G. Mettivier, R. Ricciarci, A. Sarno, F. Maddaloni, M. Porzio, M. Staffa, S. Minelli, A. Santoro, E. Antignani, M. Masi, V. Landoni, P. Ordonez, F. Ferranti, L. Greco, S. Clemente, P. Russo
The aim of the DeepLook project, funded by INFN (Italy), is to implement a deep learning architecture for Computed Aided Detection (CAD), based on neural networks developed with deep learning methods, for the automatic detection and classification of breast lesions in DBT images. A preliminary step (started 2 years ago and still ongoing) was the creation of a dataset of annotated images. This dataset includes images acquired with different clinical DBT units and different acquisition geometries, on several hundred patients, containing a variety of possible breast lesions and normal cases of absence of lesions. This will make the diagnostic capacity of the CAD system particularly extensive in various clinical situations and on a significant sample of patients, so allowing the network to diagnose various types of lesions (at the level of the single tomosynthesis slices) and capable of operate on commercial DBT systems, also available from different vendors, as found in breast diagnosis departments. The developed CAD and first result of the indication of the slice containing the suspected mass will be presented.
This work aims at evaluating the spatial resolution and noise in 3D images acquired with a clinical Computed Tomography scanner dedicated to the breast (BCT). The presampled modulation transfer function (MTF) and the noise power spectrum (NPS) are measured. In addition, the capability of the system in showing simulated lesions and microcalcification clusters was assessed via a phantom test. The impact of the selected reconstruction algorithm on MTF, NPS, and simulated lesion visibility was evaluated. The available algorithms are the Standard (Std) and Calcification (Calc) reconstructions, which use an isotropic reconstructed voxel edge of 0.273 mm and the high-resolution (HR) reconstruction algorithm that uses an isotropic reconstructed voxel edge of 0.190 mm. The spatial frequency (expressed in mm-1 ) at which the MTF curve goes down to 10% (MTF10%) was found to be 1.0 mm-1 for the case of Std reconstruction in radial direction at the chest-wall; this value increases to 1.3 mm-1 and 1.5 mm-1 for the HR and Calc reconstructions, respectively. The distance from the isocenter did not impact the system spatial resolution. As expected, the improvement in the spatial resolution in the Calc and HR reconstruction algorithms is accompanied by an increase in the noise, especially at the higher frequencies, as shown in the 1D NPS. A phantom study showed that both simulated soft lesion with diameter of 1.8 mm and microcalcification cluster with grain diameter of 0.29 mm are visible, no matter what reconstruction algorithm is selected. Microcalcifications with diameter of 0.20 mm and 0.13 mm do not appear to be visible.
KEYWORDS: Sensors, Modulation transfer functions, Monte Carlo methods, Scanners, Breast, 3D image processing, 3D modeling, Spatial resolution, Computer simulations, 3D scanning
This work proposes an empirical model for tuning spatial resolution and noise in simulated images in virtual clinical trials in x-ray breast imaging. In extending previous studies performed for direct conversion a-Se detectors used in digital mammography and digital breast tomosynthesis, this work introduces the model for the case of cone-beam computed tomography dedicated to the breast that uses a indirect conversion flat-panel detector. In the simulations, the detector is modeled as an absorbing layer whose material and thickness reflect those of the scintillator of the detector of a clinical scanner. The simulated images are then computed as a dose deposit map. The detector response curve, modulation transfer function (MTF) and noise power spectrum (NPS) were measured on a real detector. The same measurements were replicated in-silico for the simulated detector and scanner. The comparison of simulated and measured detector response curves permits to recover pixel values at the clinical scale. The difference between the simulated and measured MTFs permitted to introduce a linear filter for compensating simulated model simplification that determines a better spatial resolution in the simulated images with respect to real images. This filter presented a Gaussian shape in the Fourier domain with a standard deviation of 1.09 mm-1 , derived from those of the measured and simulated MTF curves, of 0.86 mm-1 and 1.41 mm-1 , respectively. Finally, the analysis of the NPS permits to compensate for noise characteristics due to the simulated model simplifications. The model applied to the simulated projection images produced MTF and normalized NPS in simulated 3D images, comparable to those obtained for the clinical scanner.
