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Title: Sensitivity analysis of a geometric calibration method using projection matrices for digital tomosynthesis systems

Abstract

Purpose: To study the sensitivity of a geometric calibration method using projection matrices for digital tomosynthesis systems. Methods: A generic geometric calibration method for tomographic imaging systems has been presented in our previous work. The method involves a scan of a calibration phantom with multiple markers. Their locations in projection images are detected and are associated with their 3D coordinates to compute 3x4 projection matrices, which can be used in subsequent image reconstruction. The accuracy of geometric calibration may be affected by errors in the input data of marker positions. The effects of errors may depend on the number of markers and the volume surrounded by them in 3D space. This work analyzed the sensitivity of the calibration method to the above factors. A 6 cm CIRS breast research phantom and a prototype breast tomosynthesis system were used for our tests. A high contrast ring and two small speck groups were reconstructed in various testing cases for comparison. To achieve quantitative assessment, a 15x15 point detection mask was adopted for detecting signals and for computing changes between testing cases and the regular geometric calibration. Results: When 3D coordinates and 2D projections of markers were accurate, all tested numbers of markers,more » 6-44, provided similar high quality reconstructions of the ring and the two speck groups. Errors in marker positions resulted in image degradations and signal changes, which increased with fewer markers and smaller volume surrounded by markers in the 3D object space. Signal changes of small specks were more significant than those of the ring. Errors in marker projections produced drastic image degradations. Coplanar marker placement caused a failure in projection matrix computation. Conclusions: For practical geometric calibration phantom design, ample markers are desired. They need to have a large volumetric coverage in the 3D space and be far from being coplanar. Precise determination of marker projections on detector planes is crucial for accurate geometric calibration and for small object detection using the reconstructed images.« less

Authors:
; ;  [1]
  1. Department of Radiology, Division of Diagnostic Imaging Physics, Massachusetts General Hospital, Boston, Massachusetts 02114 (United States)
Publication Date:
OSTI Identifier:
22096857
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 38; Journal Issue: 1; Other Information: (c) 2011 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 60 APPLIED LIFE SCIENCES; ACCURACY; ALGEBRA; BIOMEDICAL RADIOGRAPHY; CALCULATION METHODS; CALIBRATION; COMPARATIVE EVALUATIONS; COMPUTERIZED TOMOGRAPHY; FAILURES; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; PHANTOMS; RESPIRATORS; SENSITIVITY; SENSITIVITY ANALYSIS; TESTING

