Quantitative Characterization of Respiratory Patterns on Dynamic Higher Temporal Resolution MRI to Stratify Postacute Covid-19 Patients by Cardiopulmonary Symptom Burden.

BACKGROUND: Postacute Covid-19 patients commonly present with respiratory symptoms; however, a noninvasive imaging method for quantitative characterization of respiratory patterns is lacking. PURPOSE: To evaluate if quantitative characterization of respiratory pattern on free-breathing higher temporal resolution MRI stratifies patients by cardiopulmonary symptom burden. STUDY TYPE: Prospective analysis of retrospectively acquired data. SUBJECTS: A total of 37 postacute Covid-19 patients (25 male; median [interquartile range (IQR)] age: 58 [42-64] years; median [IQR] days from acute infection: 335 [186-449]). FIELD STRENGTH/SEQUENCE: 0.55 T/two-dimensional coronal true fast imaging with steady-state free precession (trueFISP) at higher temporal resolution. ASSESSMENT: Patients were stratified into three groups based on presence of no (N = 11), 1 (N = 14), or ≥2 (N = 14) cardiopulmonary symptoms, assessed using a standardized symptom inventory within 1 month of MRI. An automated lung postprocessing workflow segmented each lung in each trueFISP image (temporal resolution 0.2 seconds) and respiratory curves were generated. Quantitative parameters were derived including tidal lung area, rates of inspiration and expiration, lung area coefficient of variability (CV), and respiratory incoherence (departure from sinusoidal pattern) were. Pulmonary function tests were recorded if within 1 month of MRI. Qualitative assessment of respiratory pattern and lung opacity was performed by three independent readers with 6, 9, and 23 years of experience. STATISTICAL TESTS: Analysis of variance to assess differences in demographic, clinical, and quantitative MRI parameters among groups; univariable analysis and multinomial logistic regression modeling to determine features predictive of patient symptom status; Akaike information criterion to compare the quality of regression models; Cohen and Fleiss kappa (κ) to quantify inter-reader reliability. Two-sided 5% significance level was used. RESULTS: Tidal area and lung area CV were significantly higher in patients with two or more symptoms than in those with one or no symptoms (area: 15.4 cm2 vs. 12.9 cm2 vs. 12.8 cm2 ; CV: 0.072, 0.067, and 0.058). Respiratory incoherence was significantly higher in patients with two or more symptoms than in those with one or no symptoms (0.05 vs. 0.043 vs. 0.033). There were no significant differences in patient age (P = 0.19), sex (P = 0.88), lung opacity severity (P = 0.48), or pulmonary function tests (P = 0.35-0.97) among groups. Qualitative reader assessment did not distinguish between groups and showed slight inter-reader agreement (κ = 0.05-0.11). DATA CONCLUSION: Quantitative respiratory pattern measures derived from dynamic higher-temporal resolution MRI have potential to stratify patients by symptom burden in a postacute Covid-19 cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

An efficient 3D stack-of-stars turbo spin echo pulse sequence for simultaneous T2-weighted imaging and T2 mapping.

PURPOSE: To design a pulse sequence for efficient 3D T2-weighted imaging and T2 mapping. METHODS: A stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles and a flexible pseudorandom view ordering is proposed for simultaneous T2-weighted imaging and T2 mapping. An analytical framework is introduced for the selection of refocusing flip angles to maximize relative tissue contrast while minimizing T2 estimation errors and maintaining low specific absorption rate. Images at different echo times are generated using a subspace constrained iterative reconstruction algorithm. T2 maps are obtained by modeling the signal evolution using the extended phase graph model. The technique is evaluated using phantoms and demonstrated in vivo for brain, knee, and carotid imaging. RESULTS: Numerical simulations demonstrate an improved point spread function with the proposed pseudorandom view ordering compared to golden angle view ordering. Phantom experiments show that T2 values estimated from the stack-of-stars turbo spin echo pulse sequence with variable refocusing flip angles have good concordance with spin echo reference values. In vivo results show the proposed pulse sequence can generate qualitatively comparable T2-weighted images as conventional Cartesian 3D SPACE in addition to simultaneously generating 3D T2 maps. CONCLUSION: The proposed stack-of-stars turbo spin echo pulse sequence with pseudorandom view ordering and variable refocusing flip angles allows high resolution isotropic T2 mapping in clinically acceptable scan times. The optimization framework for the selection of refocusing flip angles improves T2 estimation accuracy while generating T2-weighted contrast comparable to conventional Cartesian imaging.

A multi-band double-inversion radial fast spin-echo technique for T2 cardiovascular magnetic resonance mapping of the heart.

