Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (1) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (2) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Noise is an intrinsic property of every imaging system. For imaging systems using ionizing radiation, such as digital breast tomosynthesis (DBT) or digital mammography (DM), we strive to ensure that x-ray quantum noise is the limiting noise source in images, while using the lowest radiation dose possible to achieve clinically satisfactory images. Therefore, new computer methods are being sought to help reduce the dose of these systems. In the case of DBT, this can be achieved when solving the inverse problem of tomographic reconstruction. In this work, we propose to use a Non-Local Gaussian Markov Random Field (NLGMRF) model to represent a priori knowledge in a Bayesian (Maximum a Posteriori - MAP) reconstruction approach for DBT. The main advantage of the Non-Local Markov Random Field models is that they explicitly consider two important constraints to regularize the solution of this inverse problem - smoothing and redundancy. To evaluate this new method in DBT, a number of experiments were performed to compare these methods to existing reconstruction techniques. Comparable or superior results were achieved when compared with methods in the DBT reconstruction literature in terms of structural similarity index (SSIM), artifact spread function (ASF) and visual analysis, demonstrating that the NLGMRF model is suitable to regularize the MAP solution in DBT reconstruction.

Chapter 40 - GPU Acceleration of Iterative Digital Breast Tomosynthesis

Statistical image reconstruction (SIR) is a promising approach to reducing radiation dose in clinical computerized tomography (CT) scans. Clinical CT scanners use energy-integrating detectors. The CT signal follows a compound Poisson distribution, its probability density function (PDF) does not have an analytical form hence cannot be used in an SIR method. The goal of this work is to quantify the effects of using an approximate statistical assumption in SIR methods for clinical CT applications. We apply a pseudo-Ideal Observer (pIO) to simulated CT projection data of the fanbeam geometry at different dose levels. The simulation models the polychromatic X-ray tube spectrum, the effects of the bowtie filter, and the energy-integrating detectors. The pIO uses a pseudo likelihood function (pLF) to calculate the pseudo likelihood ratio, which is the decision variable used by the pIO in a binary detection task. The pLF is an approximation to the true LF of the underlying data. The pIO has inferior performance than the IO unless the pLF coincides with the LF; this performance difference quantifies the closeness between the pseudo likelihood and the exact one. Using lesion detectability in a signal known exactly, background known exactly binary detection task as a figure-of-merit, our results show that at down to 0.1% of a reference tube current level I0, the pIO that uses a Poisson approximation, or a matched variance Gaussian approximation in either the transmission or the line integral domain, achieves 99% the performance of the IO. The constant variance Gaussian approximation has only 70%-80% of the IO performance. At tube currents lower than 0.1% I0, the performance difference is more substantial. We conclude that at current clinical dose levels, it is important to account for the mean-dependent variance in CT projection data in SIR problem formulation, the exact PDF of the CT signal is not as important.

In digital breast tomosynthesis (DBT), the reconstructed image quality of small objects such as microcalcifications are limited by the detector resolution and the reconstruction voxel dimensions used, in addition to the inherent in-plane and inter-plane blurring due to limited-angle image acquisition. We are investigating the effects of subpixel reconstruction on the image quality of microcalcifications. In this study, subpixel projection images were generated by polynomial interpolation of the gray level values of the original projection images to reduce the pixel pitch by a factor of 2 to 4. The voxel dimensions in the reconstruction volume were varied. The in-plane voxel dimensions were chosen to match the pitch in the subpixel projection images while the voxel dimension in the depth-direction was varied systematically to study the relative effects. DBT scans of human subjects with microcalcifications were acquired with a GE prototype DBT system at 0.1 mm x 0.1 mm pixel pitch. In addition, computer modeling of the same system was used to generate simulated DBT projections of small spheres as a surrogate for microcalcifications. DBT volumes corresponding to the subpixel projections were reconstructed with SART. The FWHMs of the line profiles of the simulated microcalcifications on their in-focus DBT slices and FWHMs of the inter-plane artifact spread function (ASF) in the depth-direction were used for comparison of reconstruction quality. The results indicated that subpixel reconstruction increased the object contrast and reduced the inter-plane blurring to a certain extent. Further work is underway to study the dependence of the trade-off between image quality and reconstruction voxel dimensions on the image acquisition parameters (total scan angle, angular increment) of the DBT system and the properties of the objects of interest.

