arXiv subjects

Valery Vishnevskiy

Publications and source records attributed to Valery Vishnevskiy.

12 recordsLinked to original sources

arXiv2025

FlowMRI-Net: A Generalizable Self-Supervised 4D Flow MRI Reconstruction network

Background: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications. Methods: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R=8,16,24) and compared to state-of-the-art compressed sensing (CS-LLR) and deep learning-based (FlowVN) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes. Results: FlowMRI-Net outperforms CS-LLR and FlowVN for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction, where no FlowVN can be trained due to the lack of high-quality reference data. Reconstruction times ranged from 3 to 7 minutes on commodity CPU/GPU hardware. Conclusion: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.

physics.med-ph↗

arXiv2024

Optimal OnTheFly Feedback Control of Event Sensors

Event-based vision sensors produce an asynchronous stream of events which are triggered when the pixel intensity variation exceeds a predefined threshold. Such sensors offer significant advantages, including reduced data redundancy, micro-second temporal resolution, and low power consumption, making them valuable for applications in robotics and computer vision. In this work, we consider the problem of video reconstruction from events, and propose an approach for dynamic feedback control of activation thresholds, in which a controller network analyzes the past emitted events and predicts the optimal distribution of activation thresholds for the following time segment. Additionally, we allow a user-defined target peak-event-rate for which the control network is conditioned and optimized to predict per-column activation thresholds that would eventually produce the best possible video reconstruction. The proposed OnTheFly control scheme is data-driven and trained in an end-to-end fashion using probabilistic relaxation of the discrete event representation. We demonstrate that our approach outperforms both fixed and randomly-varying threshold schemes by 6-12% in terms of LPIPS perceptual image dissimilarity metric, and by 49% in terms of event rate, achieving superior reconstruction quality while enabling a fine-tuned balance between performance accuracy and the event rate. Additionally, we show that sampling strategies provided by our OnTheFly control are interpretable and reflect the characteristics of the scene. Our results, derived from a physically-accurate simulator, underline the promise of the proposed methodology in enhancing the utility of event cameras for image reconstruction and other downstream tasks, paving the way for hardware implementation of dynamic feedback EVS control in silicon.

cs.CV↗

arXiv2020

Frequency-Dependent Attenuation Reconstruction with an Acoustic Reflector

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04dB/cm at 1MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, consistently from different images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as facilitate attenuation compensation of other ultrasound-based imaging techniques.

physics.med-ph↗

arXiv2020

Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction

Speed-of-sound has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. Speed-of-sound images can be reconstructed from time-of-flight measurements from ultrasound images acquired using conventional handheld ultrasound transducers. Variational Networks (VN) have recently been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction. Despite earlier promising results, these methods however do not generalize well from simulated to acquired data, due to the domain shift. In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access. This is made possible by incorporating simulations with varying complexity into training. We use loop unrolling of gradient descent with momentum, with an exponentially weighted loss of outputs at each unrolled iteration in order to regularize training. We learn norms as activation functions regularized to have smooth forms for robustness to input distribution variations. We evaluate reconstruction quality on ray-based and full-wave simulations as well as on tissue-mimicking phantom data, in comparison to a classical iterative (L-BFGS) optimization of this image reconstruction problem. We show that the proposed regularization techniques combined with multi-source domain training yield substantial improvements in the domain adaptation capabilities of VN, reducing median RMSE by 54% on a wave-based simulation dataset compared to the baseline VN. We also show that on data acquired from a tissue-mimicking breast phantom the proposed VN provides improved reconstruction in 12 milliseconds.

