Search arXiv⌕ Search

arXiv · physics/0410084

GPCALMA: A Tool For Mammography With A GRID-Connected Distributed Database

Abstract

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used for acquire new images, as archive and to perform statistical analysis. The images are completely described: pathological ones have a consistent characterization with radiologist's diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions analysis and microcalcification clusters analysis. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as "second reader" will also be presented.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

U. Bottigli, P. Cerello, S. Cheran, P. Delogu, M. E. Fantacci, F. Fauci, B. Golosio, A. Lauria, E. Lopez Torres, R. Magro, G. L. Masala, P. Oliva, R. Palmiero, G. Raso, A. Retico, S. Stumbo, S. Tangaro. 2004-10-13. GPCALMA: A Tool For Mammography With A GRID-Connected Distributed Database. https://doi.org/10.1063/1.1615100

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Evaluation of Image Noise from Computed Tomography in an Organ-Specific Anthropomorphic Phantom

Computed tomography (CT) is used ubiquitously for diagnostic imaging, with novel iterative reconstructions such as hybrid iterative reconstruction with partial model-based component (ASiR-V) and deep learning image reconstruction (DLIR) increasingly utilized. In conjunction, CT vendors have developed a noise index (NI) to reduce radiation dose during protocol development. However, it is unclear how these reconstruction algorithms reduce noise as NI changes, particularly across multiple organs. The goal of this study was to utilize an anthropomorphic phantom to evaluate image noise against the prescribed NI across different organs, determine if DLIR reduces image noise compared to ASiR-V, and use this relationship to determine the maximum volume CT dose index (CTDIvol) reduction at equivalent noise levels. Repeat images were acquired on a GE HealthCare Revolution Apex CT scanner across NI values in an anthropomorphic adult-sized phantom from Kyoto Kagaku with ASiR-V and DLIR. Organs-of-interest were contoured using TotalSegmentator in 3D Slicer. The correlation between the CTDIvol and NI was determined. The standard deviation of the difference in the repeat images was used as a noise metric (NM), correlated with NI, used to determine the NI at equivalent NM between ASiR-V and DLIR, then applied to determine the dose reduction and difference in NM. The relationship between NI and the resulting CTDIvol was found to be proportional to a power law of exponent -2.23. The maximum potential absolute dose reduction from using DLIR was found in the liver at 1.3 mGy with an NI of 7.9. Across all organs, the relative dose reduction increased, the NM-to-NI ratio decreased, and the noise reduction increased across NI. The relationship between image noise and the prescribed NI differed between ASiR-V and DLIR and varied by organ, with model-based dose reductions of up to 1.3 mGy in the liver.

physics.med-ph↗

Constrained estimation of rotational invariants of the cumulant expansion (RICE) for rapid tensor-valued diffusion MRI

Purpose: To complement 1.5-minute measurements of common tensor-valued diffusion MRI (dMRI) markers with rapid constrained fitting. Methods: Fast dMRI protocols for obtaining rotational invariants of the cumulant expansion (RICE) were paired with constrained weighted linear least squares (CWLLS) to stabilize the more fragile WLLS fit. A compact constraint set was formulated, including a novel mean-dependent upper bound on total diffusional variance. Evaluation used diffusion tensor distribution (DTD) simulations, healthy-volunteer data with a resolution-dependent SNR experiment, and a glioma patient dataset. A 5-minute q-space trajectory imaging (QTI) protocol served as a reference. Results: Across experiments, CWLLS reduced unphysical estimates and fit outliers in parameters such as microscopic FA and isotropic diffusivity variance. In simulations, it narrowed error distributions most clearly in the CSF-dominant case, while some metrics showed a bias-variance trade-off. In vivo, CWLLS removed negative variance estimates, truncated out-of-bounds tails, and reduced artifacts in fluid-contaminated voxels while preserving anatomical contrast. It also retained more stable maps than WLLS at higher resolution, although both estimators degraded in the lowest-SNR setting. Notably, the new mean-dependent variance bound was violated in 15.4% of voxels in the patient dataset, accounting for nearly half of the 32.7% that violated at least one constraint. Healthy-volunteer benchmarking showed that CWLLS completed in under 30 seconds. The constrained QTI fit required 72 minutes, making CWLLS 160 times faster. Conclusion: CWLLS for fast RICE yielded high-quality parameter maps at an online-ready computational cost. This may enhance the reliability of dMRI tissue characterization and strengthen the path toward clinical translation.

physics.med-ph↗

Estimation of Very High Energy Electron (VHEE) beam parameters using an in-air screen

Very high energy electron (VHEE) beams offer several promising properties for radiotherapy. Accurate beam delivery requires a beam model that accounts for scattering along the beam propagation path. This work develops and validates a beam model incorporating Multiple Coulomb Scattering (MCS) to estimate beam parameters. MCS is incorporated as an additive term to the covariance matrix transformation for a drift space, allowing conventional quadrupole scans to estimate beam parameters in the presence of a downstream scatterer. The scattering correction factor is fitted to Monte Carlo (MC) simulations for a range of materials and thicknesses. The model is then validated by recovering the initial beam parameters from simulated quadrupole scans of a realistic beamline. Finally, the method is applied to in-air screen measurements at the Pulsed Energetic Electrons for Research (PEER) end station of the Australian Synchrotron. After fitting the correction factors, the mean and standard deviation of the estimation errors for the final emittance, diameter, divergence, and mean kinetic energy were $(-0.06 \pm 3.17)\%$, $(0.15 \pm 0.66)\%$, $(0.18 \pm 2.18)\%$, and $(-0.03 \pm 0.06)\%$, respectively. Simulated quadrupole scans successfully recovered the initial beam parameters, with errors of at most $13\%$. PEER measurements yielded emittance estimates ranging from $0.238$ to $1.753~\mathrm{mm\,mrad}$ across scans, which were within the expected range. Following an initial quadrupole scan, the method allows the in-air beam size to be estimated for other quadrupole configurations. Further work is required to improve the consistency and reliability of the experimental estimates.

physics.med-ph↗