Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Ablation-induced left atrial mechanical dysfunction recovers in weeks after ablation.

BACKGROUND: The immediate impact of catheter ablation on left atrial mechanical function and the timeline for its recovery in patients undergoing ablation for atrial fibrillation (AF) remain uncertain. The mechanical function response to catheter ablation in patients with different AF types is poorly understood. METHODS: A total of 113 AF patients were included in this retrospective study. Each patient had three magnetic resonance imaging (MRI) studies in sinus rhythm: one pre-ablation, one immediate post-ablation (within 2 days after ablation), and one post-ablation follow-up MRI (≤ 3 months). We used feature tracking in the MRI cine images to determine peak longitudinal atrial strain (PLAS). We evaluated the change in strain from pre-ablation, immediately after ablation to post-ablation follow-up in a short-term study (< 50 days) and a 3-month study (3 months after ablation). RESULTS: The PLAS exhibited a notable reduction immediately after ablation, compared to both pre-ablation levels and those observed in follow-up studies conducted at short-term (11.1 ± 9.0 days) and 3-month (69.6 ± 39.6 days) intervals. However, there was no difference between follow-up and pre-ablation PLAS. The PLAS returned to 95% pre-ablation level within 10 days. Paroxysmal AF patients had significantly higher pre-ablation PLAS than persistent AF patients in pre-ablation MRIs. Both type AF patients had significantly lower immediate post-ablation PLAS compared with pre-ablation and post-ablation PLAS. CONCLUSION: The present study suggested a significant drop in PLAS immediately after ablation. Left atrial mechanical function recovered within 10 days after ablation. The drop in PLAS did not show a substantial difference between paroxysmal and persistent AF patients.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Technical note: Accuracy and precision of T2 and T2* with a gradient-echo spin-echo (GESE) sequence for cardiac imaging.

BACKGROUND: The use of a gradient echo spin echo (GESE) method to obtain rapid T2 and T2* estimation in the heart has been proposed. The effect of acquisition parameter settings on T2 and T2* bias and precision have not been investigated in depth. PURPOSE: To understand factors impacting the quantification of T2 and T2* values with a gradient echo spin echo (GESE) method using echo planar imaging (EPI) readouts in a reduced field of view acquisition. METHODS: The GESE method is implemented with a reduced field-of-view using an outer volume suppression (OVS) technique to minimize the time for multi-echo EPI readouts. The number of EPI readouts (images) for the GESE is optimized using Cramer-Rao Lower Bound (CRLB) and Monte Carlo simulations with a nonlinear least-square (NLLS) estimator. The SNR requirements were studied using the latter simulation method for a selected range of T2 and T2* values and T2/T2* ratios. Two healthy control subjects were imaged with the proposed GESE sequence and evaluated with the NLLS estimation method. In addition, the proposed OVS method was compared with a saturation bands OVS method in one subject. Clinical T2 and T2* mappings were used as the reference. RESULTS: The optimal number of EPI readouts is five and the performance is slightly better when the refocusing pulse is placed between the 2nd and 3rd readouts. The SNR requirement for achieving a target bias < 1 ms and standard deviation (SD) < 5 ms is more demanding when T2/T2* ratio increases. The minimum SNR requirement in the GESE acquisition should vary from 6 to 20 depending on specific myocardial T2 and T2* values at 3T. The T2 and T2* estimates using the proposed OVS method and the saturation bands OVS method are both similar to the reference. CONCLUSION: The GESE sequence with five EPI readouts is a feasible and efficient technique that can estimate T2 and T2* values in the septal myocardium within a heartbeat when the SNR requirement can be satisfied.

Comparison of Prospective and Retrospective Gated 4D Flow Cardiac MR Image Acquisitions in the Carotid Bifurcation.

