Patient-specific Monte Carlo-based organ dose estimates in spiral CT via optical 3D body scanning and adaptation of a voxelized phantom dataset: proof-of-principle.

Objective. We present a method for personalized organ dose estimates obtained before the computed tomography (CT) exam, via 3D optical body scanning and Monte Carlo (MC) simulations.Approach. A voxelized phantom is derived by adapting a reference phantom to the body size and shape measured with a portable 3D optical scanner, which returns the 3D silhouette of the patient. This was used as an external rigid envelope for incorporating a tailored version of the internal body anatomy derived from a phantom dataset (National Cancer Institute, NIH, USA) matched for gender, age, weight, and height. The proof-of-principle was conducted on adult head phantoms. The Geant4 MC code provided estimates of the organ doses from 3D absorbed dose maps in the voxelized body phantom.Main results. We applied this approach for head CT scanning using an anthropomorphic voxelized head phantom derived from 3D optical scans of manikins. We compared the estimates of head organ doses with those provided by the NCICT 3.0 software (NCI, NIH, USA). Head organ doses differed up to 38% using the proposed personalized estimate and MC code, with respect to corresponding estimates calculated for the standard (non-personalized) reference head phantom. Preliminary application of the MC code to chest CT scans is shown. Real-time pre-exam personalized CT dosimetry is envisaged with adoption of a Graphics Processing Unit-based fast MC code.Significance. The developed procedure for personalized organ dose estimates before the CT exam, introduces a new approach for realistic description of size and shape of patients via voxelized phantoms specific for each patient.

Attenuation coefficient in the energy range 14-36 keV of 3D printing materials for physical breast phantoms.

Objective.To measure the monoenergetic x-ray linear attenuation coefficient,µ, of fused deposition modeling (FDM) colored 3D printing materials (ABS, PLAwhite, PLAorange, PET and NYLON), used as adipose, glandular or skin tissue substitutes for manufacturing physical breast phantoms.Approach. Attenuation data (at 14, 18, 20, 24, 28, 30 and 36 keV) were acquired at Elettra synchrotron radiation facility, with step-wedge objects, using the Lambert-Beer law and a CCD imaging detector. Test objects were 3D printed using the Ultimaker 3 FDM printer. PMMA, Nylon-6 and high-density polyethylene step objects were also investigated for the validation of the proposed methodology. Printing uniformity was assessed via monoenergetic and polyenergetic imaging (32 kV, W/Rh).Main results. Maximum absolute deviation ofµfor PMMA, Nylon-6 and HD-PE was 5.0%, with reference to literature data. For ABS and NYLON,µdiffered by less than 6.1% and 7.1% from that of adipose tissue, respectively; for PET and PLAorangethe difference was less than 11.3% and 6.3% from glandular tissue, respectively. PLAorangeis a good substitute of skin (differences from -9.4% to +1.2%). Hence, ABS and NYLON filaments are suitable adipose tissue substitutes, while PET and PLAorangemimick the glandular tissue. PLAwhitecould be printed at less than 100% infill density for matching the attenuation of glandular tissue, using the measured density calibration curve. The printing mesh was observed for sample thicknesses less than 60 mm, imaged in the direction normal to the printing layers. Printing dimensional repeatability and reproducibility was less 1%.Significance. For the first time an experimental determination was provided of the linear attenuation coefficient of common 3D printing filament materials with estimates ofµat all energies in the range 14-36 keV, for their use in mammography, breast tomosynthesis and breast computed tomography investigations.

Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra.

PURPOSE: To design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging. MATERIALS: We realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile-butadiene-styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the ß parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner. RESULTS: DM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. ß parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower ß was calculated from the CT scan. CONCLUSIONS: FDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

Dataset of patient-derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography.

PURPOSE: To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. ACQUISITION AND VALIDATION METHODS: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. DATA FORMAT AND ACCESS: The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). POTENTIAL APPLICATIONS: The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.