Previous methods for optimizing the scan times for PET transmission and emission scans under a total scan time constraint were based on linear non-statistical methods and used noise equivalent counts (NEC) criteria. The scan times determined by NEC analysis may be suboptimal when nonlinear statistical image reconstruction methods are used. For statistical image reconstruction, the predicted variance in selected regions of interest is an appropriate alternative to NEC analysis. The authors propose a new method for optimizing the relative scan times (fractions) based on analytical approximations to the covariance of images reconstructed by both conventional and penalized-likelihood methods. The authors perform simulations to compare predicted standard deviations with empirical ones. Results show that for statistical transmission image reconstruction, the optimal fraction of the scan time devoted to transmission scanning is shorter than for conventional transmission smoothing.

The radiation dose generated from x-ray computed tomography (CT) scans and its responsibility for increasing the risk of malignancy became a major concern in the medical imaging community. Accordingly, investigating possible approaches for image reconstruction from low-dose imaging protocols, which minimize the patient radiation exposure without affecting image quality, has become an issue of interest. Statistical reconstruction (SR) methods are known to achieve a superior image quality compared with conventional analytical methods. Effective physical noise modeling and possibilities of incorporating priors in the image reconstruction problem are the main advantages of the SR methods. Nevertheless, the high computation cost limits its wide use in clinical scanners. This paper presents a framework for SR in x-ray CT when the angular sampling rate of the projection data is low. The proposed framework is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. Therefore, the x-ray attenuation distribution in some regions of the object can be expected prior to the reconstruction. Under this assumption, the proposed method is developed by incorporating this prior information into the image reconstruction objective function to suppress streak artifacts. We limit the prior information to only a set of intensity values that represent the average intensity of the normal and expected homogeneous regions within the scanned object. This prior information can be easily computed in several x-ray CT applications. Considering the theory of compressed sensing, the objective function is formulated using the ℓ1 norm distance between the reconstructed image and the available intensity priors. Experimental comparative studies applied to simulated data and real data are used to evaluate the proposed method. The comparison indicates a significant improvement in image quality when the proposed method is used.

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: 1. To understand the fundamental principles behind tomosynthesis image acquisition. 2. To understand the fundamentals of tomosynthesis image reconstruction. 3. To learn the determinants of image quality and dose in DBT, including measurement techniques. 4. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care

As a special case of tomography, digital breast tomosynthesis (DBT) can realize quasi-3D image reconstruction for breast lesion detection from few-view and limited-angle projection data. For DBT image reconstruction, iterative algorithms are needed to suppress artifacts due to undersampling, and adaptive regularizations are necessary for preserving the edges of masses and calcifications. This paper presents a novel reconstruction method by regularizing projection onto convex sets (POCS) with multiscale Tikhonov-total variation (MTTV). The regularization, known as adaptive multiscale anisotropic diffusion, is able to preserve edges to a considerable extent and selectively suppress noise without introducing artifacts. The proposed method is referred to as MTTV-POCS and is evaluated quantitatively using 3D numerical breast and Shepp-Logan phantoms as well as two clinical volume images acquired from an advanced DBT machine. Experimental results show that the proposed method has better performance in metrics of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) over two existing methods: adaptive-steepest-descent-POCS (ASD-POCS) and selective-diffusion regularized simultaneous algebraic reconstruction technique (SD-SART). As indicated by the results, the proposed method is applicable to DBT for high-quality image reconstruction.

