Phantom-based study exploring the effects of different scatter correction approaches on the reconstructed images generated by contrast-enhanced stationary digital breast tomosynthesis.
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ABSTRACT:
SUBMITTER: Puett C
PROVIDER: S-EPMC5793963 | biostudies-other | 2018 Jan
REPOSITORIES: biostudies-other
Publications
Phantom-based study exploring the effects of different scatter correction approaches on the reconstructed images generated by contrast-enhanced stationary digital breast tomosynthesis.
Puett Connor C Inscoe Christina C Lee Yueh Z YZ Zhou Otto O Lu Jianping J
Journal of medical imaging (Bellingham, Wash.) 20180101 1
Stationary digital breast tomosynthesis (sDBT) is an emerging technology in which the single rotating x-ray tube is replaced by a fixed array of multiple carbon nanotube-enabled sources, providing a higher spatial and temporal resolution. As such, sDBT offers a promising platform for contrast-enhanced (CE) imaging. However, given the minimal enhancement above background with standard operational tube settings and iodine dosing, CE breast imaging requires additional acquisition steps to isolate t ...[more]
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Project description:RATIONALE AND OBJECTIVES:A linear array of carbon nanotube-enabled x-ray sources allows for stationary digital breast tomosynthesis (sDBT), during which projection views are collected without the need to move the x-ray tube. This work presents our initial clinical experience with a first-generation sDBT device. MATERIALS AND METHODS:Following informed consent, women with a "suspicious abnormality" (Breast Imaging Reporting and Data System 4), discovered by digital mammography and awaiting biopsy, were also imaged by the first generation sDBT. Four radiologists participated in this paired-image study, completing questionnaires while interpreting the mammograms and sDBT image stacks. Areas under the receiver operating characteristic curve were used to measure reader performance (likelihood of correctly identifying malignancy based on pathology as ground truth), while a multivariate analysis assessed preference, as readers compared one modality to the next when interpreting diagnostically important image features. RESULTS:Findings from 43 women were available for analysis, in whom 12 cases of malignancy were identified by pathology. The mean areas under the receiver operating characteristic curve was significantly higher (p < 0.05) for sDBT than mammography for all breast density categories and breast thicknesses. Additionally, readers preferred sDBT over mammography when evaluating mass margins and shape, architectural distortion, and asymmetry, but preferred mammography when characterizing microcalcifications. CONCLUSION:Readers preferred sDBT over mammography when interpreting soft-tissue breast features and were diagnostically more accurate using images generated by sDBT in a Breast Imaging Reporting and Data System 4 population. However, the findings also demonstrated the need to improve microcalcification conspicuity, which is guiding both technological and image-processing design changes in future sDBT devices.
| S-EPMC6635094 | biostudies-literature
Project description:Digital breast tomosynthesis (DBT) offers poor image quality along the depth direction. This paper presents a new method that improves the image quality of DBT considerably through the a priori information from automated ultrasound (AUS) images.DBT and AUS images of a complex breast-mimicking phantom are acquired by a DBT/AUS dual-modality system. The AUS images are taken in the same geometry as the DBT images and the gradient information of the in-slice AUS images is adopted into the new loss functional during the DBT reconstruction process. The additional data allow for new iterative equations through solving the optimization problem utilizing the gradient descent method. Both visual comparison and quantitative analysis are employed to evaluate the improvement on DBT images. Normalized line profiles of lesions are obtained to compare the edges of the DBT and AUS-corrected DBT images. Additionally, image quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) are calculated to quantify the effectiveness of the proposed method.In traditional DBT image reconstructions, serious artifacts can be found along the depth direction (Z direction), resulting in the blurring of lesion edges in the off-focus planes parallel to the detector. However, by applying the proposed method, the quality of the reconstructed DBT images is greatly improved. Visually, the AUS-corrected DBT images have much clearer borders in both in-focus and off-focus planes, fewer Z direction artifacts and reduced overlapping effect compared to the conventional DBT images. Quantitatively, the corrected DBT images have better ASF, indicating a great reduction in Z direction artifacts as well as better Z resolution. The sharper line profiles along the Y direction show enhancement on the edges. Besides, noise is also reduced, evidenced by the obviously improved SDNR values.The proposed method provides great improvement on the quality of DBT images. This improvement makes it easier to locate and to distinguish a lesion, which may help improve the accuracy of the diagnosis using DBT imaging.
