nonlocal but similar patches are searched by matching image contents within entire DWI dataset and then structured into a patch matrix. The resulting patch matrices are denoised by enforcing low-rankness via weighted nuclear norm minimization and finally are back-distributed to DWI space. The proposed procedure was evaluated with simulated and in vivo brain diffusion tensor imaging (DTI) datasets and then compared to existing Marchenko-Pastur principal component analysis denoising method. RESULTS: The proposed method achieved significant noise reduction while preserving structural details in all DWIs for both simulated and in vivo datasets. Quantitative evaluation of error maps demonstrated it consistently outperformed Marchenko-Pastur principal component analysis method. Further, the denoised DWIs led to substantially improved DTI parametric maps, exhibiting significantly less noise and revealing more microstructural details. CONCLUSION: The proposed method denoises DWI dataset by utilizing both nonlocal self-similarity and local structural similarity within DWI dataset. This weighted nuclear norm minimization-based low-rank patch matrix denoising approach is effective and highly applicable to various diffusion MRI applications, including DTI as a postprocessing procedure.

Partial Fourier reconstruction of complex MR images using complex-valued convolutional neural networks.

PURPOSE: To provide a complex-valued deep learning approach for partial Fourier (PF) reconstruction of complex MR images. METHODS: Conventional PF reconstruction methods, such as projection onto convex sets (POCS), uses low-resolution image phase information from the central symmetrically sampled k-space for image reconstruction. However, this smooth phase constraint undermines the phase estimation accuracy in presence of rapid local phase variations, causing image artifacts and limiting the extent of PF reconstruction. Using both magnitude and phase characteristics in big complex image datasets, we propose a complex-valued deep learning approach with an unrolled network architecture for PF reconstruction that iteratively reconstructs PF sampled data and enforces data consistency. We evaluate our approach for reconstructing both spin-echo and gradient-echo data. RESULTS: The proposed method outperformed the iterative POCS PF reconstruction method. It produced better artifact suppression and recovery of both image magnitude and phase details in presence of local phase changes. No noise amplification was observed even for highly PF reconstruction. Moreover, the network trained on axial brain data could reconstruct sagittal and coronal brain and knee data. This method could be extended to 2D PF reconstruction and joint multi-slice PF reconstruction. CONCLUSION: Our proposed method can effectively reconstruct MR data even at low PF fractions, yielding high-fidelity magnitude and phase images. It presents a valuable alternative to conventional PF reconstruction, especially for phase-sensitive 2D or 3D MRI applications.

Calibrationless multi-slice Cartesian MRI via orthogonally alternating phase encoding direction and joint low-rank tensor completion.

We propose a multi-slice acquisition with orthogonally alternating phase encoding (PE) direction and subsequent joint calibrationless reconstruction for accelerated multiple individual 2D slices or multi-slice 2D Cartesian MRI. Specifically, multi-slice multi-channel data are first acquired with random or uniform PE undersampling while orthogonally alternating PE direction between adjacent slices. They are then jointly reconstructed through a recently developed low-rank multi-slice Hankel tensor completion (MS-HTC) approach. The proposed acquisition and reconstruction strategy was evaluated with human brain MR data. It effectively suppressed aliasing artifacts even at high acceleration factor, outperforming the existing MS-HTC approach, where PE direction is the same between adjacent slices. More importantly, the new strategy worked robustly with uniform undersampling or random undersampling without any consecutive central k-space lines. In summary, our proposed multi-slice MRI strategy exploits both coil sensitivity and image content similarities across adjacent slices. Orthogonally alternating PE direction among slices substantially facilitates the low-rank completion process and improves image reconstruction quality. This new strategy is applicable to uniform and random PE undersampling. It can be easily implemented in practice for Cartesian parallel imaging of multiple individual 2D slices without any coil sensitivity calibration.

Optogenetic fMRI interrogation of brain-wide central vestibular pathways.

Blood oxygen level-dependent functional MRI (fMRI) constitutes a powerful neuroimaging technology to map brain-wide functions in response to specific sensory or cognitive tasks. However, fMRI mapping of the vestibular system, which is pivotal for our sense of balance, poses significant challenges. Physical constraints limit a subject's ability to perform motion- and balance-related tasks inside the scanner, and current stimulation techniques within the scanner are nonspecific to delineate complex vestibular nucleus (VN) pathways. Using fMRI, we examined brain-wide neural activity patterns elicited by optogenetically stimulating excitatory neurons of a major vestibular nucleus, the ipsilateral medial VN (MVN). We demonstrated robust optogenetically evoked fMRI activations bilaterally at sensorimotor cortices and their associated thalamic nuclei (auditory, visual, somatosensory, and motor), high-order cortices (cingulate, retrosplenial, temporal association, and parietal), and hippocampal formations (dentate gyrus, entorhinal cortex, and subiculum). We then examined the modulatory effects of the vestibular system on sensory processing using auditory and visual stimulation in combination with optogenetic excitation of the MVN. We found enhanced responses to sound in the auditory cortex, thalamus, and inferior colliculus ipsilateral to the stimulated MVN. In the visual pathway, we observed enhanced responses to visual stimuli in the ipsilateral visual cortex, thalamus, and contralateral superior colliculus. Taken together, our imaging findings reveal multiple brain-wide central vestibular pathways. We demonstrate large-scale modulatory effects of the vestibular system on sensory processing.

