Position-Prior-Guided Network for System Matrix Super-Resolution in Magnetic Particle Imaging
Magnetic Particle Imaging (MPI) is a novel medical imaging modality. One of the established methods for MPI reconstruction is based on the System Matrix (SM). However, the calibration of the SM is often time-consuming and requires repeated measurements whenever the system parameters change. Current methodologies utilize deep learning-based super-resolution (SR) techniques to expedite SM calibration; nevertheless, these strategies do not fully exploit physical prior knowledge associated with the SM, such as symmetric positional priors. Consequently, we integrated positional priors into existing frameworks for SM calibration. Underpinned by theoretical justification, we empirically validated the efficacy of incorporating positional priors through experiments involving both 2D and 3D SM SR methods.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have be…
ClassificationCompressed Computation is (probably) not Computation in Superposition
We study whether the Compressed Computation (CC) toy model (Braun et al., 2025) is an instance of computation in superposition. The CC model appears to compute 100 ReLU functions with just 50 neurons, achieving a better …
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning
The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions. In this …
Compositional Zero-Shot LearningMissing LabelsZero-Shot LearningFreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior
We offer a novel approach to image composition, which integrates multiple input images into a single, coherent image. Rather than concentrating on specific use cases such as appearance editing (image harmonization) or se…
DenoisingImage HarmonizationDecomposing Temperature Time Series with Non-Negative Matrix Factorization
During the fabrication of casting parts sensor data is typically automatically recorded and accumulated for process monitoring and defect diagnosis. As casting is a thermal process with many interacting process parameter…
Time SeriesTime Series Analysis