An optimal control framework for joint-channel parallel MRI reconstruction without coil sensitivities

Bian, Wanyu; Chen, Yunmei; Ye, Xiaojing

doi:10.1016/j.mri.2022.01.011

Cited by 9 publications

(9 citation statements)

References 31 publications

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“…J is the coil combination network that integrates the multi-coil images to produce a coil-combined image [3]. Each task is associated with a task-specific J, and the resulting coil combined image J wi ({x j }) for i-th task is input into the high-dimensional meta-learner H to extract meta-knowledge Θ.…”

Section: Methodology and Algorithmmentioning

confidence: 99%

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

Bian

Liu

et al. 2021

Magnetic Resonance Imaging

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Background: CD8 + T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8 + T-cell expression can better identify the population benefitting from immunotherapy. Purpose: To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8 + T-cell expression in patients with PDAC. Study Type: Retrospective cohort study. Population: Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. Field Strength/Sequence/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. Assessment: CD8 + T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1-and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. Statistical Tests: Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. Results: A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). Data Conclusions: This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8 + T-cell expression in patients with PDAC and potentially immunotherapy planning.

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Section: Methodology and Algorithmmentioning

confidence: 99%

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

Bian

Liu

et al. 2021

Magnetic Resonance Imaging

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“…where the first term in (1) is the data fidelity for the source modalities that ensures consistency between the reconstructed images {x 1 , x 2 } and the sensed partial k-space data {f 1 , f 2 }. Here, F stands for the discrete Fourier transform and P i is the binary matrix representing the k-space mask when acquiring data for x i .…”

Section: Modelmentioning

confidence: 99%

“…In this section, we present a novel and efficient learnable optimization algorithm (LOA) for solving the nonconvex nonsmooth minimization problem (1). (Comprehensive convergence analysis of this algorithm is provided in Supplementary Material.)…”

Section: Efficient Learnable Optimization Algorithmmentioning

confidence: 99%

“…To overcome the weakness of the end-toend black-box networks, several learnable optimization algorithms (LOAs) have been developed attracted much attention, which possess of a more interpretable network architecture. LOA-based reconstruction methods unfold the iterative optimization algorithm into a multi-phase network in which the regularization and image transformation are learned to improve the network performance [15,2,4,1], e.g. ADMM-Net [20], ISTA-Net + [26] and PD-Net [5].…”

Section: Introductionmentioning

confidence: 99%

“…In order to synthesize target modality by using partially scanned k-space data from source modalities in stead of fully scanned data that used in the state-ofthe-art multimodal synthesis, in this paper, we propose to jointly reconstruct undersampled multiple source modality MR images and synthesize the target modality image. Our contributions are summarized as follows: (1) We propose a novel LOA for joint multimodal MRI reconstruction and synthesis with theoretical analysis guarantee; (2) The parameters and hyper-parameters of the network induced by our LOA are learned using a bilevel optimization algorithm robust parameter training; (3) Extensive experimental results demonstrate the efficiency of the proposed method and high quality of the reconstructed/synthesized images.…”

Section: Introductionmentioning

confidence: 99%

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A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Bian¹,

Zhang²,

Ye³

et al. 2022

Preprint

Self Cite

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Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.

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A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Bian¹,

Zhang²,

Ye³

et al. 2022

Lecture Notes in Computer Science

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An optimal control framework for joint-channel parallel MRI reconstruction without coil sensitivities

Cited by 9 publications

References 31 publications

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

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An optimal control framework for joint-channel parallel MRI reconstruction without coil sensitivities

Cited by 9 publications

References 31 publications

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

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Product

Resources

About

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging

Preoperative Radiomics Approach to Evaluating Tumor‐Infiltrating CD8⁺ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging