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1. Effect of radiofrequency shield diameter on signal‐to‐noise ratio at ultra‐high field MRI

   https://doi.org/10.1002/mrm.28670  

   Zhang, Bei ; Adriany, Gregor ; Delabarre, Lance ; Radder, Jerahmie ; Lagore, Russell ; Rutt, Brian ; Yang, Qing X. ; Ugurbil, Kamil ; Lattanzi, Riccardo ( January 2021 , Magnetic Resonance in Medicine)

   In this work, we investigated how the position of the radiofrequency (RF) shield can affect the signal‐to‐noise ratio (SNR) of a receive RF coil. Our aim was to obtain physical insight for the design of a 10.5T 32‐channel head coil, subject to the constraints on the diameter of the RF shield imposed by the head gradient coil geometry.

   We used full‐wave numerical simulations to investigate how the SNR of an RF receive coil depends on the diameter of the RF shield at ultra‐high magnetic field (UHF) strengths (≥7T).

   Our simulations showed that there is an SNR‐optimal RF shield size at UHF strength, whereas at low field the SNR monotonically increases with the shield diameter. For a 32‐channel head coil at 10.5T, an optimally sized RF shield could act as a cylindrical waveguide and increase the SNR in the brain by 27% compared to moving the shield as far as possible from the coil. Our results also showed that a separate transmit array between the RF shield and the receive array could considerably reduce SNR even if they are decoupled.

   At sufficiently high magnetic field strength, the design of local RF coils should be optimized together with the design of the RF shield to benefit from both near field and resonant modes.

    

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2. Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data

   https://doi.org/10.1177/19714009221122171  

   Meyer, Nolan K. ; Kang, Daehun ; Black, David F. ; Campeau, Norbert G. ; Welker, Kirk M. ; Gray, Erin M. ; In, Myung-Ho ; Shu, Yunhong ; Huston III, John ; Bernstein, Matt A. ; et al ( September 2022 , The Neuroradiology Journal)

   This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.

   Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.

   fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( p = 4.88×10^–4to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.

   LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

    

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3. Disentangling the effects of high permittivity materials on signal optimization and sample noise reduction via ideal current patterns

   https://doi.org/10.1002/mrm.27554  

   Vaidya, Manushka V. ; Sodickson, Daniel K. ; Collins, Christopher M. ; Lattanzi, Riccardo ( November 2018 , Magnetic Resonance in Medicine)

   To investigate how high‐permittivity materials (HPMs) can improve SNR when placed between MR detectors and the imaged body.

   We used a simulation framework based on dyadic Green’s functions to calculate the electromagnetic field inside a uniform dielectric sphere at 7 Tesla, with and without a surrounding layer of HPM. SNR‐optimizing (ideal) current patterns were expressed as the sum of signal‐optimizing (signal‐only) current patterns and dark mode current patterns that minimize sample noise while contributing nothing to signal. We investigated how HPM affects the shape and amplitude of these current patterns, sample noise, and array SNR.

   Ideal and signal‐only current patterns were identical for a central voxel. HPMs introduced a phase shift into these patterns, compensating for signal propagation delay in the HPMs. For an intermediate location within the sphere, dark mode current patterns were present and illustrated the mechanisms by which HPMs can reduce sample noise. High‐amplitude signal‐only current patterns were observed for HPM configurations that shield the electromagnetic field from the sample. For coil arrays, these configurations corresponded to poor SNR in deep regions but resulted in large SNR gains near the surface due to enhanced fields in the vicinity of the HPM. For very high relative permittivity values, HPM thicknesses corresponding to even multiples of λ/4 resulted in coil SNR gains throughout the sample.

   HPMs affect both signal sensitivity and sample noise. Lower amplitude signal‐only optimal currents corresponded to higher array SNR performance and could guide the design of coils integrated with HPM.

    

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4. The Cerebellum Is Sensitive to the Lexical Properties of Words During Spoken Language Comprehension

   https://doi.org/10.1162/nol_a_00126  

   Mechtenberg, Hannah ; Heffner, Christopher C. ; Myers, Emily B. ; Guediche, Sara ( February 2024 , Neurobiology of Language)

   Over the past few decades, research into the function of the cerebellum has expanded far beyond the motor domain. A growing number of studies are probing the role of specific cerebellar subregions, such as Crus I and Crus II, in higher-order cognitive functions including receptive language processing. In the current fMRI study, we show evidence for the cerebellum’s sensitivity to variation in two well-studied psycholinguistic properties of words—lexical frequency and phonological neighborhood density—during passive, continuous listening of a podcast. To determine whether, and how, activity in the cerebellum correlates with these lexical properties, we modeled each word separately using an amplitude-modulated regressor, time-locked to the onset of each word. At the group level, significant effects of both lexical properties landed in expected cerebellar subregions: Crus I and Crus II. The BOLD signal correlated with variation in each lexical property, consistent with both language-specific and domain-general mechanisms. Activation patterns at the individual level also showed that effects of phonological neighborhood and lexical frequency landed in Crus I and Crus II as the most probable sites, though there was activation seen in other lobules (especially for frequency). Although the exact cerebellar mechanisms used during speech and language processing are not yet evident, these findings highlight the cerebellum’s role in word-level processing during continuous listening.

    

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5. Deep learning‐based Accelerated and Noise‐Suppressed Estimation (DANSE) of quantitative Gradient‐Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties

   https://doi.org/10.1002/nbm.4883  

   Kahali, Sayan ; Kothapalli, Satya V. V. N. ; Xu, Xiaojian ; Kamilov, Ulugbek S. ; Yablonskiy, Dmitriy A. ( December 2022 , NMR in Biomedicine)

   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 ofR2t*andR2’maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B₀) magnetic field inhomogeneities directly from the gradient‐recalled echo (GRE) magnitude images. TheR2t*andR2’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 B₀‐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‐generatedR2t*andR2’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.

    

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