Christian Fedon

Towards breast tomography with synchrotron radiation at Elettra: First images

The aim of the SYRMA-CT collaboration is to set-up the first clinical trial of phase-contrast breast CT with synchrotron radiation (SR). In order to combine high image quality and low delivered dose a number of innovative elements are merged: a CdTe single photon counting detector, state-of-the-art CT reconstruction and phase retrieval algorithms. To facilitate an accurate exam optimization, a Monte Carlo model was developed for dose calculation using GEANT4. In this study, high isotropic spatial resolution (120 μm)(3) CT scans of objects with dimensions and attenuation similar to a human breast were acquired, delivering mean glandular doses in the range of those delivered in clinical breast CT (5-25 mGy). Due to the spatial coherence of the SR beam and the long distance between sample and detector, the images contain, not only absorption, but also phase information from the samples. The application of a phase-retrieval procedure increases the contrast-to-noise ratio of the tomographic images, while the contrast remains almost constant. After applying the simultaneous algebraic reconstruction technique to low-dose phase-retrieved data sets (about 5 mGy) with a reduced number of projections, the spatial resolution was found to be equal to filtered back projection utilizing a four fold higher dose, while the contrast-to-noise ratio was reduced by 30%. These first results indicate the feasibility of clinical breast CT with SR.

Imaging performance of phase-contrast breast computed tomography with synchrotron radiation and a CdTe photon-counting detector

PURPOSE Within the SYRMA-CT collaboration based at the ELETTRA synchrotron radiation (SR) facility the authors investigated the imaging performance of the phase-contrast computed tomography (CT) system dedicated to monochromatic in vivo 3D imaging of the female breast, for breast cancer diagnosis. METHODS Test objects were imaged at 38keV using monochromatic SR and a high-resolution CdTe photon-counting detector. Signal and noise performance were evaluated using modulation transfer function (MTF) and noise power spectrum. The analysis was performed on the images obtained with the application of a phase retrieval algorithm as well as on those obtained without phase retrieval. The contrast to noise ratio (CNR) and the capability of detecting test microcalcification clusters and soft masses were investigated. RESULTS For a voxel size of (60μm)(3), images without phase retrieval showed higher spatial resolution (6.7mm(-1) at 10% MTF) than corresponding images with phase retrieval (2.5mm(-1)). Phase retrieval produced a reduction of the noise level and an increase of the CNR by more than one order of magnitude, compared to raw phase-contrast images. Microcalcifications with a diameter down to 130μm could be detected in both types of images. CONCLUSIONS The investigation on test objects indicates that breast CT with a monochromatic SR source is technically feasible in terms of spatial resolution, image noise and contrast, for in vivo 3D imaging with a dose comparable to that of two-view mammography. Images obtained with the phase retrieval algorithm showed the best performance in the trade-off between spatial resolution and image noise.

Glandular dose in breast computed tomography with synchrotron radiation

The purpose of this work is to provide an evaluation of the mean glandular dose (MGD) for breast computed tomography (CT) with synchrotron radiation in an axial scanning configuration with a partial or total organ volume irradiation, for the in vivo program of breast CT ongoing at the ELETTRA facility (Trieste, Italy). A Geant4 Monte Carlo code was implemented, simulating the photon irradiation from a synchrotron radiation source in the energetic range from 8 to 50 keV with 1 keV intervals, to evaluate the MGD. The code was validated with literature data, in terms of mammographic normalized glandular dose coefficients (DgN) and with ad hoc experimental data, in terms of computed tomography dose index (CTDI). Simulated cylindrical phantoms of different sizes (diameter at phantom base 8, 10, 12, 14 or 16 cm, axial length 1.5 times the radius) and glandular fraction by weight (0%, 14.3%, 25%, 50%, 75% and 100%) were implemented into the code. The validation of the code shows an excellent agreement both with previously published work and in terms of DgN and CDTI measurements. The implemented simulations show a dependence of the glandular dose estimate on the vertical dimension of the irradiated zone when a partial organ irradiation was implemented. Specific normalized coefficients for calculating the MGD to the whole breast or to the single irradiated slice were reported.

GEANT4 for breast dosimetry: parameters optimization study.

