Anterior occipital condyle screw placement through the endonasal corridor: proof of concept study with cadaveric analysis.

PURPOSE: Odontoidectomy for ventral compressive pathology may result in O-C1 and/or C1-2 instability. Same-stage endonasal C1-2 spinal fusion has been advocated to eliminate risks associated with separate-stage posterior approaches. While endonasal methods for C1 instrumentation and C1-2 trans-articular stabilization exist, no hypothetical construct for endonasal occipital instrumentation has been validated. We provide an anatomic description of anterior occipital condyle (AOC) screw endonasal placement as proof-of-concept for endonasal craniocervical stabilization. METHODS: Eight adult, injected cadaveric heads were studied for placing 16 AOC screws endonasally. Thin-cut CT was used for registration. After turning a standard inferior U-shaped nasopharyngeal flap endonasally, 4 mm × 22 mm AOC screws were placed with a 0° driver using neuronavigation. Post-placement CT scans were obtained to determine: site-of-entry, measured from the endonasal projection of the medial O-C1 joint; screw angulation in sagittal and axial planes, proximity to critical structures. RESULTS: Average site-of-entry was 6.88 mm lateral and 9.74 mm rostral to the medial O-C1 joint. Average angulation in the sagittal plane was 0.16° inferior to the palatal line. Average angulation in the axial plane was 23.97° lateral to midline. Average minimum screw distances from the jugular bulb and hypoglossal canal were 4.80 mm and 1.55 mm. CONCLUSION: Endonasal placement of AOC screws is feasible using a 0° driver. Our measurements provide useful parameters to guide optimal placement. Given proximity of hypoglossal canal and jugular bulb, neuronavigation is recommended. Biomechanical studies will ultimately be necessary to evaluate the strength of AOC screws with plate-screw constructs utilizing endonasal C1 lateral mass or C1-2 trans-articular screws as inferior fixation points.

Generation of Cross-Specialty Consensus Statements on Soft Tissue Management via a Modified Delphi Method.

BACKGROUND: Soft tissue management (STM) training programs for surgeons are largely tradition based, and substantial differences exist among different surgical specialties. The lack of comprehensive and systematic clinical evidence on how surgical techniques and implants affect soft tissue healing makes it difficult to develop evidence-based curricula. As a curriculum development group (CDG), we set out to find common grounds in the form of a set of consensus statements to serve as the basis for surgical soft tissue education. METHODS: Following a backward planning process and Kern's six-step approach, the group selected 13 topics to build a cross-specialty STM curriculum. A set of statements based on the curriculum topics were generated by the CDG through discussions and a literature review of three topics. A modified Delphi process including one round of pilot voting through a face-to-face CDG meeting and two rounds of web-based survey involving 22 panelists were utilized for the generation of consensus statements. RESULTS: Seventy-one statements were evaluated, and 56 statements reached the 80% consensus for "can be taught as is." CONCLUSIONS: Using a modified Delphi method, a set of cross-specialty consensus statements on soft tissue management were generated. These consensus statements can be used as a foundation for multi-specialty surgical education. Similar methods that combine expert experience and clinical evidence can be used to develop specialty-specific consensus on soft tissue handling.

Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications.

PURPOSE: Deep learning has had success with MRI reconstruction, but previously published works use real-valued networks. The few works which have tried complex-valued networks have not fully assessed their impact on phase. Therefore, the purpose of this work is to fully investigate end-to-end complex-valued convolutional neural networks (CNNs) for accelerated MRI reconstruction and in several phase-based applications in comparison to 2-channel real-valued networks. METHODS: Several complex-valued activation functions for MRI reconstruction were implemented, and their performance was compared. Complex-valued convolution was implemented and tested on an unrolled network architecture and a U-Net-based architecture over a wide range of network widths and depths with knee, body, and phase-contrast datasets. RESULTS: Quantitative and qualitative results demonstrated that complex-valued CNNs with complex-valued convolutions provided superior reconstructions compared to real-valued convolutions with the same number of trainable parameters for both an unrolled network architecture and a U-Net-based architecture, and for 3 different datasets. Complex-valued CNNs consistently had superior normalized RMS error, structural similarity index, and peak SNR compared to real-valued CNNs. CONCLUSION: Complex-valued CNNs can enable superior accelerated MRI reconstruction and phase-based applications such as fat-water separation, and flow quantification compared to real-valued convolutional neural networks.