Neural activity temporal pattern dictates long-range propagation targets.

Brain possesses a complex spatiotemporal architecture for efficient information processing and computing. However, it remains unknown how neural signal propagates to its intended targets brain-wide. Using optogenetics and functional MRI, we arbitrarily initiated various discrete neural activity pulse trains with different temporal patterns and revealed their distinct long-range propagation targets within the well-defined, topographically organized somatosensory thalamo-cortical circuit. We further observed that such neural activity propagation over long range could modulate brain-wide sensory functions. Electrophysiological analysis indicated that distinct propagation pathways arose from system level neural adaptation and facilitation in response to the neural activity temporal characteristics. Together, our findings provide fundamental insights into the long-range information transfer and processing. They directly support that temporal coding underpins the whole brain functional architecture in presence of the vast and relatively static anatomical architecture.

Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework.

PURPOSE: To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework. METHODS: A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T1 -weighted, T2 -weighted, fluid-attenuated inversion recovery, and T1 -weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes. RESULTS: The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated. CONCLUSION: The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration.

Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion.

PURPOSE: To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k-space data. METHODS: Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low-rank tensor completion. Specifically, the multichannel k-space data from multiple slices are constructed into a block-wise Hankel tensor and iteratively updated by promoting tensor low-rankness through higher-order SVD. This multislice block-wise Hankel tensor completion was implemented for 2D spiral and Cartesian k-space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. RESULTS: The proposed multislice block-wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. CONCLUSION: Our proposed multislice block-wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.

Physiological system analysis of the kidney by high-temporal-resolution T2∗ monitoring of an oxygenation step response.

PURPOSE: Examine the feasibility of characterizing the regulation of renal oxygenation using high-temporal-resolution monitoring of the T2∗ response to a step-like oxygenation stimulus. METHODS: For T2∗ mapping, multi-echo gradient-echo imaging was used (temporal resolution = 9 seconds). A step-like renal oxygenation challenge was applied involving sequential exposure to hyperoxia (100% O2 ), hypoxia (10% O2 + 90% N2 ), and hyperoxia (100% O2 ). In vivo experiments were performed in healthy rats (N = 10) and in rats with bilateral ischemia-reperfusion injury (N = 4). To assess the step response of renal oxygenation, a second-order exponential model was used (model parameters: amplitude [A], time delay [Δt], damping constant [D], and period of the oscillation [T]) for renal cortex, outer stripe of the outer medulla, inner stripe of the outer medulla, and inner medulla. RESULTS: The second-order exponential model permitted us to model the exponential T2∗ recovery and the superimposed T2∗ oscillation following renal oxygenation stimulus. The in vivo experiments revealed a difference in Douter medulla between healthy controls (D < 1, indicating oscillatory recovery) and ischemia-reperfusion injury (D > 1, reflecting aperiodic recovery). The increase in Douter medulla by a factor of 3.7 (outer stripe of the outer medulla) and 10.0 (inner stripe of the outer medulla) suggests that this parameter might be rather sensitive to (patho)physiological oxygenation changes. CONCLUSION: This study demonstrates the feasibility of monitoring the dynamic oxygenation response of renal tissues to a step-like oxygenation challenge using high-temporal-resolution T2∗ mapping. Our results suggest that the implemented system analysis approach may help to unlock questions regarding regulation of renal oxygenation, with the ultimate goal of providing imaging means for diagnostics and therapy of renal diseases.

Multiparametric MR Investigation of Proteoglycan Diffusivity, T2 Relaxation, and Concentration in an Ex Vivo Model of Intervertebral Disc Degeneration.

