Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K values ranging from 0.01 to 0.8 min and fixed V = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K and V. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.
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http://dx.doi.org/10.3390/tomography8030128 | DOI Listing |
We propose and demonstrate a photonic compressive sensing (PCS) scheme for microwave signals using optical pulse random mixing, significantly enhancing both the compression ratio and operating frequency range. Unlike continuous-wave laser-based PCS systems, our approach mitigates the non-ideal characteristics of the pseudo-random binary sequence (PRBS), such as sloped edges and amplitude jitters, resulting in a more ideal compression process. Additionally, the high harmonic components of the optical pulses further facilitate wideband downconversion, improving the system's operating frequency range.
View Article and Find Full Text PDFEur Heart J Imaging Methods Pract
January 2025
A.I. Virtanen Institute, University of Eastern Finland, Neulaniementie 2, 70210 Kuopio, Finland.
Aims: The aim of this study was to develop an ultra-short echo time 3D magnetic resonance imaging (MRI) method for imaging subacute myocardial infarction (MI) quantitatively and in an accelerated way. Here, we present novel 3D T- and T -weighted Multi-Band SWeep Imaging with Fourier Transform and Compressed Sensing (MB-SWIFT-CS) imaging of subacute MI in mice hearts .
Methods And Results: Relaxation time-weighted and under-sampled 3D MB-SWIFT-CS MRI were tested with manganese chloride (MnCl) phantom and mice MI model.
Purpose: To develop a rapid, high-resolution and distortion-free quantitative $R_{2}^{*}$ mapping technique for fetal brain at 3 T.
Methods: A 2D multi-echo radial FLASH sequence with blip gradients is adapted for fetal brain data acquisition during maternal free breathing at 3 T. A calibrationless model-based reconstruction with sparsity constraints is developed to jointly estimate water, fat, $R_{2}^{*}$ and $B_{0}$ field maps directly from the acquired k-space data.
J Microsc
January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation, Southeast University, Nanjing 210096, China.
Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps.
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