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

PURPOSE: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. METHODS: We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l1 -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice. RESULTS: The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l1 -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l1 -ESPIRiT images. CONCLUSIONS: DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.

Free-breathing R2∗ mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI.

PURPOSE: To enable motion-robust, ungated, free-breathing R2∗ mapping of hepatic iron overload in children with 3D multi-echo UTE cones MRI. METHODS: A golden-ratio re-ordered 3D multi-echo UTE cones acquisition was developed with chemical-shift encoding (CSE). Multi-echo complex-valued source images were reconstructed via gridding and coil combination, followed by confounder-corrected R2∗ (=1/ T2∗ ) mapping. A phantom containing 15 different concentrations of gadolinium solution (0-300 mM) was imaged at 3T. 3D multi-echo UTE cones with an initial TE of 0.036 ms and Cartesian CSE-MRI (IDEAL-IQ) sequences were performed. With institutional review board approval, 85 subjects (81 pediatric patients with iron overload + 4 healthy volunteers) were imaged at 3T using 3D multi-echo UTE cones with free breathing (FB cones), IDEAL-IQ with breath holding (BH Cartesian), and free breathing (FB Cartesian). Overall image quality of R2∗ maps was scored by 2 blinded experts and compared by a Wilcoxon rank-sum test. For each pediatric subject, the paired R2∗ maps were assessed to determine if a corresponding artifact-free 15 mm region-of-interest (ROI) could be identified at a mid-liver level on both images. Agreement between resulting R2∗ quantification from FB cones and BH/FB Cartesian was assessed with Bland-Altman and linear correlation analyses. RESULTS: ROI-based regression analysis showed a linear relationship between gadolinium concentration and R2∗ in IDEAL-IQ (y = 8.83x - 52.10, R2 = 0.995) as well as in cones (y = 9.19x - 64.16, R2 = 0.992). ROI-based Bland-Altman analysis showed that the mean difference (MD) was 0.15% and the SD was 5.78%. However, IDEAL-IQ R2∗ measurements beyond 200 mM substantially deviated from a linear relationship for IDEAL-IQ (y = 5.85x + 127.61, R2 = 0.827), as opposed to cones (y = 10.87x - 166.96, R2 = 0.984). In vivo, FB cones R2∗ had similar image quality with BH and FB Cartesian in 15 and 42 cases, respectively. FB cones R2∗ had better image quality scores than BH and FB Cartesian in 3 and 21 cases, respectively, where BH/FB Cartesian exhibited severe ghosting artifacts. ROI-based Bland-Altman analyses were 2.23% (MD) and 6.59% (SD) between FB cones and BH Cartesian and were 0.21% (MD) and 7.02% (SD) between FB cones and FB Cartesian, suggesting a good agreement between FB cones and BH (FB) Cartesian R2∗ . Strong linear relationships were observed between BH Cartesian and FB cones (y = 1.00x + 1.07, R2 = 0.996) and FB Cartesian and FB cones (y = 0.98x + 1.68, R2 = 0.999). CONCLUSION: Golden-ratio re-ordered 3D multi-echo UTE Cones MRI enabled motion-robust, ungated, and free-breathing R2∗ mapping of hepatic iron overload, with comparable R2∗ measurements and image quality to BH Cartesian, and better image quality than FB Cartesian.

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.

