Learning-based Optimization of the Under-sampling Pattern in MRI

Bahadir, Cagla Deniz; Dalca, Adrian V.; Sabuncu, Mert R.

doi:10.48550/arxiv.1901.01960

Cited by 3 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two basic variants of our SemuNet framework are built: (1) Baseline = fixed pattern + ReconNet + SegNet; and (2) LOUPESeg = LOUPE + Seg-Net. LOUPE is a recently proposed sampling pattern learning model driven by reconstruction [8]. We first trained LOUPE with high quality MR images and then SegNet is trained with the data generated by LOUPE.…”

Section: Methodsmentioning

confidence: 99%

“…The goal of SampNet is to optimize the sampling pattern for specific datasets in the k-space. To learn a probabilistic observation matrix T c in the k-space, we adopt the similar architecture to the [8,10,12] for our SampNet. The architecture of SampNet is shown in Fig.…”

Section: Sampnet: the Sampling Pattern Learning Networkmentioning

confidence: 99%

“…The architecture of ReconNet adopts the well-known U-Net [10] as the backbone as shown in Fig. 1b, which has demonstrated competitive performance in artifact reduction for MRI [8,14].…”

Section: Reconnet: the Reconstruction Networkmentioning

confidence: 99%

“…In [5,6], the authors coupled MRI reconstruction with segmentation to improve the performance of both tasks. On the other hand, some recent studies attempted to optimize the k-space sampling patterns with a data-driven manner [7,8,9] and the optimized undersampling patterns show significant improvements, compared to empirical ones. Unfortunately, the scheme of these trajectories only learned from the reconstruction stage ignore the tissue of interest.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

Wang

Xia

et al. 2021

Preprint

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notice that there are significant relevances among these tasks and this procedure artificially cuts off these potential connections, which may lead to losing clinically important information for the final diagnosis. To involve these potential relations for further performance improvement, a sequential multi-task joint learning network model is proposed to train a combined end-to-end pipeline in a differentiable way, aiming at exploring the mutual influence among those tasks simultaneously. Our design consists of three cascaded modules: 1) deep sampling pattern learning module optimizes the k-space sampling pattern with predetermined sampling rate; 2) deep reconstruction module is dedicated to reconstructing MR images from the undersampled data using the learned sampling pattern; 3) deep segmentation module encodes MR images reconstructed from the previous module to segment the interested tissues. The proposed model retrieves the latently interactive and cyclic relations among those tasks, from which each task will be mutually beneficial. The proposed framework is verified on MRB dataset, which achieves superior performance on other SOTA methods in terms of both reconstruction and segmentation.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Sampnet: the Sampling Pattern Learning Networkmentioning

confidence: 99%

“…The architecture of ReconNet adopts the well-known U-Net [10] as the backbone as shown in Fig. 1b, which has demonstrated competitive performance in artifact reduction for MRI [8,14].…”

Section: Reconnet: the Reconstruction Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

Wang

Xia

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The prospect of altering acquisition parameters to improve image quality has recently also been recognized for magnetic resonance imaging [1], where the undersampling pattern in k-space can be optimized via end-to-end learning with respect to fully sampled image. The approach closest to ours considers finding an optimal acoustic window for cardiac ultrasound [7], where the current image is interpreted by a reinforcement learning agent that suggests an ultrasound probe displacement towards a better acoustic window.…”

Section: Introductionmentioning

confidence: 99%

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

Zaech

Gao

Bier

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patientspecific manner, and describe the first step towards the envisioned taskaware CBCT protocol. The traditional short-scan CBCT trajectory is planar, with little room for scene-specific adjustment. We extend this trajectory by autonomously adjusting out-of-plane angulation. This enables C-arm source trajectories that are scene-specific in that they avoid acquiring "poor images", characterized by beam hardening, photon starvation, and noise. The recommendation of ideal out-of-plane angulation is performed on-the-fly using a deep convolutional neural network that regresses a detectability-rank derived from imaging physics.

show abstract

FReSCO: Flow Reconstruction and Segmentation for low‐latency Cardiac Output monitoring using deep artifact suppression and segmentation

Jaubert

Montalt-Tordera

Brown

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose Real‐time monitoring of cardiac output (CO) requires low‐latency reconstruction and segmentation of real‐time phase‐contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for “FReSCO” (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring). Methods Deep artifact suppression and segmentation U‐Nets were independently trained. Breath‐hold spiral phase‐contrast MR data (N = 516) were synthetically undersampled using a variable‐density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U‐net. A subset of the data (N = 96) was segmented and used to train the segmentation U‐net. Real‐time spiral phase‐contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed‐sensing CO. Results The FReSCO framework was demonstrated prospectively at the scanner. Beat‐to‐beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = −0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458). Conclusions The FReSCO framework was successfully demonstrated for real‐time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.

show abstract

Learning-based Optimization of the Under-sampling Pattern in MRI

Cited by 3 publications

References 0 publications

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

FReSCO: Flow Reconstruction and Segmentation for low‐latency Cardiac Output monitoring using deep artifact suppression and segmentation

Contact Info

Product

Resources

About