BACKGROUND: Proteoglycan (PG) is a major component of the intervertebral disc extracellular matrix (ECM) that acts to hydrate the disc nucleus. Early detection of PG degradation is valuable for both diagnosis and preclinical research of intervertebral disc degeneration (IVDD). PURPOSE: To compare different MR techniques for detecting early degradative changes of PG in IVDD. STUDY TYPE: Prospective. PHANTOM/SPECIMEN: Glycosaminoglycan (GAG) phantom/bovine discs with papain injection and human cadaveric discs. FIELD STRENGTH/SEQUENCES: 7T/diffusion-weighted MR spectroscopy (DW-MRS), T2 -weighted MRS (T2 W-MRS), and chemical exchange saturation transfer (CEST) imaging. ASSESSMENT: DW-MRS, T2 W-MRS, and CEST imaging were applied longitudinally to measure PG diffusivity, T2 value, overall content, and spatial distribution in the disc nucleus with enzyme-induced proteolytic ECM degradation (n = 8). Similar MR measurements were applied in GAG phantom and human cadaveric discs with different levels of degeneration (n = 6). STATISTICAL TESTS: T-tests were conducted to measure the differences of PG properties between pre- and post-enzyme injection. Linear regression and mixed-effects models were used to assess the associations among different PG properties as well as the degeneration grades in human cadaveric discs. RESULTS: In bovine discs, PG diffusivity increased most rapidly after the enzyme was injected into the disc nucleus (12 hours postinjection, t = 5.76, P = 0.0007). The PG T2 value did not change significantly (t < 1.54, P > 0.17 for all timepoints) during ECM degradation and was not associated with PG diffusivity (t = 0.06, P = 0.95). PG distribution change was more rapid than overall PG content and was strongly associated with PG diffusivity increase (t = -9.25, P < 1 × 10-8 ). In severely degenerated human cadaveric discs, the PG ADCs and T2 values were both associated with degeneration grades. DATA CONCLUSION: PG diffusivity is a direct biomarker for early ECM degradation, while PG distribution can be an indirect biomarker for early IVDD. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1390-1400.

Low-frequency hippocampal-cortical activity drives brain-wide resting-state functional MRI connectivity.

The hippocampus, including the dorsal dentate gyrus (dDG), and cortex engage in bidirectional communication. We propose that low-frequency activity in hippocampal-cortical pathways contributes to brain-wide resting-state connectivity to integrate sensory information. Using optogenetic stimulation and brain-wide fMRI and resting-state fMRI (rsfMRI), we determined the large-scale effects of spatiotemporal-specific downstream propagation of hippocampal activity. Low-frequency (1 Hz), but not high-frequency (40 Hz), stimulation of dDG excitatory neurons evoked robust cortical and subcortical brain-wide fMRI responses. More importantly, it enhanced interhemispheric rsfMRI connectivity in various cortices and hippocampus. Subsequent local field potential recordings revealed an increase in slow oscillations in dorsal hippocampus and visual cortex, interhemispheric visual cortical connectivity, and hippocampal-cortical connectivity. Meanwhile, pharmacological inactivation of dDG neurons decreased interhemispheric rsfMRI connectivity. Functionally, visually evoked fMRI responses in visual regions also increased during and after low-frequency dDG stimulation. Together, our results indicate that low-frequency activity robustly propagates in the dorsal hippocampal-cortical pathway, drives interhemispheric cortical rsfMRI connectivity, and mediates visual processing.

Thalamic low frequency activity facilitates resting-state cortical interhemispheric MRI functional connectivity.

Blood-oxygen-level-dependent (BOLD) resting-state functional MRI (rsfMRI) has emerged as a valuable tool to map complex brain-wide functional networks, predict cognitive performance and identify biomarkers for neurological diseases. However, interpreting these findings poses challenges, as the neural basis of rsfMRI connectivity remains poorly understood. The thalamus serves as a relay station and modulates diverse long-range cortical functional integrations, yet few studies directly interrogate its role in brain-wide rsfMRI connectivity. Utilizing a multi-modal approach of rsfMRI, optogenetic stimulation and multi-depth cortical electrophysiology recording, we examined whether and how the somatosensory thalamus contributes to cortical interhemispheric rsfMRI connectivity. We found that low frequency (1 Hz) optogenetic stimulation of somatosensory-specific ventral posteromedial (VPM) thalamocortical excitatory neurons increased the interhemispheric rsfMRI connectivity in all examined sensory cortices, somatosensory, visual and auditory, and the local intrahemispheric BOLD activity at infraslow frequency (0.01-0.1 Hz). In parallel, multi-depth local field potential recordings at bilateral primary somatosensory cortices revealed increased interhemispheric correlations of low frequency neural oscillations (i.e., mainly < 10 Hz) at all cortical layers. Meanwhile, pharmacologically inhibiting VPM thalamocortical neurons decreased interhemispheric rsfMRI connectivity and local intrahemispheric infraslow BOLD activity in all sensory cortices. Taken together, our findings demonstrate that low frequency activities in the thalamo-cortical network contribute to brain-wide rsfMRI connectivity, highlighting the thalamus as a pivotal region that underlies rsfMRI connectivity.

MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks.

PURPOSE: To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. THEORY AND METHODS: Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. RESULTS: The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. CONCLUSION: The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.