PURPOSE: To evaluate the agreement of 4D flow cMRI-derived bulk flow features and fluid (blood) velocities in the carotid bifurcation using prospective and retrospective gating techniques. METHODS: Prospective and retrospective ECG-gated three-dimensional (3D) cine phase-contrast cardiac MRI with three-direction velocity encoding (i.e., 4D flow cMRI) data were acquired in ten carotid bifurcations from men (n = 3) and women (n = 2) that were cardiovascular disease-free. MRI sequence parameters were held constant across all scans except temporal resolution values differed. Velocity data were extracted from the fluid domain and evaluated across the entire volume or at defined anatomic planes (common, internal, external carotid arteries). Qualitative agreement between gating techniques was performed by visualizing flow streamlines and topographical images, and statistical comparisons between gating techniques were performed across the fluid volume and defined anatomic regions. RESULTS: Agreement in the kinematic data (e.g., bulk flow features and velocity data) were observed in the prospectively and retrospectively gated acquisitions. Voxel differences in time-averaged, peak systolic, and diastolic-averaged velocity magnitudes between gating techniques across all volunteers were 2.7%, 1.2%, and 6.4%, respectively. No significant differences in velocity magnitudes or components ([Formula: see text], [Formula: see text], [Formula: see text]) were observed. Importantly, retrospective acquisitions captured increased retrograde flow in the internal carotid artery (i.e., carotid sinus) compared to prospective acquisitions (10.4 ± 6.3% vs. 4.6 ± 5.3%; [Formula: see text] < 0.05). CONCLUSION: Prospective and retrospective ECG-gated 4D flow cMRI acquisitions provide comparable evaluations of fluid velocities, including velocity vector components, in the carotid bifurcation. However, the increased temporal coverage of retrospective acquisitions depicts increased retrograde flow patterns (i.e., disturbed flow) not captured by the prospective gating technique.

A comparison of simultaneous multislice and conventional diffusion tensor imaging techniques for ischemic stroke evaluation at 1.5T.

OBJECTIVE: Diffusion tensor imaging (DTI) is a promising technique for ischemic stroke evaluation; however, acquisition time is longer than DWI. Simultaneous multislice (SMS) imaging acquires multiple slices together and reduces scan time. This study compared conventional and SMS DTI for ischemic stroke workup. METHODS: Following IRB approval, the departmental stroke protocol was supplemented with SMS DTI on a clinical 1.5T MRI. Cases suspicious for ischemic stroke outside the treatment window were included. Standard DTI (STD-DTI, 20-direction, b = 2000 s/mm2), was followed by SMS-2-DTI (two slices simultaneously imaged). Two blinded neuroradiologists independently assessed image quality and DTI-trace status (positive/negative = stroke/other). Average image quality, interrater reliability (κ), receiver operating characteristic area under the curve (AUC), signal-to-noise ratio (SNR = DTI-source min/max/average), coefficient of variation (CV), mean diffusivity (MD), and fractional anisotropy (FA, of DTI-trace) were compared using two-tailed t-tests and a p < .05. RESULTS: 41 patients were evaluated. SMS-2-DTI decreased DTI time by 132.17 ± 15.33 s, a 45% reduction. SMS-2-DTI reduced image quality (STD-DTI 4.7 ± 0.5 vs SMS-2-DTI 3.8 ± 0.6, p < .001). Diagnostic accuracy persisted, AUC was high for observer 1 (STD-DTI 0.95, 95%CI = 0.88-1.00 vs SMS-2-DTI 0.94, 95%CI = 0.87-1.00, p = .86) and observer 2 (STD-DTI 0.89, 95%CI = 0.79-0.99 vs SMS-2-DTI 0.86, 95%CI = 0.76-0.97, p = .66). Interrater reliability was high for STD-DTI (κ = 0.80, 95%CI = 0.61-0.98) and SMS-2-DTI (κ = 0.84, 95%CI = 0.67-1.00). SMS-2-DTI significantly decreased average SNR (STD-DTI 42.85 ± 4.44 vs SMS-2-DTI 32.58 ± 4.30, p < .001), and CV MD (STD-DTI 0.23 ± 0.03 vs 0.20 ± 0.04, p < .001). CV FA and CV DTI-trace were not statistically different. CONCLUSIONS: This study supports using SMS to accelerate DTI for ischemic stroke workup at 1.5T in the non-hyper-acute setting. ADVANCES IN KNOWLEDGE: This study highlights the feasibility of accelerated multislice DTI for faster diagnostic DTI-trace images capable of ischemic stroke detection.