BACKGROUND: Double inversion recovery (DIR) fast spin-echo (FSE) cardiovascular magnetic resonance (CMR) sequences are used clinically for black-blood T2-weighted imaging. However, these sequences suffer from slice inefficiency due to the non-selective inversion pulses. We propose a multi-band (MB) encoded DIR radial FSE (MB-DIR-RADFSE) technique to simultaneously excite two slices. This sequence has improved signal-to-noise ratio per unit time compared to a single slice excitation. It is also motion robust and enables the reconstruction of high-resolution black-blood T2-weighted images and T2 maps for the excited slices. METHODS: Hadamard encoded MB pulses were used in MB-DIR-RADFSE to simultaneously excite two slices. A principal component based iterative reconstruction was used to jointly reconstruct black-blood T2-weighted images and T2 maps. Phantom and in vivo experiments were performed to evaluate T2 mapping performance and results were compared to a T2-prepared balanced steady state free precession (bSSFP) method. The inter-segment variability of the T2 maps were assessed using data acquired on healthy subjects. A reproducibility study was performed to evaluate reproducibility of the proposed technique. RESULTS: Phantom experiments show that the T2 values estimated from MB-DIR-RADFSE are comparable to the spin-echo based reference, while T2-prepared bSSFP over-estimated T2 values. The relative contrast of the black-blood images from the multi-band scheme was comparable to those from a single slice acquisition. The myocardial segment analysis on 8 healthy subjects indicated a significant difference (p-value < 0.01) in the T2 estimates from the apical slice when compared to the mid-ventricular slice. The mean T2 estimate from 12 subjects obtained using T2-prepared bSSFP was significantly higher (p-value = 0.012) compared to MB-DIR-RADFSE, consistent with the phantom results. The Bland-Altman analysis showed excellent reproducibility between the MB-DIR-RADFSE measurements, with a mean T2 difference of 0.12 ms and coefficient of reproducibility of 2.07 in 15 clinical subjects. The utility of this technique is demonstrated in two subjects where the T2 maps show elevated values in regions of pathology. CONCLUSIONS: The use of multi-band pulses for excitation improves the slice efficiency of the double inversion fast spin-echo pulse sequence. The use of a radial trajectory and a joint reconstruction framework allows reconstruction of TE images and T2 maps for the excited slices.

Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI.

Random matrix theory (RMT) combined with principal component analysis has resulted in a widely used MPPCA noise mapping and denoising algorithm, that utilizes the redundancy in multiple acquisitions and in local image patches. RMT-based denoising relies on the uncorrelated identically distributed noise. This assumption breaks down after regridding of non-Cartesian sampling. Here we propose a Universal Sampling Denoising (USD) pipeline to homogenize the noise level and decorrelate the noise in non-Cartesian sampled k-space data after resampling to a Cartesian grid. In this way, the RMT approaches become applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the denoising pipeline on MRI data acquired using radial trajectories, including diffusion MRI of a numerical phantom and ex vivo mouse brains, as well as in vivo $T_2$ MRI of a healthy subject. The proposed pipeline robustly estimates noise level, performs noise removal, and corrects bias in parametric maps, such as diffusivity and kurtosis metrics, and $T_2$ relaxation time. USD stabilizes the variance, decorrelates the noise, and thereby enables the application of RMT-based denoising approaches to MR images reconstructed from any non-Cartesian data. In addition to MRI, USD may also apply to other medical imaging techniques involving non-Cartesian acquisition, such as PET, CT, and SPECT.

Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising.

INTRODUCTION: Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS: Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted images were acquired with 4 non-collinear directions, for which b = 50 s/mm 2 was used with 8 averages and b = 1000 s/mm 2 with 40 averages; 2 extra b = 50 s/mm 2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS: The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm 2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55-minute scan reconstructed with RMT. CONCLUSIONS: Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.

Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI.

Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming. In this work, we propose a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images. Transformation of images to an efficient representation has been proposed to improve the performance of subsequent classification tasks especially when working with limited labeled data. We investigate this by transforming the MPRAGE images to White-Matter-nulled MPRAGE (WMn-MPRAGE) contrast, previously shown to exhibit good intra-thalamic nuclear contrast, prior to the segmentation step. We trained two 3D segmentation frameworks using MPRAGE images (n = 35 subjects): (a) a native contrast segmentation (NCS) on MPRAGE images and (b) a synthesized contrast segmentation (SCS) where synthesized WMn-MPRAGE representation generated by a contrast synthesis CNN were used. Thalamic nuclei labels were generated using THOMAS, a multi-atlas segmentation technique proposed for WMn-MPRAGE images. The segmentation accuracy and clinical utility were evaluated on a healthy cohort (n = 12) and a cohort (n = 45) comprising of healthy subjects and patients with alcohol use disorder (AUD), respectively. Both the segmentation CNNs yielded comparable performances on most thalamic nuclei with Dice scores greater than 0.84 for larger nuclei and at least 0.7 for smaller nuclei. However, for some nuclei, the SCS CNN yielded significant improvements in Dice scores (medial geniculate nucleus, P = 0.003, centromedian nucleus, P = 0.01) and percent volume difference (ventral anterior, P = 0.001, ventral posterior lateral, P = 0.01) over NCS. In the AUD cohort, the SCS CNN demonstrated a significant atrophy in ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P = 0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS CNN showed spurious atrophy of the ventral posterior lateral nucleus. CNN-based segmentation of thalamic nuclei provides a fast and automated technique for thalamic nuclei prediction in MPRAGE images. The transformation of images to an efficient representation, such as WMn-MPRAGE, can provide further improvements in segmentation performance.