arXiv subjects

Rizwan Ahmad

Publications and source records attributed to Rizwan Ahmad.

At least 37 records · Page 2Linked to original sources

arXiv2024

Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction

In imaging inverse problems, one seeks to recover an image from missing/corrupted measurements. Because such problems are ill-posed, there is great motivation to quantify the uncertainty induced by the measurement-and-recovery process. Motivated by applications where the recovered image is used for a downstream task, such as soft-output classification, we propose a task-centered approach to uncertainty quantification. In particular, we use conformal prediction to construct an interval that is guaranteed to contain the task output from the true image up to a user-specified probability, and we use the width of that interval to quantify the uncertainty contributed by measurement-and-recovery. For posterior-sampling-based image recovery, we construct locally adaptive prediction intervals. Furthermore, we propose to collect measurements over multiple rounds, stopping as soon as the task uncertainty falls below an acceptable level. We demonstrate our methodology on accelerated magnetic resonance imaging (MRI): https://github.com/jwen307/TaskUQ.

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arXiv2024

Motion-robust free-running volumetric cardiovascular MRI

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications. CODE & DATA: Implementation code and datasets are available on GitHub at http://github.com/OSU-MR/motion-robust-CMR

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arXiv2024

Coil Reweighting to Suppress Motion Artifacts in Real-Time Exercise Cine Imaging

Background: Accelerated real-time cine (RT-Cine) imaging enables cardiac function assessment without the need for breath-holding. However, when performed during in-magnet exercise, RT-Cine images may exhibit significant motion artifacts. Methods: By projecting the time-averaged images to the subspace spanned by the coil sensitivity maps, we propose a coil reweighting (CR) method to automatically suppress a subset of receive coils that introduces a high level of artifacts in the reconstructed image. RT-Cine data collected at rest and during exercise from ten healthy volunteers and six patients were utilized to assess the performance of the proposed method. One short-axis and one two-chamber RT-Cine series reconstructed with and without CR from each subject were visually scored by two cardiologists in terms of the level of artifacts on a scale of 1 (worst) to 5 (best). Results: For healthy volunteers, applying CR to RT-Cine images collected at rest did not significantly change the image quality score (p=1). In contrast, for RT-Cine images collected during exercise, CR significantly improved the score from 3.9 to 4.68 (p<0.001). Similarly, in patients, CR did not significantly change the score for images collected at rest (p=0.031) but markedly improved the score from 3.15 to 4.42 (p<0.001) for images taken during exercise. Despite lower image quality scores in the patient cohort compared to healthy subjects, likely due to larger body habitus and the difficulty of limiting body motion during exercise, CR effectively suppressed motion artifacts, with all image series from the patient cohort receiving a score of four or higher. Conclusion: Using data from healthy subjects and patients, we demonstrate that the motion artifacts in the reconstructed RT-Cine images can be effectively suppressed significantly with the proposed CR method.

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arXiv2024

Surface Coil Intensity Correction for MRI

Modern MRI scanners utilize one or more arrays of small receive-only coils to collect k-space data. The sensitivity maps of the coils, when estimated using traditional methods, differ from the true sensitivity maps, which are generally unknown. Consequently, the reconstructed MR images exhibit undesired spatial variation in intensity. These intensity variations can be at least partially corrected using pre-scan data. In this work, we propose an intensity correction method that utilizes pre-scan data. For demonstration, we apply our method to a digital phantom, as well as to cardiac MRI data collected on a commercial scanner by Siemens Healthineers. The code is available at https://github.com/OSU-MR/SCC.

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arXiv2024

Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction

High-quality training data are not always available in dynamic MRI. To address this, we propose a self-supervised deep learning method called deep image prior with structured sparsity (DISCUS) for reconstructing dynamic images. DISCUS is inspired by deep image prior (DIP) and recovers a series of images through joint optimization of network parameters and input code vectors. However, DISCUS additionally encourages group sparsity on frame-specific code vectors to discover the low-dimensional manifold that describes temporal variations across frames. Compared to prior work on manifold learning, DISCUS does not require specifying the manifold dimensionality. We validate DISCUS using three numerical studies. In the first study, we simulate a dynamic Shepp-Logan phantom with frames undergoing random rotations, translations, or both, and demonstrate that DISCUS can discover the dimensionality of the underlying manifold. In the second study, we use data from a realistic late gadolinium enhancement (LGE) phantom to compare DISCUS with compressed sensing (CS) and DIP, and to demonstrate the positive impact of group sparsity. In the third study, we use retrospectively undersampled single-shot LGE data from five patients to compare DISCUS with CS reconstructions. The results from these studies demonstrate that DISCUS outperforms CS and DIP, and that enforcing group sparsity on the code vectors helps discover true manifold dimensionality and provides additional performance gain.