The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast. This prospective study included 28 women undergoing clinically indicated breast MRI and six asymptomatic volunteers. In vivo studies were performed at 3 T and included DW-DESS-Cones, DW-DESS-Cartesian, DWI, and CE-MRI acquisitions. Phantom experiments (diffusion phantom, High Precision Devices) and simulations were performed to establish framework for contrast of DW-DESS-Cones in comparison to DWI in the breast. Motion artifacts of DW-DESS-Cones were measured with artifact-to-noise ratio in volunteers and patients. Lesion-to-fibroglandular tissue signal ratios were measured, lesions were categorized as hyperintense or hypointense, and an image quality observer study was performed in DW-DESS-Cones and DWI in patients. Effect of DW-DESS-Cones method on motion artifacts was tested by mixed-effects generalized linear model. Effect of DW-DESS-Cones on signal in phantom was tested by quadratic regression. Correlation was calculated between DW-DESS-Cones and DWI lesion-to-fibroglandular tissue signal ratios. Inter-observer agreement was assessed with Gwet's AC. Simulations predicted hyperintensity of lesions with DW-DESS-Cones but at a 3% to 67% lower degree than with DWI. Motion artifacts were reduced with DW-DESS-Cones versus DW-DESS-Cartesian (p < 0.05). Lesion-to-fibroglandular tissue signal ratios were not correlated between DW-DESS-Cones and DWI (r = 0.25, p = 0.38). Concordant hyperintensity/hypointensity was observed between DW-DESS-Cones and DWI in 11/14 lesions. DW-DESS-Cones improved sharpness, distortion, and overall image quality versus DWI. DW-DESS-Cones may be able to eliminate motion artifacts in the breast allowing for investigation of higher degrees of steady-state diffusion weighting. Malignant breast lesions in DW-DESS-Cones demonstrated hyperintensity with respect to surrounding tissue without an injection of contrast. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 1.

How Many Steps Per Day During the Early Postoperative Period are Associated With Patient-Reported Outcomes of Disability, Pain, and Opioid Use After Lumbar Spine Surgery?

OBJECTIVE: To investigate whether early postoperative walking is associated with "best outcome" and no opioid use at 1 year after lumbar spine surgery and establish a threshold for steps/day to inform clinical practice. DESIGN: Secondary analysis from randomized controlled trial. SETTING: Two academic medical centers in the United States. PARTICIPANTS: We enrolled 248 participants undergoing surgery for a degenerative lumbar spine condition (N=248). A total of 212 participants (mean age, 62.8±11.4y, 53.3% female) had valid walking data at baseline. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Disability (Oswestry Disability Index), back and leg pain (Brief Pain Inventory), and opioid use (yes vs no) were assessed at baseline and 1 year after surgery. "Best outcome" was defined as Oswestry Disability Index ≤20, back pain ≤2, and leg pain ≤2. Steps/day (walking) was assessed with an accelerometer worn for at least 3 days and 10 h/d at 6 weeks after spine surgery, which was considered as study baseline. Separate multivariable logistic regression analyses were conducted to determine the association between steps/day at 6 weeks and "best outcome" and no opioid use at 1-year. Receiver operating characteristic curves identified a steps/day threshold for achieving outcomes. RESULTS: Each additional 1000 steps/d at 6 weeks after spine surgery was associated with 41% higher odds of achieving "best outcome" (95% confidence interval [CI], 1.15-1.74) and 38% higher odds of no opioid use (95% CI, 1.09-1.76) at 1 year. Walking ≥3500 steps/d was associated with 3.75 times the odds (95% CI, 1.56-9.02) of achieving "best outcome" and 2.37 times the odds (95% CI, 1.07-5.24) of not using opioids. CONCLUSIONS: Walking early after surgery may optimize patient-reported outcomes after lumbar spine surgery. A 3500 steps/d threshold may serve as an initial recommendation during early postoperative counseling.

Role of psychosocial factors on the effect of physical activity on physical function in patients after lumbar spine surgery.

BACKGROUND: The purpose of this study was to investigate the longitudinal postoperative relationship between physical activity, psychosocial factors, and physical function in patients undergoing lumbar spine surgery. METHODS: We enrolled 248 participants undergoing surgery for a degenerative lumbar spine condition. Physical activity was measured using a triaxial accelerometer (Actigraph GT3X) at 6-weeks (6wk), 6-months (6M), 12-months (12M) and 24-months (24M) following spine surgery. Physical function (computerized adaptive test domain version of Patient-Reported Outcomes Measurement Information System) and psychosocial factors (pain self-efficacy, depression and fear of movement) were assessed at preoperative visit and 6wk, 6M, 12M and 24M after surgery. Structural equation modeling (SEM) techniques were utilized to analyze data, and results are represented as standardized regression weights (SRW). Overall SRW were computed across five imputed datasets to account for missing data. The mediation effect of each psychosocial factor on the effect of physical activity on physical function were computed [(SRW for effect of activity on psychosocial factor X SRW for effect of psychosocial factor on function) ÷ SRW for effect of activity on function]. Each SEM model was tested for model fit by assessing established fit indexes. RESULTS: The overall effect of steps per day on physical function (SRW ranged from 0.08 to 0.19, p<0.05) was stronger compared to the overall effect of physical function on steps per day (SRW ranged from non-existent to 0.14, p<0.01 to 0.3). The effect of steps per day on physical function and function on steps per day remained consistent after accounting for psychosocial factors in each of the mediation models. Depression and fear of movement at 6M mediated 3.4% and 5.4% of the effect of steps per day at 6wk on physical function at 12M, respectively. Pain self-efficacy was not a statistically significant mediator. CONCLUSIONS: The findings of this study suggest that the relationship between physical activity and physical function is stronger than the relationship of function to activity. However, future research is needed to examine whether promoting physical activity during the early postoperative period may result in improvement of long-term physical function. Since depression and fear of movement had a very small mediating effect, additional work is needed to investigate other potential mediating factors such as pain catastrophizing, resilience and exercise self-efficacy.