Quantitative myocardial perfusion with a hybrid 2D simultaneous multi-slice sequence.

PURPOSE: To evaluate a novel 2D simultaneous multi-slice (SMS) myocardial perfusion acquisition and compare directly to a published quantitative 3D stack-of-stars (SoS) acquisition. METHODS: A hybrid saturation recovery radial 2D SMS sequence following a single saturation was created for the quantification of myocardial blood flow (MBF). This sequence acquired three slices simultaneously and generated an arterial input function (AIF) using the first 24 rays. Validation was done in a novel way by alternating heartbeats between the hybrid 2D SMS and the 3D SoS acquisitions. Initial studies were done to study the effects of using only every other beat for the 2D SMS in two subjects, and for the 3D SoS in four subjects. The proposed alternating acquisitions were then performed in ten dog studies at rest, four dog studies at adenosine stress, and two human resting studies. Quantitative MBF analysis was performed for 2D SMS and 3D SoS separately, using a compartment model. RESULTS: Acquiring every-other-beat data resulted in 6 ± 5% ("ideal") and 11 ± 8% ("practical") perfusion changes for both 2D SMS and 3D SoS methods. For alternating acquisitions, 2D SMS and 3D SoS quantitative perfusion values were comparable for both the twelve rest studies (2D SMS: 0.69 ± 0.16 vs 3D: 0.69 ± 0.15 ml/g/min, p = 0.55) and the four stress studies (2D SMS: 1.28 ± 0.22 vs 3D: 1.30 ± 0.24 ml/g/min, p = 0.61). CONCLUSION: Every-other-beat acquisition changed estimated perfusion values relatively little for both sequences. The quantitative hybrid radial 2D SMS myocardial first-pass perfusion imaging sequence gave results similar to 3D perfusion when compared directly with an alternating beat acquisition.

Accelerated cardiac T1 mapping with recurrent networks and cyclic, model-based loss.