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arXiv2023

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems

In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements. Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, super-resolution, inpainting, phase retrieval, image-to-image translation, and other applications. Given a training set of signal/measurement pairs, we seek to do more than just produce one good image estimate. Rather, we aim to rapidly and accurately sample from the posterior distribution. To do this, we propose a regularized conditional Wasserstein GAN that generates dozens of high-quality posterior samples per second. Our regularization comprises an $\ell_1$ penalty and an adaptively weighted standard-deviation reward. Using quantitative evaluation metrics like conditional Fréchet inception distance, we demonstrate that our method produces state-of-the-art posterior samples in both multicoil MRI and large-scale inpainting applications. The code for our model can be found here: https://github.com/matt-bendel/rcGAN

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arXiv2023

A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging

Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by collecting data below the Nyquist rate. As an ill-posed inverse problem, many plausible solutions exist, yet the majority of deep learning approaches generate only a single solution. We instead focus on sampling from the posterior distribution, which provides more comprehensive information for downstream inference tasks. To do this, we design a novel conditional normalizing flow (CNF) that infers the signal component in the measurement operator's nullspace, which is later combined with measured data to form complete images. Using fastMRI brain and knee data, we demonstrate fast inference and accuracy that surpasses recent posterior sampling techniques for MRI. Code is available at https://github.com/jwen307/mri_cnf/

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arXiv2023

Cardiac and respiratory motion extraction for MRI using Pilot Tone-a patient study

Background: Several studies have shown that both respiratory and cardiac motion can be extracted from the Pilot Tone (PT) signal successfully. However, most of these studies were performed in healthy volunteers. In addition, validating PT using ECG as a reference can be problematic because both PT and ECG tend to be unreliable in patients with arrhythmias. Purpose: We seek to evaluate the accuracy and reliability of the cardiac and respiratory signals extracted from PT in patients clinically referred for cardiovascular MRI with the image-derived signals as the reference. Methods: Twenty-three patients were scanned on a 1.5 T scanner using balanced steady-state free-precession real-time (RT) cine sequence. The PT signal was generated by a built-in PT transmitter integrated within the body array coil. For comparison, commercial ECG and BioMatrix (BM) respiratory sensor signals were synchronously recorded. Results: The respiratory motion extracted from PT correlated positively with the image-derived respiratory signal in all cases and showed a stronger correlation (absolute coefficient: 0.95+-0.09) than BM (0.72+-0.24). For the cardiac signal, PT trigger jitter (standard deviation of PT trigger locations relative to ECG triggers) ranged from 6.6 to 83.3 ms, with a median of 21.8 ms. The mean absolute difference between the PT and corresponding ECG cardiac cycle duration was less than 5% of the averaged ECG RR interval for 21 out of 23 patients. Overall, the performance of PT-based trigger extraction was comparable to that of ECG. We did not observe significant linear dependence (p>0.28) of PT delay and PT jitter on the patients' BMI or cardiac cycle duration. Conclusions: This study demonstrates the potential of PT to monitor both respiratory and cardiac motion in patients clinically referred for cardiovascular MRI.

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arXiv2022

Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery

To solve inverse problems, plug-and-play (PnP) methods replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN). Although such methods yield accurate solutions, they can be improved. For example, denoisers are usually designed/trained to remove white Gaussian noise, but the denoiser input error in PnP algorithms is usually far from white or Gaussian. Approximate message passing (AMP) methods provide white and Gaussian denoiser input error, but only when the forward operator is sufficiently random. In this work, for Fourier-based forward operators, we propose a PnP algorithm based on generalized expectation-consistent (GEC) approximation -- a close cousin of AMP -- that offers predictable error statistics at each iteration, as well as a new DNN denoiser that leverages those statistics. We apply our approach to magnetic resonance (MR) image recovery and demonstrate its advantages over existing PnP and AMP methods.