Citation Formats

Li Xinhua, Zhang Da, and Liu, Bob. Sensitivity analysis of a geometric calibration method using projection matrices for digital tomosynthesis systems. United States: N. p., 2011. Web. doi:10.1118/1.3524221.
Li Xinhua, Zhang Da, & Liu, Bob. Sensitivity analysis of a geometric calibration method using projection matrices for digital tomosynthesis systems. United States. doi:10.1118/1.3524221.
Li Xinhua, Zhang Da, and Liu, Bob. 2011. "Sensitivity analysis of a geometric calibration method using projection matrices for digital tomosynthesis systems". United States. doi:10.1118/1.3524221.
@article{osti_22096857,
title = {Sensitivity analysis of a geometric calibration method using projection matrices for digital tomosynthesis systems},
author = {Li Xinhua and Zhang Da and Liu, Bob},
abstractNote = {Purpose: To study the sensitivity of a geometric calibration method using projection matrices for digital tomosynthesis systems. Methods: A generic geometric calibration method for tomographic imaging systems has been presented in our previous work. The method involves a scan of a calibration phantom with multiple markers. Their locations in projection images are detected and are associated with their 3D coordinates to compute 3x4 projection matrices, which can be used in subsequent image reconstruction. The accuracy of geometric calibration may be affected by errors in the input data of marker positions. The effects of errors may depend on the number of markers and the volume surrounded by them in 3D space. This work analyzed the sensitivity of the calibration method to the above factors. A 6 cm CIRS breast research phantom and a prototype breast tomosynthesis system were used for our tests. A high contrast ring and two small speck groups were reconstructed in various testing cases for comparison. To achieve quantitative assessment, a 15x15 point detection mask was adopted for detecting signals and for computing changes between testing cases and the regular geometric calibration. Results: When 3D coordinates and 2D projections of markers were accurate, all tested numbers of markers, 6-44, provided similar high quality reconstructions of the ring and the two speck groups. Errors in marker positions resulted in image degradations and signal changes, which increased with fewer markers and smaller volume surrounded by markers in the 3D object space. Signal changes of small specks were more significant than those of the ring. Errors in marker projections produced drastic image degradations. Coplanar marker placement caused a failure in projection matrix computation. Conclusions: For practical geometric calibration phantom design, ample markers are desired. They need to have a large volumetric coverage in the 3D space and be far from being coplanar. Precise determination of marker projections on detector planes is crucial for accurate geometric calibration and for small object detection using the reconstructed images.},
doi = {10.1118/1.3524221},
journal = {Medical Physics},
number = 1,
volume = 38,
place = {United States},
year = 2011,
month = 1
}
  • Digital breast tomosynthesis (DBT) is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters (MCCs) for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 atmore » 1.3 false positive clusters, which is below that of current MCC detection algorithms for full-field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal-to-noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.« less
  • Purpose: To investigate the feasibility of a new two-dimensional (2D) multichannel response (MCR) analysis approach for the detection of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT). Methods: With IRB approval and informed consent, a data set of two-view DBTs from 42 breasts containing biopsy-proven MC clusters was collected in this study. The authors developed a 2D approach for MC detection using projection view (PV) images rather than the reconstructed three-dimensional (3D) DBT volume. Signal-to-noise ratio (SNR) enhancement processing was first applied to each PV to enhance the potential MCs. The locations of MC candidates were then identified with iterativemore » thresholding. The individual MCs were decomposed with Hermite–Gaussian (HG) and Laguerre–Gaussian (LG) basis functions and the channelized Hotelling model was trained to produce the MCRs for each MC on the 2D images. The MCRs from the PVs were fused in 3D by a coincidence counting method that backprojects the MC candidates on the PVs and traces the coincidence of their ray paths in 3D. The 3D MCR was used to differentiate the true MCs from false positives (FPs). Finally a dynamic clustering method was used to identify the potential MC clusters in the DBT volume based on the fact that true MCs of clinical significance appear in clusters. Using two-fold cross validation, the performance of the 3D MCR for classification of true and false MCs was estimated by the area under the receiver operating characteristic (ROC) curve and the overall performance of the MCR approach for detection of clustered MCs was assessed by free response receiver operating characteristic (FROC) analysis. Results: When the HG basis function was used for MCR analysis, the detection of MC cluster achieved case-based test sensitivities of 80% and 90% at the average FP rates of 0.65 and 1.55 FPs per DBT volume, respectively. With LG basis function, the average FP rates were 0.62 and 1.57 per DBT volume at the same sensitivity levels. The difference in the two sets of basis functions for detection of MCs did not show statistical significance. Conclusions: The authors' experimental results indicate that the MCR approach is promising for the detection of MCs on PV images. The HG or LG basis functions are both effective in characterizing the signal response of MCs using the channelized Hotelling model. The coincidence counting method for fusion of the 2D MCR in 3D is an important step for FP reduction. Further study is underway to improve the MCR approach for microcalcification detection in DBT.« less
  • We developed a novel digital tomosynthesis (DTS) reconstruction method using a deformation field map to optimally estimate volumetric information in DTS images. The deformation field map is solved by using prior information, a deformation model, and new projection data. Patients' previous cone-beam CT (CBCT) or planning CT data are used as the prior information, and the new patient volume to be reconstructed is considered as a deformation of the prior patient volume. The deformation field is solved by minimizing bending energy and maintaining new projection data fidelity using a nonlinear conjugate gradient method. The new patient DTS volume is thenmore » obtained by deforming the prior patient CBCT or CT volume according to the solution to the deformation field. This method is novel because it is the first method to combine deformable registration with limited angle image reconstruction. The method was tested in 2D cases using simulated projections of a Shepp-Logan phantom, liver, and head-and-neck patient data. The accuracy of the reconstruction was evaluated by comparing both organ volume and pixel value differences between DTS and CBCT images. In the Shepp-Logan phantom study, the reconstructed pixel signal-to-noise ratio (PSNR) for the 60 deg. DTS image reached 34.3 dB. In the liver patient study, the relative error of the liver volume reconstructed using 60 deg. projections was 3.4%. The reconstructed PSNR for the 60 deg. DTS image reached 23.5 dB. In the head-and-neck patient study, the new method using 60 deg. projections was able to reconstruct the 8.1 deg. rotation of the bony structure with 0.0 deg. error. The reconstructed PSNR for the 60 deg. DTS image reached 24.2 dB. In summary, the new reconstruction method can optimally estimate the volumetric information in DTS images using 60 deg. projections. Preliminary validation of the algorithm showed that it is both technically and clinically feasible for image guidance in radiation therapy.« less
  • Mammographic parenchymal texture has been shown to correlate with genetic markers of developing breast cancer. Digital breast tomosynthesis (DBT) is a novel x-ray imaging technique in which tomographic images of the breast are reconstructed from multiple source projections acquired at different angles of the x-ray tube. Compared to digital mammography (DM), DBT eliminates breast tissue overlap, offering superior parenchymal tissue visualization. We hypothesize that texture analysis in DBT could potentially provide a better assessment of parenchymal texture and ultimately result in more accurate assessment of breast cancer risk. As a first step towards validating this hypothesis, we investigated the associationmore » between DBT parenchymal texture and breast percent density (PD), a known breast cancer risk factor, and compared it to DM. Bilateral DBT and DM images from 71 women participating in a breast cancer screening trial were analyzed. Filtered-backprojection was used to reconstruct DBT tomographic planes in 1 mm increments with 0.22 mm in-plane resolution. Corresponding DM images were acquired at 0.1 mm pixel resolution. Retroareolar regions of interest (ROIs) equivalent to 2.5 cm{sup 3} were segmented from the DBT images and corresponding 2.5 cm{sup 2} ROIs were segmented from the DM images. Breast PD was mammographically estimated using the Cumulus scale. Overall, DBT texture features demonstrated a stronger correlation than DM to PD. The Pearson correlation coefficients for DBT were r = 0.40 (p<0.001) for contrast and r = -0.52 (p<0.001) for homogeneity; the corresponding DM correlations were r = 0.26 (p = 0.002) and r = -0.33 (p<0.001). Multiple linear regression of the texture features versus breast PD also demonstrated significantly stronger associations in DBT (R{sup 2} = 0.39) compared to DM (R{sup 2} = 0.33). We attribute these observations to the superior parenchymal tissue visualization in DBT. Our study is the first to perform DBT texture analysis in a screening population of women, showing that DBT could potentially provide better breast cancer risk assessment in the future.« less
  • Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection imagemore » separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.« less