BACKGROUND: Using the spin-lattice relaxation time (T1) as a biomarker, the myocardium can be quantitatively characterized using cardiac T1 mapping. The modified Look-Locker inversion (MOLLI) recovery sequences have become the standard clinical method for cardiac T1 mapping. However, the MOLLI sequences require an 11-heartbeat breath-hold that can be difficult for subjects, particularly during exercise or pharmacologically induced stress. Although shorter cardiac T1 mapping sequences have been proposed, these methods suffer from reduced precision. As such, there is an unmet need for accelerated cardiac T1 mapping. PURPOSE: To accelerate cardiac T1 mapping MOLLI sequences by using neural networks to estimate T1 maps using a reduced number of T1-weighted images and their corresponding inversion times. MATERIALS AND METHODS: In this retrospective study, 911 pre-contrast T1 mapping datasets from 202 subjects (128 males, 56 ± 15 years; 74 females, 54 ± 17 years) and 574 T1 mapping post-contrast datasets from 193 subjects (122 males, 57 ± 15 years; 71 females, 54 ± 17 years) were acquired using the MOLLI-5(3)3 sequence and the MOLLI-4(1)3(1)2 sequence, respectively. All acquisition protocols used similar scan parameters: T R = 2.2 ms $TR\; = \;2.2\;{\rm{ms}}$ , T E = 1.12 ms $TE\; = \;1.12\;{\rm{ms}}$ , and F A = 35 ∘ $FA\; = \;35^\circ $ , gadoteridol (ProHance, Bracco Diagnostics) dose ∼ 0.075 mmol / kg $\sim 0.075\;\;{\rm{mmol/kg}}$ . A bidirectional multilayered long short-term memory (LSTM) network with fully connected output and cyclic model-based loss was used to estimate T1 maps from the first three T1-weighted images and their corresponding inversion times for pre- and post-contrast T1 mapping. The performance of the proposed architecture was compared to the three-parameter T1 recovery model using the same reduction of the number of T1-weighted images and inversion times. Reference T1 maps were generated from the scanner using the full MOLLI sequences and the three-parameter T1 recovery model. Correlation and Bland-Altman plots were used to evaluate network performance in which each point represents averaged regions of interest in the myocardium corresponding to the standard American Heart Association 16-segment model. The precision of the network was examined using consecutively repeated scans. Stress and rest pre-contrast MOLLI studies as well as various disease test cases, including amyloidosis, hypertrophic cardiomyopathy, and sarcoidosis were also examined. Paired t-tests were used to determine statistical significance with p < 0.05 $p < 0.05$ . RESULTS: Our proposed network demonstrated similar T1 estimations to the standard MOLLI sequences (pre-contrast: 1260 ± 94 ms $1260 \pm 94\;{\rm{ms}}$ vs. 1254 ± 91 ms $1254 \pm 91\;{\rm{ms}}$ with p = 0.13 $p\; = \;0.13$ ; post-contrast: 484 ± 92 ms $484 \pm 92\;{\rm{ms}}$ vs. 493 ± 91 ms $493 \pm 91\;{\rm{ms}}$ with p = 0.07 $p\; = \;0.07$ ). The precision of standard MOLLI sequences was well preserved with the proposed network architecture ( 24 ± 28 ms $24 \pm 28\;\;{\rm{ms}}$ vs. 18 ± 13 ms $18 \pm 13\;{\rm{ms}}$ ). Network-generated T1 reactivities are similar to stress and rest pre-contrast MOLLI studies ( 5.1 ± 4.0 % $5.1 \pm 4.0\;\% $ vs. 4.9 ± 4.4 % $4.9 \pm 4.4\;\% $ with p = 0.84 $p\; = \;0.84$ ). Amyloidosis T1 maps generated using the proposed network are also similar to the reference T1 maps (pre-contrast: 1243 ± 140 ms $1243 \pm 140\;\;{\rm{ms}}$ vs. 1231 ± 137 ms $1231 \pm 137\;{\rm{ms}}$ with p = 0.60 $p\; = \;0.60$ ; post-contrast: 348 ± 26 ms $348 \pm 26\;{\rm{ms}}$ vs. 346 ± 27 ms $346 \pm 27\;{\rm{ms}}$ with p = 0.89 $p\; = \;0.89$ ). CONCLUSIONS: A bidirectional multilayered LSTM network with fully connected output and cyclic model-based loss was used to generate high-quality pre- and post-contrast T1 maps using the first three T1-weighted images and their corresponding inversion times. This work demonstrates that combining deep learning with cardiac T1 mapping can potentially accelerate standard MOLLI sequences from 11 to 3 heartbeats.

Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.

PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q-space data. METHODS: We implement three DL approaches to jointly estimate parametric maps of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and multi-compartment spherical mean technique (SMT). A per-voxel q-space deep learning (1D-qDL), a per-slice convolutional neural network (2D-CNN), and a 3D-patch-based microstructure estimation with sparse coding using a separable dictionary (MESC-SD) network are considered. RESULTS: The accuracy of estimated diffusion maps depends on the q-space undersampling, the selected network architecture, and the region and the parameter of interest. The smallest errors are observed for the MESC-SD network architecture (less than 10 % normalized RMSE in most brain regions). CONCLUSION: Our experiments show that DL methods are very efficient tools to simultaneously estimate several diffusion maps from undersampled q-space data. These methods can significantly reduce both the scan ( ∼ 6-fold) and processing times ( ∼ 25-fold) for estimating advanced parametric diffusion maps while achieving a reasonable accuracy.

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

PURPOSE: To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method. METHODS: Dynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH sequence. Input to the networks consisted of complex coil combined images reconstructed using the inverse Fourier transform of undersampled radial SMS k-space data. Ground truth images were reconstructed using the PT-STCR pipeline. The performance of the residual booster 3D U-Net was tested by comparing it to state-of-the-art network architectures including MoDL, CRNN-MRI, and other U-Net variants. RESULTS: Results demonstrate significant improvements in speed requiring approximately 8 seconds to reconstruct one radial SMS dataset which is approximately 200 times faster than the PT-STCR method. Images reconstructed with the residual booster 3D U-Net retain quality of ground truth PT-STCR images (0.963 SSIM/40.238 PSNR/0.147 NRMSE). The residual booster 3D U-Net has superior performance compared to existing network architectures in terms of image quality, temporal dynamics, and reconstruction time. CONCLUSION: Residual and booster learning combined with the 3D U-Net architecture was shown to be an effective network for reconstructing high-quality images from undersampled radial SMS datasets while bypassing the reconstruction time of the PT-STCR method.

