Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image‐reconstruction algorithms

Sidky, Emil Y.; Pan, Xiaochuan; Reiser, Ingrid; Nishikawa, Robert M.; Moore, Richard H.; Kopans, Daniel B.

doi:10.1118/1.3232211

Cited by 168 publications

(126 citation statements)

References 17 publications

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“…A variety of techniques have been used to reconstruct the DBT volume from the acquired PVs. [1][2][3][4][5][6][7] Since the PVs in a DBT scan are acquired only within a limited range of acquisition angles of the compressed breast, the reconstructed 3D DBT volume contains artifacts due to missing information, regardless of the reconstruction technique applied. Despite reconstruction artifacts, DBT is considered to have a strong potential to improve breast imaging, by reducing the camouflaging effect of the overlapping fibroglandular breast tissue that is usually a limiting factor for lesion detection and characterization in mammography.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

et al. 2011

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Purpose: To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system. Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs. Results: The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters. Conclusions: Our study indicates the feasibility of the 3D approach to the detection of clustered microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.

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Section: Introductionmentioning

confidence: 99%

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

et al. 2011

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“…It is equivalent to the total variation (TV). 23,24,30,[34][35][36][37][38][39][40] In CT imaging, TV has favorable noise and artifacts mitigating properties but sometimes produce an image that lacks small and low-contrast details. 24 In contrast with many other applications of PICCS, [13][14][15][16][17][18][19][20][21][22][23][24] the primary goal of DR-PICCS is not to enable undersampled data acquisitions but rather to reduce image noise.…”

Section: Dose Reduction Using Piccs (Dr-piccs)mentioning

confidence: 99%

Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction

Thériault-Lauzier

Chen

2013

Med. Phys.

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Purpose: The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution. Methods: Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution. Results: In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model. Conclusions: DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.

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“…However, in principle, the MSBF method does not depend on specific iterative reconstruction techniques. The performance of the new regularization technique was compared with that of the nonconvex total p-variation regularization method (TpV) 29 and reconstruction with no regularization (NR). The performances of the three methods in terms of the CNR of MCs and the sharpness of subtle signals and spiculations in terms of the full width at half maximum (FWHM) were quantitatively compared.…”

Section: Introductionmentioning

confidence: 99%

“…The gradient-based regularization method such as the total variation method was shown to be an efficient method to preserve edges of relatively high-contrast signals in tomosynthesis reconstruction. 27,28 The compressive sensing method with gradient-based penalty term was used for MC enhancement 29 or radiation dose reduction 30 in DBT. The anisotropic diffusion type method was used to improve image quality of DBT reconstruction for MC detection in the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Chan

Wei

et al. 2015

Medical Physics

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Purpose: Detection of subtle microcalcifications in digital breast tomosynthesis (DBT) is a challenging task because of the large, noisy DBT volume. It is important to enhance the contrast-to-noise ratio (CNR) of microcalcifications in DBT reconstruction. Most regularization methods depend on local gradient and may treat the ill-defined margins or subtle spiculations of masses and subtle microcalcifications as noise because of their small gradient. The authors developed a new multiscale bilateral filtering (MSBF) regularization method for the simultaneous algebraic reconstruction technique (SART) to improve the CNR of microcalcifications without compromising the quality of masses. Methods: The MSBF exploits a multiscale structure of DBT images to suppress noise and selectively enhance high frequency structures. At the end of each SART iteration, every DBT slice is decomposed into several frequency bands via Laplacian pyramid decomposition. No regularization is applied to the low frequency bands so that subtle edges of masses and structured background are preserved. Bilateral filtering is applied to the high frequency bands to enhance microcalcifications while suppressing noise. The regularized DBT images are used for updating in the next SART iteration. The new MSBF method was compared with the nonconvex total p-variation (TpV) method for noise regularization with SART. A GE GEN2 prototype DBT system was used for acquisition of projections at 21 angles in 3• increments over a ±30• range. The reconstruction image quality with no regularization (NR) and that with the two regularization methods were compared using the DBT scans of a heterogeneous breast phantom and several human subjects with masses and microcalcifications. The CNR and the full width at half maximum (FWHM) of the line profiles of microcalcifications and across the spiculations within their in-focus DBT slices were used as image quality measures. Results: The MSBF method reduced contouring artifacts and enhanced the CNR of microcalcifications compared to the TpV method, thus preserving the image quality of the structured background. The MSBF method achieved the highest CNR of microcalcifications among the three methods. The FWHM of the microcalcifications and mass spiculations resulting from the MSBF method was comparable to that without regularization, and superior to that of the TpV method. Conclusions: The SART regularized by the multiscale bilateral filtering method enhanced the CNR of microcalcifications and preserved the sharpness of microcalcifications and spiculated masses. The MSBF method provided better image quality of the structured background and was superior to TpV and NR for enhancing microcalcifications while preserving the appearance of mass margins. C 2015 American Association of Physicists in Medicine. [http://dx

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Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image‐reconstruction algorithms

Cited by 168 publications

References 17 publications

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

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