eess.IV↗

arXiv2020

Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

Simulation-based ultrasound training can be an essential educational tool. Realistic ultrasound image appearance with typical speckle texture can be modeled as convolution of a point spread function with point scatterers representing tissue microstructure. Such scatterer distribution, however, is in general not known and its estimation for a given tissue type is fundamentally an ill-posed inverse problem. In this paper, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed ultrasound data. We herein propose to impose a known statistical distribution on scatterers and learn the mapping between ultrasound image and distribution parameter map by training a convolutional neural network on synthetic images. In comparison with several existing approaches, we demonstrate in numerical simulations and with in-vivo images that the synthesized images from scatterer representations estimated with our approach closely match the observations with varying acquisition parameters such as compression and rotation of the imaged domain.

cs.CV↗

arXiv2020

Deep variational network for rapid 4D flow MRI reconstruction

Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated three-dimensional (3D) volume sampling over cardiac phases and breathing cycles necessitate accelerated imaging techniques that leverage data correlations. Standard compressed sensing reconstruction methods require tuning of hyperparameters and are computationally expensive, which diminishes the potential reduction of examination times. We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data. The network is shown to reconstruct undersampled 4D flow MRI data in under a minute on standard consumer hardware. Remarkably, the relatively low amounts of tunable parameters allowed the network to be trained on images from 11 reference scans while generalizing well to retrospective and prospective undersampled data for various acceleration factors and anatomies.

eess.IV↗

arXiv2019

Speed-of-Sound Imaging using Diverging Waves

Recent ultrasound imaging modalities based on ultrasound computed tomography indicate a huge potential to detect pathologies is tissue due to altered biomechanical properties. Especially the imaging of speed-of-sound (SoS) distribution in tissue has shown clinical promise and thus gained increasing attention in the field -- with several methods proposed based on transmission mode tomography. SoS imaging using conventional ultrasound (US) systems would be convenient and easy for clinical translation, but this requires using conventional US probes with single-sides tissue access and thus pulse-echo imaging sequences. Recent pulse-echo SoS imaging methods rely on plane wave (PW) insonifications, which is prone to strong aberration effects for non-homogeneous tissue composition. In this paper we propose to use diverging waves (DW) for SoS imaging and thus substantially improve the reconstruction of SoS distributions. We study this proposition by first plane wavefront aberrations compared to DW. We then present the sensitivity of both approaches to major parameterization choices on a set of simulated phantoms. Using the optimum parameter combination for each method for a given transducer model and imaging sequence, we analyze the SoS imaging performance comparatively between the two approaches. Results indicate that using DW instead of PW, the reconstruction accuracy improves substantially, by over 22% in reconstruction error (RMSE) and by 55% in contrast (CNR). We also demonstrate improvements in SoS reconstructions from an actual US acquisition of a breast phantom with tumor- and cyst-representative inclusions, with high and low SoS contrast, respectively.

physics.med-ph↗

arXiv2019

Ultrasound Aberration Correction based on Local Speed-of-Sound Map Estimation

For beamforming ultrasound (US) signals, typically a spatially constant speed-of-sound (SoS) is assumed to calculate delays. As SoS in tissue may vary relatively largely, this approximation may cause wavefront aberrations, thus degrading effective imaging resolution. In the literature, corrections have been proposed based on unidirectional SoS estimation or computationally-expensive a posteriori phase rectification. In this paper we demonstrate a direct delay correction approach for US beamforming, by leveraging 2D spatial SoS distribution estimates from plane-wave imaging. We show both in simulations and with ex vivo measurements that resolutions close to the wavelength limit can be achieved using our proposed local SoS-adaptive beamforming, yielding a lateral resolution improvement of 22% to 29% on tissue samples with up to 3% SoS-contrast (45m/s). We verify that our method accurately images absolute positions of tissue structures down to sub-pixel resolution of a tenth of a wavelength, whereas a global SoS assumption leads to artifactual localizations.

eess.IV↗

arXiv2019

Attenuation Imaging with Pulse-Echo Ultrasound based on an Acoustic Reflector

Ultrasound attenuation is caused by absorption and scattering in tissue and is thus a function of tissue composition, hence its imaging offers great potential for screening and differential diagnosis. In this paper we propose a novel method that allows to reconstruct spatial attenuation distribution in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode, thus it can be implemented on conventional ultrasound systems with minor modifications. We use calibration with water measurements in order to normalize measurements for quantitative imaging of attenuation. In contrast to earlier techniques, we herein show that attenuation reconstructions are possible without any geometric prior on the inclusion location or shape. We present a quantitative evaluation of reconstructions based on simulations, gelatin phantoms, and ex-vivo bovine skeletal muscle tissue, achieving contrast-to-noise ratio of up to 2.3 for an inclusion in ex-vivo tissue.

eess.SP↗

arXiv2019

Deep Variational Networks with Exponential Weighting for Learning Computed Tomography

Tomographic image reconstruction is relevant for many medical imaging modalities including X-ray, ultrasound (US) computed tomography (CT) and photoacoustics, for which the access to full angular range tomographic projections might be not available in clinical practice due to physical or time constraints. Reconstruction from incomplete data in low signal-to-noise ratio regime is a challenging and ill-posed inverse problem that usually leads to unsatisfactory image quality. While informative image priors may be learned using generic deep neural network architectures, the artefacts caused by an ill-conditioned design matrix often have global spatial support and cannot be efficiently filtered out by means of convolutions. In this paper we propose to learn an inverse mapping in an end-to-end fashion via unrolling optimization iterations of a prototypical reconstruction algorithm. We herein introduce a network architecture that performs filtering jointly in both sinogram and spatial domains. To efficiently train such deep network we propose a novel regularization approach based on deep exponential weighting. Experiments on US and X-ray CT data show that our proposed method is qualitatively and quantitatively superior to conventional non-linear reconstruction methods as well as state-of-the-art deep networks for image reconstruction. Fast inference time of the proposed algorithm allows for sophisticated reconstructions in real-time critical settings, demonstrated with US SoS imaging of an ex vivo bovine phantom.

eess.IV↗

arXiv2018

Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging

Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in vivo perfusion quantification with magnetic resonance imaging (MRI). However, its use is limited by typically low accuracy due to low signal-to-noise ratio (SNR) at large gradient encoding magnitudes as well as dephasing artefacts caused by subject motion, which is particularly challenging in fetal MRI. To mitigate this problem, we propose an implicit IVIM signal acquisition model with which we learn full posterior distribution of perfusion parameters using artificial neural networks. This posterior then encapsulates the uncertainty of the inferred parameter estimates, which we validate herein via numerical experiments with rejection-based Bayesian sampling. Compared to state-of-the-art IVIM estimation method of segmented least-squares fitting, our proposed approach improves parameter estimation accuracy by 65% on synthetic anisotropic perfusion data. On paired rescans of in vivo fetal MRI, our method increases repeatability of parameter estimation in placenta by 46%.

cs.CV↗

arXiv2018

Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging

Speed-of-sound is a biomechanical property for quantitative tissue differentiation, with great potential as a new ultrasound-based image modality. A conventional ultrasound array transducer can be used together with an acoustic mirror, or so-called reflector, to reconstruct sound-speed images from time-of-flight measurements to the reflector collected between transducer element pairs, which constitutes a challenging problem of limited-angle computed tomography. For this problem, we herein present a variational network based image reconstruction architecture that is based on optimization loop unrolling, and provide an efficient training protocol of this network architecture on fully synthetic inclusion data. Our results indicate that the learned model presents good generalization ability, being able to reconstruct images with significantly different statistics compared to the training set. Complex inclusion geometries were shown to be successfully reconstructed, also improving over the prior-art by 23% in reconstruction error and by 10% in contrast on synthetic data. In a phantom study, we demonstrated the detection of multiple inclusions that were not distinguishable by prior-art reconstruction, meanwhile improving the contrast by 27% for a stiff inclusion and by 219% for a soft inclusion. Our reconstruction algorithm takes approximately 10ms, enabling its use as a real-time imaging method on an ultrasound machine, for which we are demonstrating an example preliminary setup herein.

cs.CV↗