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Project description:OBJECTIVE:The imaging evaluation of cystic fibrosis currently relies on chest radiography or computed tomography. Recently, digital chest tomosynthesis has been proposed as an alternative. We have developed a stationary digital chest tomosynthesis (s-DCT) system based on a carbon nanotube (CNT) linear x-ray source array. This system enables tomographic imaging without movement of the x-ray tube and allows for physiological gating. The goal of this study was to evaluate the feasibility of clinical CF imaging with the s-DCT system. MATERIALS AND METHODS:CF patients undergoing clinically indicated chest radiography were recruited for the study and imaged on the s-DCT system. Three board-certified radiologists reviewed both the CXR and s-DCT images for image quality relevant to CF. CF disease severity was assessed by Brasfield score on CXR and chest tomosynthesis score on s-DCT. Disease severity measures were also evaluated against subject pulmonary function tests. RESULTS:Fourteen patients underwent s-DCT imaging within 72 h of their chest radiograph imaging. Readers scored the visualization of proximal bronchi, small airways and vascular pattern higher on s-DCT than CXR. Correlation between the averaged Brasfield score and averaged tomosynthesis disease severity score for CF was -0.73, p = 0.0033. The CF disease severity score system for tomosynthesis had high correlation with FEV1 (r = -0.685) and FEF 25-75% (r = -0.719) as well as good correlation with FVC (r = -0.582). CONCLUSION:We demonstrate the potential of CNT x-ray-based s-DCT for use in the evaluation of cystic fibrosis disease status in the first clinical study of s-DCT. KEY POINTS:• Carbon nanotube-based linear array x-ray tomosynthesis systems have the potential to provide diagnostically relevant information for patients with cystic fibrosis without the need for a moving gantry. • Despite the short angular span in this prototype system, lung features such as the proximal bronchi, small airways and pulmonary vasculature have improved visualization on s-DCT compared with CXR. Further improvements are anticipated with longer linear x-ray array tubes. • Evaluation of disease severity in CF patients is possible with s-DCT, yielding improved visualization of important lung features and high correlation with pulmonary function tests at a relatively low dose.
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Project description:In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited number of angles. Consequently, the reconstructed images contain severe blurring artifacts that might heavily degrade the DBT image quality and cause difficulties in detecting lesions. In this study, we propose a two-phase learning approach for artifact compensation in a coarse-to-fine manner to mitigate blurring artifacts effectively along all viewing directions of the DBT image volume (i.e., along the axial, coronal, and sagittal planes) to improve the detection performance of lesions. The proposed method employs a convolutional neural network model comprising two submodels/phases, with Phase 1 performing three-dimensional (3D) deblurring and Phase 2 performing additional 2D deblurring. To investigate the effects of loss functions on the proposed model's deblurring performance, we evaluated several loss functions, such as the pixel-based loss function, adversarial-based loss function, and perception-based loss function. Compared with the DBT image, the mean squared error of the image and the root mean squared errors of the gradient of the image decreased by 82.8% and 44.9%, respectively, and the contrast-to-noise ratio increased by 183.4% in the in-focus plane. We verified that the proposed method sequentially restored the missing frequency components as the DBT images were processed through the Phase 1 and Phase 2 steps. These results indicate that the proposed method performs effective 3D deblurring, significantly reducing the blurring artifacts in the in-focus plane and other planes of the DBT image, thus improving the detection performance of lesions.
| S-EPMC8786177 | biostudies-literature
Project description:Studies have shown that digital breast tomosynthesis (DBT) can improve breast cancer diagnosis by reconstructing 3D images. However, DBT scanners based on rotation gantry prolong the imaging time and reduce spatial resolution due to motion comparing with the regular two-view mammography. To obtain three dimension reconstruction images and maintain the high image quality of conventional mammography, we proposed a prototype stationary digital breast tomosynthesis system (s-DBT). The proposed s-DBT system acquires projection images without mechanical movement. The core component of the s-DBT system is a specially designed spatially distributed multi-beam x-ray tube based on the carbon nanotube field emission x-ray technology. The multi-beam x-ray source array enables collection of all projection images from different viewing angles without mechanical motion. Preliminary results show the s-DBT system can achieve a scan time comparable to the regular two-view mammography, and improve the spatial resolution comparing with rotating gantry DBT.
| S-EPMC3146055 | biostudies-other
Project description:Digital breast tomosynthesis (DBT) is a quasi-three-dimensional imaging modality that can reduce false negatives and false positives in mass lesion detection caused by overlapping breast tissue in conventional two-dimensional (2D) mammography. The patient dose of a DBT scan is similar to that of a single 2D mammogram, while acquisition of each projection view adds detector readout noise. The noise is propagated to the reconstructed DBT volume, possibly obscuring subtle signs of breast cancer such as microcalcifications (MCs). This study developed a deep convolutional neural network (DCNN) framework for denoising DBT images with a focus on improving the conspicuity of MCs as well as preserving the ill-defined margins of spiculated masses and normal tissue textures. We trained the DCNN using a weighted combination of mean squared error (MSE) loss and adversarial loss. We configured a dedicated x-ray imaging simulator in combination with digital breast phantoms to generate realistic in silico DBT data for training. We compared the DCNN training between using digital phantoms and using real physical phantoms. The proposed denoising method improved the contrast-to-noise ratio (CNR) and detectability index (d') of the simulated MCs in the validation phantom DBTs. These performance measures improved with increasing training target dose and training sample size. Promising denoising results were observed on the transferability of the digital-phantom-trained denoiser to DBT reconstructed with different techniques and on a small independent test set of human subject DBT images.
