arXiv subjects

Sizhuo Liu

Publications and source records attributed to Sizhuo Liu.

5 recordsLinked to original sources

arXiv2026

Analytic Cut in Epistemic Logics with Distributed Knowledge

Distributed knowledge is a notion of group knowledge studied in multi-agent epistemic logic. Semantically, the distributed knowledge of a group is interpreted via an accessibility relation given by the intersection of the epistemic accessibility relations of the agents in that group. This paper investigates sequent calculi for epistemic logics of distributed knowledge based on K45, KD45, and S5. While cut elimination holds in existing sequent calculi for modal logics K45 and KD45, it fails in all the systems mentioned above. Instead, we establish the analytic cut property for all three systems by adapting Takano' s (2018) strategy, which restricts the cut formulas to the set of subformulas of the conclusion of the cut rule. As a corollary, the Craig interpolation theorem holds for all logics considered. We also show that all proof-theoretic results remain valid when the empty group is allowed for the distributed-knowledge operator, in which case the distributed knowledge for the empty group is interpreted as the global modality.

cs.MA↗

arXiv2025

Whole Heart Perfusion with High-Multiband Simultaneous Multislice Imaging via Linear Phase Modulated Extended Field of View (SMILE)

Purpose: To develop a simultaneous multislice (SMS) first-pass perfusion technique that can achieve whole heart coverage with high multi-band factors, while avoiding the issue of slice leakage. Methods: The proposed Simultaneous Multislice Imaging via Linear phase modulated Extended field of view (SMILE) treats the SMS acquisition and reconstruction within an extended field of view framework, allowing arbitrarily under-sampling of phase encoding lines of the extended k-space matrix and enabling the direct application of 2D parallel imaging reconstruction techniques. We presented a theoretical framework that offers insights into the performance of SMILE. We performed retrospective comparison on 28 subjects and prospective perfusion experiments on 43 patients undergoing routine clinical CMR studies with SMILE at multiband (MB) factors of 3-5, with a net acceleration rate ($R$) of 8 and 10 respectively, and compared SMILE to conventional SMS techniques using standard FOV 2D CAIPI acquisition and standard 2D slice separation techniques including split-slice GRAPPA and ROCK-SPIRiT. Results: Retrospective studies demonstrated 5.2 to 8.0 dB improvement in signal to error ratio (SER) of SMILE over CAIPI perfusion. Prospective studies showed good image quality with grades of 4.1 $\pm$ 0.7 for MB = 3, $R$ = 8 and 3.5 $\pm$ 1.0 for MB = 5, $R$ = 10. (5-point Likert Scale) Conclusion: The theoretical derivation and experimental results validate the SMILE's improved performance at high acceleration and MB as compared to the existing 2D CAIPI SMS acquisition and reconstruction techniques for first-pass myocardial perfusion imaging.

eess.SP↗

arXiv2024

MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data

Objective: Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. Materials and Methods: ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe iteratively trains the denoiser on the image or images that are being reconstructed. We introduce two variations of our method: ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements and provides faster inference. Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. Results: ReSiDe-S and ReSiDe-M outperform other methods in terms of peak signal-to-noise ratio and structural similarity index measure for Studies I and II, and in terms of expert scoring for Study III. Discussion: We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.

eess.IV↗

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.

eess.IV↗

arXiv2020

Free-breathing Cardiovascular MRI Using a Plug-and-Play Method with Learned Denoiser

Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality that provides a comprehensive evaluation of the cardiovascular system. The clinical utility of CMR is hampered by long acquisition times, however. In this work, we propose and validate a plug-and-play (PnP) method for CMR reconstruction from undersampled multi-coil data. To fully exploit the rich image structure inherent in CMR, we pair the PnP framework with a deep learning (DL)-based denoiser that is trained using spatiotemporal patches from high-quality, breath-held cardiac cine images. The resulting "PnP-DL" method iterates over data consistency and denoising subroutines. We compare the reconstruction performance of PnP-DL to that of compressed sensing (CS) using eight breath-held and ten real-time (RT) free-breathing cardiac cine datasets. We find that, for breath-held datasets, PnP-DL offers more than one dB advantage over commonly used CS methods. For RT free-breathing datasets, where ground truth is not available, PnP-DL receives higher scores in qualitative evaluation. The results highlight the potential of PnP-DL to accelerate RT CMR.

eess.IV↗