Query: mri reconstruct

title: 3D BBPConvNet to reconstruct parallel MRI
abstract: In recent years, compressed sensing techniques have been applied to the reconstruction of parallel magnetic resonance (MR) images. Particularly for 3D MR signal, it is crucial to acquire fewer samples to reduce the distortions caused by long time acquisitions (e.g. motion, organ dynamics) Motivated by the recent success of ConvNet in 2D image reconstruction, we propose to extend the approach to 3D volume reconstruction and parallel MR imaging. The structure of the proposed network follows FBPConvNet with additional coil compression by SSoS and wavelet transform. A parallelism using two GPUs is also applied to overcome the memory shortage. The proposed method is able to reconstruct a (320 x 320 x 256 x 8) volume in less than 10s with 2 GPUs, while the iterative algorithm l1 ESPIRiT takes over 5 min in CPU.

title: 3D MRI brain tumor segmentation using autoencoder regularization
abstract: Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder decoder architecture. Due to a limited training dataset size, a variational auto encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.

title: DAGAN: Deep De Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
abstract: Compressed sensing magnetic resonance imaging (CS MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS MRI breaks the Nyquist Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning based strategy for reconstruction of CS MRI, and bridges a substantial gap between conventional non learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks based model (DAGAN) based model is proposed to reconstruct CS MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U Net based generator, which provides an end to end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real time processing.

title: Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network
abstract: Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN) based conjugate gradient SENSE (CG SENSE) reconstruction framework for motion correction in multishot MRI. First CG SENSE reconstruction is employed to reconstruct an image from the motion corrupted k space data and then the GAN based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several fold the computational time reported by the current state of the art technique.

title: An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
abstract: Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k space data, an iterative learning based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the <inline formula> <tex math notation="LaTeX"$8\times /tex math>/inline formula> accelerated multi coil, the <inline formula> <tex math notation="LaTeX"$4\times /tex math>/inline formula> multi coil, and the <inline formula> <tex math notation="LaTeX"$4\times /tex math>/inline formula> single coil tracks. This demonstrates the superior performance and wide applicability of the method.

title: Brain connections derived from diffusion MRI tractography can be highly anatomically accurate if we know where white matter pathways start, where they end, and where they do not go
abstract: MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that call into question its accuracy in many applications. Therefore, there has been intense interest in defining and mitigating the intrinsic limitations of the method. Recent studies have reported that tractography is inherently limited in its ability to accurately reconstruct the connections of the brain, when based on voxel averaged estimates of local fiber orientation alone. Several validation studies have confirmed that tractography techniques are plagued by both false positive and false negative connections. However, these validation studies which quantify sensitivity and specificity, particularly in animal models, have not utilized prior anatomical knowledge, as is done in the human literature, for virtual dissection of white matter pathways, instead assessing tractography implemented in a relatively unconstrained manner. Thus, they represent a worse case scenario for bundle segmentation techniques and may not be indicative of the anatomical accuracy in the process of bundle segmentation, where streamline filtering using inclusion and exclusion regions of interest is common. With this in mind, the aim of the current study is to investigate and quantify the upper bounds of accuracy using current tractography methods. Making use of the same dataset utilized in two seminal validation papers, we show that prior anatomical knowledge in the form of manually placed or template driven constraints can significantly improve the anatomical accuracy of estimated brain connections. Thus, we show that it is possible to achieve a high sensitivity and high specificity simultaneously, and conclude that current tractography algorithms, in combination with anatomically driven constraints, can result in reconstructions which very accurately reflect the ground truth white matter connections.

title: Deep variational network for rapid 4D flow MRI reconstruction
abstract: 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. 4D MRI scans can reconstruct cardiovascular flow, although they typically take many minutes, hindering real time assessment. Vishnevskiy et al. develop a deep variational network to permit high fidelity image reconstruction in a matter of seconds, allowing integration of 4D flow MRI into clinical workflows.

title: Deep Learning Based Optimization of the Under Sampling Pattern in MRI
abstract: In compressed sensing MRI (CS MRI) k space measurements are under sampled to achieve accelerated scan times. CS MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under sampled scan. In this article, we tackle both problems simultaneously for the specific case of 2D Cartesian sampling, using a novel end to end learning framework that we call LOUPE (Learning based Optimization of the Under sampling PattErn) Our method trains a neural network model on a set of full resolution MRI scans, which are retrospectively under sampled on a 2D Cartesian grid and forwarded to an anti aliasing (a.k.a. reconstruction) model that computes a reconstruction, which is in turn compared with the input. This formulation enables a data driven optimized under sampling pattern at a given sparsity level. In our experiments, we demonstrate that LOUPE optimized under sampling masks are data dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large scale, publicly available knee MRI dataset, where LOUPE offered superior reconstruction quality across different conditions. Even with an aggressive 8 fold acceleration rate, LOUPE's reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under sampling patterns that were significantly different for brain vs knee MRI scans. Our code is made freely available at https:/github.com/cagladbahadir/LOUPE/

title: Retaining information from multidimensional correlation MRI using a spectral regions of interest generator
abstract: Multidimensional correlation magnetic resonance imaging (MRI) is an emerging imaging modality that is capable of disentangling highly heterogeneous and opaque systems according to chemical and physical interactions of water within them. Using this approach, the conventional three dimensional MR scalar images are replaced with spatially resolved multidimensional spectra. The ensuing abundance in microstructural and chemical information is a blessing that incorporates a real challenge: how does one distill and refine it into images while retaining its significant components? In this paper we introduce a general framework that preserves the spectral information from spatially resolved multidimensional data. Equal weight is given to significant spectral components at the single voxel level, resulting in a summarized image spectrum. This spectrum is then used to define spectral regions of interest that are utilized to reconstruct images of sub voxel components. Using numerical simulations we first show that, contrary to the conventional approach, the proposed framework preserves spectral resolution, and in turn, sensitivity and specificity of the reconstructed images. The retained spectral resolution allows, for the first time, to observe an array of distinct {T}_{1} T 1 {T}_{2} T 2 \langle D\rangle D components images of the human brain. The robustly generated images of sub voxel components overcome the limited spatial resolution of MRI, thus advancing multidimensional correlation MRI to fulfilling its full potential.

title: Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi channel multi contrast MRI acquisitions
abstract: Multi contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time efficient strategy to acquire high quality multi contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage of features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi channel multi contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi channel multi contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single channel simulated and multi channel in vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage of features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.