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

PURPOSE: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS: 3D iNAVs reconstructed using the DL model-based approach and conventional l1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS: We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions.

PURPOSE: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. THEORY AND METHODS: The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging. RESULTS: The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features. CONCLUSIONS: We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.

BACKGROUND: Current self-calibration and reconstruction methods for wave-encoded single-shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed. PURPOSE: To develop and investigate the clinical feasibility of data-driven self-calibration and reconstruction of wave-encoded SSFSE imaging for computation time reduction and quality improvement. STUDY TYPE: Prospective controlled clinical trial. SUBJECTS: With Institutional Review Board approval, the proposed method was assessed on 29 consecutive adult patients (18 males, 11 females, range, 24-77 years). FIELD STRENGTH/SEQUENCE: A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable 3.5× acceleration with full-Fourier acquisitions. Data-driven calibration of wave-encoding point-spread function (PSF) was developed using a trained deep neural network. Data-driven reconstruction was developed with another set of neural networks based on the calibrated wave-encoding PSF. Training of the calibration and reconstruction networks was performed on 15,783 2D wave-encoded SSFSE abdominal images. ASSESSMENT: Image quality of the proposed data-driven approach was compared independently and blindly with a conventional approach using iterative self-calibration and reconstruction with parallel imaging and compressed sensing by three radiologists on a scale from -2 to 2 for noise, contrast, sharpness, artifacts, and confidence. Computation time of these two approaches was also compared. STATISTICAL TESTS: Wilcoxon signed-rank tests were used to compare image quality and two-tailed t-tests were used to compare computation time with P values of under 0.05 considered statistically significant. RESULTS: An average 2.1-fold speedup in computation was achieved using the proposed method. The proposed data-driven self-calibration and reconstruction approach significantly reduced the perceived noise level (mean scores 0.82, P < 0.0001). DATA CONCLUSION: The proposed data-driven calibration and reconstruction achieved twice faster computation with reduced perceived noise, providing a fast and robust self-calibration and reconstruction for clinical abdominal SSFSE imaging. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:841-853.

Direct measurement of atrioventricular valve regurgitant jets using 4D flow cardiovascular magnetic resonance is accurate and reliable for children with congenital heart disease: a retrospective cohort study.