Mean glandular dose (MGD) is the main dosimetric quantity in mammography. MGD evaluation is obtained by multiplying the entrance skin air kerma (ESAK) by normalized glandular dose (DgN) coefficients. While ESAK is an empirical quantity, DgN coefficients can only be estimated with Monte Carlo (MC) methods. Thus, a MC parameters benchmark is needed for effectively evaluating DgN coefficients. GEANT4 is a MC toolkit suitable for medical purposes that offers to the users several computational choices. In this work we investigate the GEANT4 performances testing the main PhysicsLists for medical applications. Four electromagnetic PhysicsLists were implemented: the linear attenuation coefficients were calculated for breast glandularity 0%, 50%, 100% in the energetic range 8-50 keV and DgN coefficients were evaluated. The results were compared with published data. Fit equations for the estimation of the G-factor parameter, introduced by the literature for converting the dose delivered in the heterogeneous medium to that in the glandular tissue, are proposed and the application of this parameter interaction-by-interaction or retrospectively is discussed. G4EmLivermorePhysicsList shows the best agreement for the linear attenuation coefficients both with theoretical values and published data. Moreover, excellent correlation factor (r2>0.99) is found for the DgN coefficients with the literature. The final goal of this study is to identify, for the first time, a benchmark of parameters that could be useful for future breast dosimetry studies with GEANT4.

Imaging study of a phase-sensitive breast-CT system in continuous acquisition mode

The SYRMA-CT project aims to set-up the first clinical trial of phase-contrast breast Computed Tomography with synchrotron radiation at the SYRMEP beamline of Elettra, the Italian synchrotron light source. The challenge in a dedicated breast CT is to match a high spatial resolution with a low dose level. In order to fulfil these requirements, the SYRMA-CT project uses a large area CdTe single photon counting detector (Pixirad-8), simultaneous algebraic reconstruction technique (SART) and phase retrieval pre-processing. This work investigates the imaging performances of the system in a continuous acquisition mode and with a low dose level towards the clinical application. A custom test object and a large surgical sample have been studied.

Use of XR-QA2 radiochromic films for quantitative imaging of a synchrotron radiation beam

In the framework of an ongoing project, promoted by INFN, at the SYRMEP beamline of the ELETTRA synchrotron radiation facility (Trieste, Italy) for phase-contrast breast X-ray computed tomography, the assessment of the dose to the breast is one of the issues, requiring the determination of the distribution of X-ray incident photon fluence. This work investigates the use of XR-QA2 radiochromic films for quantitative imaging of the synchrotron radiation (SR) beam. XR-QA2 films were irradiated in a plane transverse to the beam axis, with a monochromatic beam of energy of 28, 35, 38 or 40 keV. The response of the radiochromic film was calibrated in terms of average air kerma measured with an ionization chamber. The net reflectance of the exposed film was then converted to photon fluence per unit air kerma (mm−2mGy−1). The SR beam profile was acquired also with a scintillator (GOS) based, fiber optic coupled CCD camera as well as with a scintillator based flat panel detector. Horizontal and vertical line profiles acquired with the radiochromic films show the 2D distribution of the beam intensity, with variations in the order of 15–20% in the horizontal direction. The response of the radiochromic film is comparable to that of the other imaging detectors, within less than 5% variation.

Energy response of GR-200A thermoluminescence dosemeters to 60Co and to monoenergetic synchrotron radiation in the energy range 28–40 keV

The response of LiF:Mg,Cu,P thermoluminescence dosemeters (type GR-200A) to monoenergetic radiation of energy 28, 35, 38 and 40 keV was evaluated with respect to irradiation with a calibrated (60)Co gamma-ray source. High-precision measurements of the relative air kerma response performed at the SYRMEP beamline of the ELETTRA synchrotron radiation facility (Trieste, Italy) showed a significant deviation of the average response to low-energy X-rays from that to (60)Co, with an over-response from 6 % (at 28 keV) to 22 % (at 40 keV). These data are not consistent with literature data for these dosemeters, where model predictions gave deviation from unity of the relative air kerma response of about 10 %. The authors conclude for the need of additional determinations of the low-energy relative response of GR-200A dosemeters, covering a wider range of monoenergetic energies sampled at a fine energy step, as planned in future experiments by their group at the ELETTRA facility.

