. The goal of this study was to assess the imaging performances of the pCT system developed in the framework of INFN-funded (Italian National Institute of Nuclear Physics) research projects. The spatial resolution, noise power spectrum (NPS) and RSP accuracy has been investigated, as a preliminary step to implement a new cross-calibration method for x-ray CT (xCT).. The INFN pCT apparatus, made of four planes of silicon micro-strip detectors and a YAG:Ce scintillating calorimeter, reconstructs 3D RSP maps by a filtered-back projection algorithm. The imaging performances (i.e. spatial resolution, NPS and RSP accuracy) of the pCT system were assessed on a custom-made phantom, made of plastic materials with different densities ((0.66, 2.18) g cm). For comparison, the same phantom was acquired with a clinical xCT system.. The spatial resolution analysis revealed the nonlinearity of the imaging system, showing different imaging responses in air or water phantom background. Applying the Hann filter in the pCT reconstruction, it was possible to investigate the imaging potential of the system. Matching the spatial resolution value of the xCT (0.54 lp mm) and acquiring both with the same dose level (11.6 mGy), the pCT appeared to be less noisy than xCT, with an RSP standard deviation of 0.0063. Concerning the RSP accuracy, the measured mean absolute percentage errors were (0.23+-0.09)% in air and (0.21+-0.07)% in water.. The obtained performances confirm that the INFN pCT system provides a very accurate RSP estimation, appearing to be a feasible clinical tool for verification and correction of xCT calibration in proton treatment planning.

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-6560/acd6d3DOI Listing

Publication Analysis

Top Keywords

spatial resolution
16
pct system
12
rsp accuracy
12
imaging performances
8
nps rsp
8
infn pct
8
pct
6
system
6
rsp
6
imaging
5

Similar Publications

Purpose: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).

View Article and Find Full Text PDF

Confocal Raman Microscopy with Adaptive Optics.

ACS Photonics

January 2025

Institute of Biomedical Physics, Medical University of Innsbruck, Müllerstraße 44, 6020 Innsbruck, Austria.

Confocal Raman microscopy, a highly specific and label-free technique for the microscale study of thick samples, often presents difficulties due to weak Raman signals. Inhomogeneous samples introduce wavefront aberrations that further reduce these signals, requiring even longer acquisition times. In this study, we introduce Adaptive Optics to confocal Raman microscopy for the first time to counteract such aberrations, significantly increasing the Raman signal and image quality.

View Article and Find Full Text PDF

Spatial datasets of CMIP6 climate change projections for Canada and the United States.

Data Brief

February 2025

Great Lakes Forestry Centre, Natural Resources Canada, 1219 Queen Street East, Sault Ste Marie, Ontario P6A 2E5, Canada.

Geospatial climate change projections are critical for assessing climate change impacts and adaptations across a wide range of disciplines. Here we present monthly-based grids of climate change projections at a 2-km resolution covering Canada and the United States. These data products are based on outputs from the 6th Coupled Model Intercomparison Project (CMIP6) and include projections for 13 General Circulation Models (GCMs), three Shared Socio-economic Pathways (SSP1 2.

View Article and Find Full Text PDF

Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling.

View Article and Find Full Text PDF

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!