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arXiv2022

Venc Design and Velocity Estimation for Phase Contrast MRI

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional "dual-venc" technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits.

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arXiv2022

Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI

For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.

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arXiv2022

Expectation Consistent Plug-and-Play for MRI

For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network. Although such methods have been successful, they can be improved. For example, the denoiser is often trained using white Gaussian noise, while PnP's denoiser input error is often far from white and Gaussian, with statistics that are difficult to predict from iteration to iteration. PnP methods based on approximate message passing (AMP) are an exception, but only when the forward operator behaves like a large random matrix. In this work, we design a PnP method using the expectation consistent (EC) approximation algorithm, a generalization of AMP, that offers predictable error statistics at each iteration, from which a deep-net denoiser can be effectively trained.

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arXiv2022

Maximizing Unambiguous Velocity Range in Phase-contrast MRI with Multipoint Encoding

In phase-contrast magnetic resonance imaging (PC-MRI), the velocity of spins at a voxel is encoded in the image phase. The strength of the velocity encoding gradient offers a trade-off between the velocity-to-noise ratio (VNR) and the extent of phase aliasing. Phase differences provide invariance to an unknown background phase. Existing literature proposes processing a reduced set of phase difference equations, simplifying the phase unwrapping problem at the expense of VNR or unaliased range of velocities, or both. Here, we demonstrate that the fullest unambiguous range of velocities is a parallelepiped, which can be accessed by jointly processing all phase differences. The joint processing also minimizes the velocity-to-noise ratio. The simple understanding of the unambiguous parallelepiped provides the potential for analyzing new multi-point acquisitions for an enhanced range of unaliased velocities; two examples are given.

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arXiv2021

MRI Recovery with A Self-calibrated Denoiser

Plug-and-play (PnP) methods that employ application-specific denoisers have been proposed to solve inverse problems, including MRI reconstruction. However, training application-specific denoisers is not feasible for many applications due to the lack of training data. In this work, we propose a PnP-inspired recovery method that does not require data beyond the single, incomplete set of measurements. The proposed method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered. The denoiser training and a call to the denoising subroutine are performed in each iteration of a PnP algorithm, leading to a progressive refinement of the reconstructed image. For validation, we compare ReSiDe with a compressed sensing-based method and a PnP method with BM3D denoising using single-coil MRI brain data.

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arXiv2021

High-dimensional Fast Convolutional Framework (HICU) for Calibrationless MRI

Purpose: To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging. Theory and Methods: Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast ConvolU-tional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to six 2D T2-weighted brain, seven 2D cardiac cine, five 3D knee, and one multi-shot diffusion weighted imaging (MSDWI) datasets. Results: The 2D imaging results show that HICU can offer one to two orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3 dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application. Conclusions: The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

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arXiv2020

Calibrationless MRI Reconstruction with a Plug-in Denoiser

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) with a plug-in denoiser and demonstrate its feasibility using 2D brain data.

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arXiv2020

Automatic image-based respiratory signal extraction in real-time CMR

Purpose: To develop a fully automatic method for extraction and directionality determination of respiratory signal in free-breathing, real-time (RT) cardiac MRI. Methods: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. First, the signal polarity of all slices is made consistent with a reference slice. Second, a global sign correction is performed by maximizing the correlation of the respiratory signal with the zeroth-moment center curve. The proposed method is evaluated in multi-slice RT cine from eleven volunteers and two patients. The motion in a manually selected region-of-interest is used as reference. Results: The extracted respiratory signal using the proposed method exhibits high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. To demonstrate its clinical utility, the method is used to identify heartbeats from inspiratory and expiratory phases and, in turn, to account for respiration-induced changes in the cardiac output. Conclusions: The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.

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arXiv2020

Autotuning Plug-and-Play Algorithms for MRI

For magnetic resonance imaging (MRI), recently proposed "plug-and-play" (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image denoiser, such as BM3D or DnCNN. The fixed-points of PnP algorithms depend upon an algorithmic stepsize parameter, however, which must be tuned for optimal performance. In this work, we propose a fast and robust auto-tuning PnP-PDS algorithm that exploits knowledge of the measurement-noise variance that is available from a pre-scan in MRI. Experimental results show that our algorithm converges very close to genie-tuned performance, and does so significantly faster than existing autotuning approaches.

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