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- Bakic, Predrag R., et al.
(författare)
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Artifact reduction in simultaneous tomosynthesis and mechanical imaging of the breast
- 2019
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Ingår i: Medical Imaging 2019 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 9781510625433 ; 10948
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Konferensbidrag (refereegranskat)abstract
- Mechanical imaging (MI) uses a pressure sensor array to estimate the stiffness of lesions. Recent clinical studies have suggested that MI combined with digital mammography may reduce false positive findings and negative biopsies by over 30%. Digital breast tomosynthesis (DBT) has been adopted progressively in cancer screening. The tomographic nature of DBT improves lesion visibility by reducing tissue overlap in reconstructed images. For maximum benefit, DBT and MI data should be acquired simultaneously; however, that arrangement produces visible artifacts in DBT images due to the presence of the MI sensor array. We propose a method for reducing artifacts during the DBT image reconstruction. We modified the parameters of a commercial DBT reconstruction engine and investigated the conspicuity of artifacts in the resultant images produced with different sensor orientations. The method was evaluated using a physical anthropomorphic phantom imaged on top of the sensor. Visual assessment showed a reduction of artifacts. In a quantitative test, we calculated the artifact spread function (ASF), and compared the ratio of the mean ASF values between the proposed and conventional reconstruction (termed ASF ratio, RASF). We obtained a mean RASF of 2.74, averaged between two analyzed sensor orientations (45° and 90°). The performance varied with the orientation and the type of sensor structures causing the artifacts. RASF for wide connection lines was larger at 45° than at 90° (5.15 vs. 1.00, respectively), while for metallic contacts RASF was larger at 90° than at 45° (3.31 vs. 2.21, respectively). Future work will include a detailed quantitative assessment, and further method optimization in virtual clinical trials.
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| 2. |
- Schmidt, Taly Gilat, et al.
(författare)
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Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype
- 2023
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Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 50:10, s. 6008-6021
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Tidskriftsartikel (refereegranskat)abstract
- Background: Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data. Purpose: This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects. Methods: Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraint settings. Results: cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin, when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values. Conclusions: The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-step approach.
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