KEYWORDS: Breast, Monte Carlo methods, Sensors, Tissues, Digital breast tomosynthesis, Clinical trials, X-rays, 3D image processing, X-ray imaging, Breast imaging
In silico reproductions of clinical exams represent an alternative strategy in the research and development of medical devices, which permit to avoid issues and costs related to clinical trials on patient population. In this work, we present a platform for virtual clinical trials in 2D and 3D x-ray breast imaging. The platform, developed by the medical physics team at University of Naples, Italy, permits to simulate digital mammography (DM), digital breast tomosynthesis (DBT) and CT dedicated to the breast (BCT) examinations. It relies on Monte Carlo simulations based on Geant4 toolkit and adopts digital models of patients derived from high-resolution 3D clinical breast images acquired at UC Davis, USA. Uncompressed digital breast models for BCT exam simulations were produced by means of a tissue classification algorithm; the compressed digital breast models for simulating DM and DBT are derived by the uncompressed ones via a simulated tissue compression. For a selected exam, specifications and digital patient, the platform computes breast image projections and glandular dose maps within the organ. Energy integrating a well as photon counting and spectral imaging detection scheme have been simulated. The current version of the software uses the Geant4 standard physics list Option4 and simulates and tracks <105 photons/s, when run on a 16-core CPU at 3.0 GHz. The developed platform will be an invaluable tool for R and D of apparatuses, and it will permit the access to clinical-like data to a broad research community. Digital patient exposures with the available phantom dataset will be possible for the same patient-derived phantom in uncompressed or compressed format, in DM, DBT and BCT modalities.
We are developing a platform for virtual (in silico) clinical trials in x-ray breast imaging and dosimetry fully based on Monte Carlo simulations and which adopts patient-like digital phantoms. We report the initial steps and first results of this project. Our collaboration was established for producing a “user-friendly” computational platform which reproduces both 2D (full-fields digital mammography) and 3D (digital breast tomosynthesis, CT dedicated to the breast) breast imaging examinations for technique optimization and for developing protocols and computed aided diagnostic applications. Dose optimization strategies and technique performance comparisons will be explored. A dataset of anatomically realistic, 2D and 3D digital breast phantoms have been produced by means of voxel classification and digital compression of clinical breast CT scans acquired at UC Davis. Monte Carlo simulations, based on the Geant4 toolkit, have been developed for insilico FFDM, DBT and BCT examinations.
KEYWORDS: Electron beams, X-rays, Laser systems engineering, X-ray sources, S band, Monte Carlo methods, Optical simulations, Hard x-rays, X-ray imaging, Compton scattering, Particle accelerators
There is a strong demand for small foot-print high-flux hard X-rays machines in order to enable a large variety of science activities and serve a multidisciplinary user community. For this purpose, two compact Inverse Compton Sources (ICSs) are currently being developed in Italy. The most recent one is the Bright and Compact X-ray Source (BriXS) which has recently been proposed to produce very energetic X-rays (up to 180 keV) and high photon flux (up to 1013 photons/s with expected bandwidth of 1-10%). BriXS will be installed in Milan and it will enable advanced large area radiological imaging applications to be conducted with mono-chromatic X-rays, as well as allowing basic fundamental science of matter and health sciences at both pre- and clinical levels. Based on an energy-recovery linac (ERL) scheme and superconducting technology, BriXS will operate in CW regime with an unprecedented electron beam repetition rate of 100 MHz. The second Italian ICS light source is the Southern Europe Thomson back-scattering source for Applied Research (STAR) which is currently installed at the University of Calabria (UniCal). STAR is a compact machine that has been designed to produce monochromatic and tunable, ps-long, polarized X-ray beams in the range 40-140 keV with a photon flux up to 1010 photons/s and energy bandwidth below 10%. The electron beam injector is based on normal-conducting technology in S-Band with a repetition rate up to 100 Hz.
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