Simultaneous multi-slice image reconstruction using regularized image domain split slice-GRAPPA for diffusion MRI.

The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single-band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by ∼5% and increases the structural similarity index (SSIM) by ∼4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dispersion index (ODI) by ∼5% and the mean value of intracellular volume fraction by ∼7% in regions of interest in brain white matter compared to SSG.

Reproducibility of clinical late gadolinium enhancement magnetic resonance imaging in detecting left atrial scar after atrial fibrillation ablation.

INTRODUCTION: Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) can be used to detect postablation atrial scar (PAAS) but its reproducibility and reliability in clinical scans across different magnetic flux densities and scar detection methods are unknown. METHODS: Patients (n = 45) having undergone two consecutive MRIs (3 months apart) on 3T and 1.5T scanners were studied. We compared PAAS detection reproducibility using four methods of thresholding: simple thresholding, Otsu thresholding, 3.3 standard deviations (SD) above blood pool (BP) mean intensity, and image intensity ratio (IIR). We performed a texture study by dividing the left atrial wall intensity histogram into deciles and evaluated the correlation of the same decile of the two scans as well as to a randomized distribution of intensities, quantified using Dice Similarity Coefficient (DSC). RESULTS: The choice of scanner did not significantly affect the reproducibility. The scar detection performed by Otsu thresholding (DSC of 71.26 ± 8.34) resulted in a better correlation of the two scans compared with the methods of 3.3 SD above BP mean intensity (DSC of 57.78 ± 21.2, p < .001) and IIR above 1.61 (DSC of 45.76 ± 29.55, p <.001). Texture analysis showed that correlation only for voxels with intensities in deciles above the 70th percentile of wall intensity histogram was better than random distribution (p < .001). CONCLUSIONS: Our results demonstrate that clinical LGE-MRI can be reliably used for visualizing PAAS across different magnetic flux densities if the threshold is greater than 70th percentile of the wall intensity distribution. Also, atrial wall-based thresholding is better than BP-based thresholding for reproducible PAAS detection.

Whole-heart, ungated, free-breathing, cardiac-phase-resolved myocardial perfusion MRI by using Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state (CRIMP).

PURPOSE: To develop a whole-heart, free-breathing, non-electrocardiograph (ECG)-gated, cardiac-phase-resolved myocardial perfusion MRI framework (CRIMP; Continuous Radial Interleaved simultaneous Multi-slice acquisitions at sPoiled steady-state) and test its quantification feasibility. METHODS: CRIMP used interleaved radial simultaneous multi-slice (SMS) slice groups to cover the whole heart in 9 or 12 short-axis slices. The sequence continuously acquired data without magnetization preparation, ECG gating or breath-holding, and captured multiple cardiac phases. Images were reconstructed by a motion-compensated patch-based locally low-rank reconstruction. Bloch simulations were performed to study the signal-to-noise ratio/contrast-to-noise ratio (SNR/CNR) for CRIMP and to study the steady-state signal under motion. Seven patients were scanned with CRIMP at stress and rest to develop the sequence. One human and two dogs were scanned at rest with a dual-bolus method to test the quantification feasibility of CRIMP. The dual-bolus scans were performed using both CRIMP and an ungated radial SMS saturation recovery (SMS-SR) sequence with injection dose = 0.075 mmol/kg to compare the sequences in terms of SNR, cardiac phase resolution and quantitative myocardial blood flow (MBF). RESULTS: Perfusion images with multiple cardiac phases in all image slices with a temporal resolution of 72 ms/frame were obtained. Simulations and in-vivo acquisitions showed CRIMP kept the inner slices in steady-state regardless of motion. CRIMP outperformed SMS-SR in slice coverage (9 over 6), SNR (mean 20% improvement), and provided cardiac phase resolution. CRIMP and SMS-SR sequences provided comparable MBF values (rest systolic CRIMP = 0.58 ± 0.07, SMS-SR = 0.61 ± 0.16). CONCLUSION: CRIMP allows for whole-heart, cardiac-phase-resolved myocardial perfusion images without ECG-gating or breath-holding. The sequence can provide MBF if an accurate arterial input function is obtained separately.

Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors.

The state-of-the-art approaches for image reconstruction using under-sampled k-space data are compressed sensing based. They are iterative algorithms that optimize objective functions with spatial and/or temporal constraints. This paper proposes a non-iterative algorithm to estimate the un-measured data and then to reconstruct the image with the efficient filtered backprojection algorithm. The feasibility of the proposed method is demonstrated with a patient magnetic resonance imaging study. The proposed method is also compared with the state-of-the-art iterative compressed-sensing image reconstruction method using the total-variation optimization norm.

Iterative versus non-iterative image reconstruction methods for sparse magnetic resonance imaging.

Magnetic resonance imaging (MRI) using under-sampled k-space data is a common method to shorten the imaging time. Iterative Bayesian algorithms are usually used for its image reconstruction. This paper compares an iterative Bayesian image reconstruction method that uses both spatial and temporal constraints and a non-iterative reconstruction algorithm that does not use temporal constraints. Three patient studies are performed. It is interesting to notice that the images reconstructed by the iterative Bayesian algorithm may introduce more bias than the non-iterative algorithm, even though the images provided by the iterative Bayesian algorithm look less noisy. The bias can be reduced by decreasing the influence of the temporal constraints.

Initial investigation of free-breathing 3D whole-heart stress myocardial perfusion MRI.

Objective: Myocardial first-pass perfusion imaging with MRI is well-established clinically. However, it is potentially weakened by limited myocardial coverage compared to nuclear medicine. Clinical evaluations of whole-heart MRI perfusion by 3D methods, while promising, have to date had the limit of breathhold requirements at stress. This work aims to develop a new free-breathing 3D myocardial perfusion method, and to test its performance in a small patient population. Methods: This work required tolerance to respiratory motion for stress investigations, and therefore employed a "stack-of-stars" hybrid Cartesian-radial MRI acquisition method. The MRI sequence was highly optimised for rapid acquisition and combined with a compressed sensing reconstruction. Stress and rest datasets were acquired in four healthy volunteers, and in six patients with coronary artery disease (CAD), which were compared against clinical reference information. Results: This free-breathing method produced datasets that appeared consistent with clinical reference data in detecting moderate-to-strong induced perfusion abnormalities. However, the majority of the mild defects identified clinically were not detected by the method, potentially due to the presence of transient myocardial artefacts present in the images. Discussion: The feasibility of detecting CAD using this 3D first-pass perfusion sequence during free-breathing is demonstrated. Good agreement on typical moderate-to-strong CAD cases is promising, however, questions still remain on the sensitivity of the technique to milder cases.

Quantitative 3D myocardial perfusion with an efficient arterial input function.

PURPOSE: The purpose of this study was to further develop and combine several innovative sequence designs to achieve quantitative 3D myocardial perfusion. These developments include an optimized 3D stack-of-stars readout (150 ms per beat), efficient acquisition of a 2D arterial input function, tailored saturation pulse design, and potential whole heart coverage during quantitative stress perfusion. THEORY AND METHODS: All studies were performed free-breathing on a Prisma 3T MRI scanner. Phantom validation was used to verify sequence accuracy. A total of 21 subjects (3 patients with known disease) were scanned, 12 with a rest only protocol and 9 with both stress (regadenoson) and rest protocols. First pass quantitative perfusion was performed with gadoteridol (0.075 mmol/kg). RESULTS: Implementation and quantitative perfusion results are shown for healthy subjects and subjects with known coronary disease. Average rest perfusion for the 15 included healthy subjects was 0.79 ± 0.19 mL/g/min, the average stress perfusion for 6 healthy subject studies was 2.44 ± 0.61 mL/g/min, and the average global myocardial perfusion reserve ratio for 6 healthy subjects was 3.10 ± 0.24. Perfusion deficits for 3 patients with ischemia are shown. Average resting heart rate was 59 ± 7 bpm and the average stress heart rate was 81 ± 10 bpm. CONCLUSION: This work demonstrates that a quantitative 3D myocardial perfusion sequence with the acquisition of a 2D arterial input function is feasible at high stress heart rates such as during stress. T1 values and gadolinium concentrations of the sequence match the reference standard well in a phantom, and myocardial rest and stress perfusion and myocardial perfusion reserve values are consistent with those published in literature.