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Project description:Develop a computer-aided detection (CADe) system for clustered microcalcifications in digital breast tomosynthesis (DBT) volume enhanced with multiscale bilateral filtering (MSBF) regularization.With Institutional Review Board approval and written informed consent, two-view DBT of 154 breasts, of which 116 had biopsy-proven microcalcification (MC) clusters and 38 were free of MCs, was imaged with a General Electric GEN2 prototype DBT system. The DBT volumes were reconstructed with MSBF-regularized simultaneous algebraic reconstruction technique (SART) that was designed to enhance MCs and reduce background noise while preserving the quality of other tissue structures. The contrast-to-noise ratio (CNR) of MCs was further improved with enhancement-modulated calcification response (EMCR) preprocessing, which combined multiscale Hessian response to enhance MCs by shape and bandpass filtering to remove the low-frequency structured background. MC candidates were then located in the EMCR volume using iterative thresholding and segmented by adaptive region growing. Two sets of potential MC objects, cluster centroid objects and MC seed objects, were generated and the CNR of each object was calculated. The number of candidates in each set was controlled based on the breast volume. Dynamic clustering around the centroid objects grouped the MC candidates to form clusters. Adaptive criteria were designed to reduce false positive (FP) clusters based on the size, CNR values and the number of MCs in the cluster, cluster shape, and cluster based maximum intensity projection. Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses were used to assess the performance and compare with that of a previous study.Unpaired two-tailed t-test showed a significant increase (p < 0.0001) in the ratio of CNRs for MCs with and without MSBF regularization compared to similar ratios for FPs. For view-based detection, a sensitivity of 85% was achieved at an FP rate of 2.16 per DBT volume. For case-based detection, a sensitivity of 85% was achieved at an FP rate of 0.85 per DBT volume. JAFROC analysis showed a significant improvement in the performance of the current CADe system compared to that of our previous system (p = 0.003).MBSF regularized SART reconstruction enhances MCs. The enhancement in the signals, in combination with properly designed adaptive threshold criteria, effective MC feature analysis, and false positive reduction techniques, leads to a significant improvement in the detection of clustered MCs in DBT.
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Project description:In principle, a stationary digital breast tomosynthesis (s-DBT) system has better image quality when compared to continuous motion DBT systems due to zero motion blur of the source. The authors have developed a s-DBT system by using a linear carbon nanotube x-ray source array. The purpose of the current study was to quantitatively evaluate the performance of the s-DBT system; and investigate the dependence of imaging quality on the system configuration parameters.Physical phantoms were used to assess the image quality of each configuration including inplane resolution as measured by the modulation transfer function (MTF), inplane contrast as measured by the signal difference to noise ratio (SdNR), and depth resolution as measured by the z-axis artifact spread function. Five parameters were varied to create five groups of configurations: (1) total angular span; (2) total number of projection images; (3) distribution of exposure (mAs) across the projection images; (4) entrance dose; (5) detector pixel size.It was found that the z-axis depth resolution increased with the total angular span but was insensitive to the number of projection images, mAs distribution, entrance dose, and detector pixel size. The SdNR was not affected by the angular span or the number of projection images. A decrease in SdNR was observed when the mAs was not evenly distributed across the projection images. As expected, the SdNR increased with entrance dose and when larger pixel sizes were used. For a given detector pixel size, the inplane resolution was found to be insensitive to the total angular span, number of projection images, mAs distribution, and entrance dose. A 25% increase in the MTF was observed when the detector was operating in full resolution mode (70 μm pixel size) compared to 2 × 2 binned mode (140 μm pixel size).The results suggest that the optimal imaging configuration for a s-DBT system is a large angular span, an intermittent number of projection views, and a uniform mAs distribution over all views. With the detector operating at full resolution, a stationary DBT system can achieve an inplane resolution of 5.1 cycles per mm, which is significantly better than continuous motion DBT systems.
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Project description:The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented a DnCNN-MARHR algorithm based on a training network (mini-batch stochastic gradient descent algorithm with momentum) to estimate the residual reference (140 keV virtual monochromatic [VM]) and object (70 kV with metal artifacts) images. For this, we used projection data and subtracted the estimated residual images from the object images, involving hybrid and subjectively reconstructed image usage (back projection and maximum likelihood expectation maximization [MLEM]). The DnCNN-MARHR algorithm was compared with the dual-energy material decomposition reconstruction algorithm (DEMDRA), VM, MLEM, established and commonly used filtered back projection (FBP), and a simultaneous algebraic reconstruction technique-total variation (SART-TV) with MAR processing. MAR was compared using artifact index (AI) and texture analysis. Artifact spread functions (ASFs) for images that were out-of-plane and in-focus were evaluated using a prosthesis phantom. The overall performance of the DnCNN-MARHR algorithm was adequate with regard to the ASF, and the derived images showed better results, without being influenced by the metal type (AI was almost equal to the best value for the DEMDRA). In the ASF analysis, the DnCNN-MARHR algorithm generated better MAR compared with that obtained employing usual algorithms for reconstruction using MAR processing. In addition, comparison of the difference (mean square error) between DnCNN-MARHR and the conventional algorithm resulted in the smallest VM. The DnCNN-MARHR algorithm showed the best performance with regard to image homogeneity in the texture analysis. The proposed algorithm is particularly useful for reducing artifacts in the longitudinal direction, and it is not affected by tissue misclassification.
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