BACKGROUND: 3D-time resolved flow (4DF) cardiovascular magnetic resonance (CMR) with retrospective analysis of atrioventricular valve regurgitation (AVVR) allows for internal validation by multiple direct and indirect methods. Limited data exist on direct measurement of AVVR by 4DF CMR in pediatric congenital heart disease (CHD). We aimed to validate direct measurement of the AVVR jet as accurate and reliable compared to the volumetric method (clinical standard by 2D CMR) and as a superior method of internal validation than the annular inflow method. METHODS: We identified 44 consecutive patients with diverse CHD referred for evaluation of AVVR by CMR. 1.5 T or 3 T scanners, intravenous contrast, and a combination of parallel imaging and compressed sensing were used. Four methods of measuring AVVR volume (RVol) were used: volumetric method (VOL; the clinical standard) = stroke volume by 2D balanced steady-state free precession - semilunar valve forward flow (SLFF); annular inflow method (AIM) = atrioventricular valve forward flow [AVFF] - semilunar valve net flow (SLNF); and direct measurement (JET). AVFF was measured using static and retrospective valve tracking planes. SLFF, SLNF, AVFF, and JET were measured by 4DF phase contrast. Regurgitant fraction was calculated as [RVol/(RVol+SLNF)]× 100. Statistical methods included Spearman, Wilcoxon rank sum test/Student paired t-test, Bland Altman analysis, and intra-class coefficient (ICC), where appropriate. RESULTS: Regurgitant fraction by JET strongly correlated with the indirect methods (VOL and AIM) (ρ = 0.73-0.80, p < 0.001) and was similar to VOL with a median difference (interquartile range) of - 1.5% (- 8.3-7.2%; p = 0.624). VOL had weaker correlations with AIM and JET (ρ = 0.69-0.73, p < 0.001). AIM underestimated RF by 3.6-6.9% compared to VOL and JET, p < 0.03. Intra- and inter- observer reliability were excellent for all methods (ICC 0.94-0.99). The mean (±standard deviation) inter-observer difference for VOL was 2.4% (±5.1%), p < 0.05. CONCLUSIONS: In a diverse cohort of pediatric CHD, measurement of AVVR using JET is accurate and reliable to VOL and is a superior method of internal validation compared to AIM. This study supports use of 4DF CMR for measurement of AVVR, obviating need for expert prospective prescription during image acquisition by 2D CMR.

Early postoperative physical activity and function: a descriptive case series study of 53 patients after lumbar spine surgery.

BACKGROUND: The purpose of this prospective case series study was to compare changes in early postoperative physical activity and physical function between 6 weeks and 3 and 6 months after lumbar spine surgery. METHODS: Fifty-three patients (mean [95% confidence interval; CI] age = 59.2 [56.2, 62.3] years, 64% female) who underwent spine surgery for a degenerative lumbar condition were assessed at 6 weeks and 3- and 6-months after surgery. The outcomes were objectively-measured physical activity (accelerometry) and patient-reported and objective physical function. Physical activity was assessed using mean steps/day and time spent in moderate to vigorous physical activity (MVPA) over a week. Physical function measures included Oswestry Disability Index (ODI), 12-item Short Form Health Survey (SF-12), Timed Up and Go (TUG), and 10-Meter Walk (10 MW). We compared changes over time in physical activity and function using generalized estimating equations with robust estimator and first-order autoregressive covariance structure. Proportion of patients who engaged in meaningful physical activity (e.g., walked at least 4400 and 6000 steps/day or engaged in at least 150 min/week in MVPA) and achieved clinically meaningful changes in physical function were compared at 3 and 6 months. RESULTS: After surgery, 72% of patients initiated physical therapy (mean [95%CI] sessions =8.5 [6.6, 10.4]) between 6 weeks and 3 months. Compared to 6 weeks post-surgery, no change in steps/day or time in MVPA/week was observed at 3 or 6 months. From 21 to 23% and 9 to 11% of participants walked at least 4400 and 6000 steps/day at 3 and 6 months, respectively, while none of the participants spent at least 150 min/week in MVPA at these same time points. Significant improvements were observed on ODI, SF-12, TUG and 10 MW (p <  0.05), with over 43 to 68% and 62 to 87% achieving clinically meaningful improvements on these measures at 3 and 6 months, respectively. CONCLUSION: Limited improvement was observed in objectively-measured physical activity from 6 weeks to 6 months after spine surgery, despite moderate to large function gains. Early postoperative physical therapy interventions targeting physical activity may be needed.

Examining the Emergency Medical Treatment and Active Labor Act: impact on telemedicine for neurotrauma.

The Emergency Medical Treatment and Active Labor Act (EMTALA) protects patient access to emergency medical treatment regardless of insurance or socioeconomic status. A significant result of the COVID-19 pandemic has been the rapid acceleration in the adoption of telemedicine services across many facets of healthcare. However, very little literature exists regarding the use of telemedicine in the context of EMTALA. This work aimed to evaluate the potential to expand the usage of telemedicine services for neurotrauma to reduce transfer rates, minimize movement of patients across borders, and alleviate the burden on tertiary care hospitals involved in the care of patients with COVID-19 during a global pandemic. In this paper, the authors outline EMTALA provisions, provide examples of EMTALA violations involving neurosurgical care, and propose guidelines for the creation of telemedicine protocols between referring and consulting institutions.

Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.

Whole-heart coronary MR angiography using a 3D cones phyllotaxis trajectory.

PURPOSE: To develop a 3D cones steady-state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free-breathing whole-heart coronary magnetic resonance angiography. METHOD: The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k-space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade-off of an increased number of acquisition heartbeats. For verification, phyllotaxis-cones is compared to sequential-cones through simulations, phantom studies, and in vivo coronary scans with 8 subjects using 2D image-based navigators for retrospective motion correction. RESULTS: Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis-cones compared to sequential-cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis-cones. CONCLUSION: Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free-breathing whole-heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady-state free precession sequence.

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique. METHODS: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one-sided t-tests. Reader agreement was determined with intraclass correlation. RESULTS: The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off-ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off-resonance (P < .01). The proposed method had superior normalized RMS error over -677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off-ResNet were noninferior to short readout scans (P < .05). CONCLUSION: The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.

Demographics, presentation and symptoms of patients with Klippel-Feil syndrome: analysis of a global patient-reported registry.

INTRODUCTION: Klippel-Feil syndrome (KFS) occurs due to failure of vertebral segmentation during development. Minimal research has been done to understand the prevalence of associated symptoms. Here, we report one of the largest collections of KFS patient data. METHODS: Data were obtained from the CoRDS registry. Participants with cervical fusions were categorized into Type I, II, or III based on the Samartzis criteria. Symptoms and comorbidities were assessed against type and location of fusion. RESULTS: Seventy-five patients (60F/14M/1 unknown) were identified and classified as: Type I, n = 21(28%); Type II, n = 15(20%); Type III, n = 39(52%). Cervical fusion by level were: OC-C1, n = 17(22.7%), C1-C2, n = 24(32%); C2-C3, n = 42(56%); C3-C4, n = 30(40%); C4-C5, n = 42(56%); C5-C6, n = 32(42.7%); C6-C7, n = 25(33.3%); C7-T1, n = 13(17.3%). 94.6% of patients reported current symptoms and the average age when symptoms began and worsened were 17.5 (± 13.4) and 27.6 (± 15.3), respectively. Patients reported to have a high number of comorbidities including spinal, neurological and others, a high frequency of general symptoms (e.g., fatigue, dizziness) and chronic symptoms (limited range of neck motion [LROM], neck/spine muscles soreness). Sprengel deformity was reported in 26.7%. Most patients reported having received medication and invasive/non-invasive procedures. Multilevel fusions (Samartzis II/III) were significantly associated with dizziness (p = 0.040), the presence of LROM (p = 0.022), and Sprengel deformity (p = 0.036). CONCLUSION: KFS is associated with a number of musculoskeletal and neurological symptoms. Fusions are more prevalent toward the center of the cervical region, and less common at the occipital/thoracic junction. Associated comorbidities including Sprengel deformity may be more common in KFS patients with multilevel cervical fusions. These slides can be retrieved under Electronic Supplementary Material.

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

General phase regularized reconstruction using phase cycling.

PURPOSE: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. THEORY AND METHODS: The problem of enforcing phase constraints in reconstruction was studied under a regularized inverse problem framework. A general phase regularized reconstruction algorithm was proposed to enable various joint reconstruction of partial Fourier imaging, water-fat imaging and flow imaging, along with parallel imaging (PI) and compressed sensing (CS). Since phase regularized reconstruction is inherently non-convex and sensitive to phase wraps in the initial solution, a reconstruction technique, named phase cycling, was proposed to render the overall algorithm invariant to phase wraps. The proposed method was applied to retrospectively under-sampled in vivo datasets and compared with state of the art reconstruction methods. RESULTS: Phase cycling reconstructions showed reduction of artifacts compared to reconstructions without phase cycling and achieved similar performances as state of the art results in partial Fourier, water-fat and divergence-free regularized flow reconstruction. Joint reconstruction of partial Fourier + water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized flow imaging + PI + CS were demonstrated. CONCLUSION: The proposed phase cycling reconstruction provides an alternative way to perform phase regularized reconstruction, without the need to perform phase unwrapping. It is robust to the choice of initial solutions and encourages the joint reconstruction of phase imaging applications. Magn Reson Med 80:112-125, 2018. © 2017 International Society for Magnetic Resonance in Medicine.