Coil-combined split slice-GRAPPA for simultaneous multi-slice diffusion MRI.

OBJECTIVE: To develop a kernel optimization method called coil-combined split slice-GRAPPA (CC-SSG) to improve the accuracy of the reconstructed coil-combined images for simultaneous multi-slice (SMS) diffusion weighted imaging (DWI) data. METHODS: The CC-SSG method optimizes the tuning parameters in the k-space SSG kernels to achieve an optimal trade-off between the intra-slice artifact and inter-slice leakage after the root-sum-of-squares (rSOS) coil combining of the de-aliased SMS DWI data. A detailed analysis is conducted to evaluate the contributions of the intra-slice artifact and inter-slice leakage to the total reconstruction error after coil combining. RESULTS: Comparisons of the proposed CC-SSG method with the slice-GRAPPA (SG) and split slice-GRAPPA (SSG) methods are provided using two in-vivo readout-segmented (RS) EPI datasets collected from stroke patients. The CC-SSG method demonstrates improved accuracy of the reconstructed coil-combined images and the estimated diffusion tensor imaging (DTI) maps. CONCLUSION: CC-SSG strikes a good balance between the intra-slice artifact and inter-slice leakage for rSOS coil combining, and so can yield better reconstruction performance compared to SG and SSG for rSOS reconstruction. The optimal trade-off between the two artifacts is robust to the contrast of SMS data and the choice of the coil combining method.

Predicting Motor Outcomes in Stroke Patients Using Diffusion Spectrum MRI Microstructural Measures.

Improved understanding of neuroimaging signal changes and their relation to patient outcomes after ischemic stroke is needed to improve ability to predict motor improvement and make therapy recommendations. The posterior limb of the internal capsule (PLIC) is a hub of afferent and efferent motor signaling and this work proposes new, image-based methods for prognosis based on interhemispheric differences in the PLIC. In this work, nine acute supratentorial ischemic stroke patients with motor impairment received a baseline, 203-direction diffusion brain MRI and a clinical assessment 3-12 days post-stroke and were compared to nine age-matched healthy controls. Asymmetries based on the mean and Kullback-Leibler divergence in the ipsilesional and contralesional PLIC were calculated for diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) measures from the baseline MRI. Predictions of upper extremity Fugl-Meyer (FM) scores at 5-weeks follow-up from baseline measures of PLIC asymmetry in diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) models were evaluated. For the stroke participants, the baseline asymmetry measures in the PLIC for the orientation dispersion index of the neurite orientation dispersion and density imaging (NODDI) model were highly correlated with upper extremity FM outcomes (r 2 = 0.83). Use of DSI and the NODDI orientation dispersion index parameter shows promise of being more predictive of stroke recovery and to help better understand white matter changes in stroke, beyond DTI measures. The new finding that baseline interhemispheric differences in the PLIC calculated from the orientation dispersion index of the NODDI model are highly correlated with upper extremity functional outcomes may lead to improved image-based motor-outcome prediction after middle cerebral artery ischemic stroke.

Feasibility of multiple-view myocardial perfusion MRI using radial simultaneous multi-slice acquisitions.

PURPOSE: Dynamic contrast enhanced MRI of the heart typically acquires 2-4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework. METHODS: 12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio. RESULTS: Full coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions. CONCLUSION: Acquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.