A Framework for Iterative Reconstruction in Phase-Contrast Computed Tomography Dedicated to the Breast

We present the implementation of the CT iterative reconstruction strategy developed within the SYRMA-CT project for in vivo phase contrast CT of the uncompressed breast, ongoing at the ELETTRA synchrotron radiation facility (Trieste, Italy). Propagation-based phase-contrast imaging exploited the high spatial coherence of the monoenergetic laminar X-ray beam (3-mm high along the chest-wall-to-nipple direction), as well as the large object-to-detector distance (∼2 m) and the use of a prototype of Pixirad-8 high-resolution photon counting CdTe detector (60-μm pitch, eight detector units arranged in a row). The signal in projection views depends on the X-ray absorption as well as on the phase shift introduced by the breast tissue in the beam path. A phase retrieval algorithm allows recovering the projected 2D phase map of the irradiated tissue layer, which were input to the CT reconstruction; then, the 3D image of the breast was reconstructed via a simultaneous algebraic reconstruction technique (SART) algorithm. The developed iterative reconstruction — coupled with a filtering process for reducing the noise level and ring artifacts by preserving edges sharpness — showed better image quality than conventional filtered backprojection (FBP) reconstruction. A phantom study showed that the iterative reconstruction produced images with a contrast-to-noise-ratio up to 65% and a spatial resolution up to 12% higher than those obtained with FBP. Finally, the developed algorithm removed ring-like artifacts caused by the detector dead space (0.16 mm) across adjacent detector units and by no perfect equalization after flat-field correction, without worsening the image quality.

Development of 3D patient-based super-resolution digital breast phantoms using machine learning

Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is proposed. Given a tomographic breast image of finite resolution, the proposed methods can generate a phantom and increase its resolution at will, not only simply through super-sampling, but also by generating additional random glandular details to account for glandular edges and strands to compensate for those that may have not been detected in the original image due to the limited spatial resolution of the imaging system used. The proposed algorithms use supervised learning to predict the loss in glandularity due to limited resolution, and then to realistically recover this loss by learning the mapping between low and high resolution images. They were trained on high-resolution synchrotron images (detector pixel size 60 μm) reconstructed at seven voxel dimensions (60 μm-480 μm), and applied to patient images acquired with a clinical breast CT system (detector pixel size 194 μm) to generate super-resolution phantoms (voxel sizes 68 μm). Several evaluations were made to assess the appropriateness of the developed methods, both with the synchrotron (relative prediction error 0.010  ±  0.004, recovering accuracy 0.95  ±  0.04), and with the clinical images (average glandularity error at 194 μm: 0.15%  ±  0.12%). Finally, a breast radiologist assessed the realism of the developed phantoms by blindly comparing original and phantom images, resulting in not being able to distinguish the real from the phantom images. In conclusion, the proposed method can generate super-resolution phantoms from tomographic breast patient images that can be used for future computer simulations for optimization of new breast imaging technologies.

Automatic estimation of glandular tissue loss due to limited reconstruction voxel size in tomographic images of the breast.

An accurate measurement of the breast glandular fraction, or glandularity, is important for many research and clinical applications, such as breast cancer risk assessment. We propose a method to estimate the loss of glandular tissue detail due to the limited voxel size in tomographic images of the breast. CT images of a breast tissue specimen were acquired using a CdTe single photon counting detector (nominal pixel size of 60 μm) and using a monochromatic synchrotron radiation x-ray beam. Images were reconstructed using a filtered backprojection algorithm at seven different voxel sizes (range 60-420 μm, with a 60 μm step) and twelve groups of Regions of Interest (ROIs) with different percentage and patterns of glandular tissue were extracted. All ROIs within each group contained the same portion of the image (and therefore the same glandular fraction) reconstructed at a different voxel size. The glandular tissue was segmented and the glandularity calculated for all ROIs. A machine learning algorithm was trained on the glandularity values as a function of reconstruction voxel size. After the training was completed, the algorithm could estimate, given a tomographic breast image reconstructed at a given voxel size with a certain glandularity, the increase (or decrease) of glandularity if the same image were reconstructed with a smaller (or larger) voxel dimension. The algorithm was tested on six additional groups of ROIs, resulting in an average relative standard error between the calculated and estimated glandularity of 0.02 ± 0.016.