A new method for accurately portraying the impact of low-dose imaging techniques in C-arm cone-beam CT (CBCT) is presented and validated, allowing identification of minimum-dose protocols suitable to a given imaging task on a patient-specific basis in scenarios that require repeat intraoperative scans. To accurately simulate lower-dose techniques and account for object-dependent noise levels (x-ray quantum noise and detector electronics noise) and correlations (detector blur), noise of the proper magnitude and correlation was injected into the projections from an initial CBCT acquired at the beginning of a procedure. The resulting noisy projections were then reconstructed to yield low-dose preview (LDP) images that accurately depict the image quality at any level of reduced dose in both filtered backprojection and statistical image reconstruction. Validation studies were conducted on a mobile C-arm, with the noise injection method applied to images of an anthropomorphic head phantom and cadaveric torso across a range of lower-dose techniques. Comparison of preview and real CBCT images across a full range of techniques demonstrated accurate noise magnitude (within ~5%) and correlation (matching noise-power spectrum, NPS). Other image quality characteristics (e.g., spatial resolution, contrast, and artifacts associated with beam hardening and scatter) were also realistically presented at all levels of dose and across reconstruction methods, including statistical reconstruction. Generating low-dose preview images for a broad range of protocols gives a useful method to select minimum-dose techniques that accounts for complex factors of imaging task, patient-specific anatomy, and observer preference. The ability to accurately simulate the influence of low-dose acquisition in statistical reconstruction provides an especially valuable means of identifying low-dose limits in a manner that does not rely on a model for the nonlinear reconstruction process or a model of observer performance.

In this study, we evaluated the improvement of image quality in digital breast tomosynthesis under low-radiation dose conditions of pre-reconstruction processing using conditional generative adversarial networks [cGAN (pix2pix)]. Pix2pix pre-reconstruction processing with filtered back projection (FBP) was compared with and without multiscale bilateral filtering (MSBF) during pre-reconstruction processing. Noise reduction and preserve contrast rates were compared using full width at half-maximum (FWHM), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) in the in-focus plane using a BR3D phantom at various radiation doses [reference-dose (automatic exposure control reference dose: AECrd), 50% and 75% reduction of AECrd] and phantom thicknesses (40 mm, 50 mm, and 60 mm). The overall performance of pix2pix pre-reconstruction processing was effective in terms of FWHM, PSNR, and SSIM. At ~50% radiation-dose reduction, FWHM yielded good results independently of the microcalcification size used in the BR3D phantom, and good noise reduction and preserved contrast. PSNR results showed that pix2pix pre-reconstruction processing represented the minimum in the error with reference FBP images at an approximately 50% reduction in radiation-dose. SSIM analysis indicated that pix2pix pre-reconstruction processing yielded superior similarity when compared with and without MSBF pre-reconstruction processing at ~50% radiation-dose reduction, with features most similar to the reference FBP images. Thus, pix2pix pre-reconstruction processing is promising for reducing noise with preserve contrast and radiation-dose reduction in clinical practice.

This paper describes a statistical image reconstruction method for X-ray computed tomography (CT) that is based on a physical model that accounts for the polyenergetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume that the object consists of a given number of nonoverlapping materials, such as soft tissue and bone. The attenuation coefficient of each voxel is the product of its unknown density and a known energy-dependent mass attenuation coefficient. We formulate a penalized-likelihood function for this polyenergetic model and develop an ordered-subsets iterative algorithm for estimating the unknown densities in each voxel. The algorithm monotonically decreases the cost function at each iteration when one subset is used. Applying this method to simulated X-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artifacts.

75. Image quality and dose measurements in digital breast tomosynthesis

Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10–30 projections over a limited angle range of 15–60° with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle-dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.

This paper proposed a method for does reduction in breast imaging. While mammogram is a gold standard for early detection of breast cancer, it still limits to dense breast women. So, an additional investigation using a Digital Breast Tomosynthesis (DBT) is then required. This makes the total dose of radiation that patients received is approximately double. We propose a method to reduce the radiation does by generating a synthesized 2D mammographic image from DBT data using Super-Resolution (SR) technique. In this paper, the motion estimation algorithm is performed within a region of interest (ROI) and then the estimated motion parameters are used to reconstruct a SR image. The synthesized 2D mammographic image is the result of SR reconstruction step. We evaluated the method using the Peak signal to noise ratio (PSNR) and mean structure similarity (MSSIM). The results are 17.2383 and 0.9976 respectively. In additions, we also evaluated the result using the phantom passing score. Our synthesized 2D mammographic image got 5, 3, 4 for fibers, group of microcalcifications and masses, respectively.