Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <+/-3%; dose <+/-20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.Comment: Accepted in Medical Image Analysi

Investigation of physical processes in digital x-ray tomosynthesis imaging of the breast

Early detection is one of the most important factors in the survival of patients diagnosed with breast cancer. For this reason the development of improved screening mammography methods is one of primary importance. One problem that is present in standard planar mammography, which is not solved with the introduction of digital mammography, is the possible masking of lesions by normal breast tissue because of the inherent collapse of three-dimensional anatomy into a two-dimensional image. Digital tomosynthesis imaging has the potential to avoid this effect by incorporating into the acquired image information on the vertical position of the features present in the breast. Previous studies have shown that at an approximately equivalent dose, the contrast-detail trends of several tomosynthesis methods are better than those of planar mammography. By optimizing the image acquisition parameters and the tomosynthesis reconstruction algorithm, it is believed that a tomosynthesis imaging system can be developed that provides more information on the presence of lesions while maintaining or reducing the dose to the patient. Before this imaging methodology can be translated to routine clinical use, a series of issues and concerns related to tomosynthesis imaging must be addressed. This work investigates the relevant physical processes to improve our understanding and enable the introduction of this tomographic imaging method to the realm of clinical breast imaging. The processes investigated in this work included the dosimetry involved in tomosynthesis imaging, x-ray scatter in the projection images, imaging system performance, and acquisition geometry. A comprehensive understanding of the glandular dose to the breast during tomosynthesis imaging, as well as the dose distribution to most of the radiosensitive tissues in the body from planar mammography, tomosynthesis and dedicated breast computed tomography was gained. The analysis of the behavior of x-ray scatter in tomosynthesis yielded an in-depth characterization of the variation of this effect in the projection images. Finally, the theoretical modeling of a tomosynthesis imaging system, combined with the other results of this work was used to find the geometrical parameters that maximize the quality of the tomosynthesis reconstruction.Ph.D.Andrew Karellas, John N. Oshinski, Xiaoping P. Hu, Carl J. D’Orsi and Ernest V. Garci

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

A dual modality, DCE-MRI and x-ray, physical phantom for quantitative evaluation of breast imaging protocols

The current clinical standard for breast cancer screening is mammography. However, this technique has a low sensitivity which results in missed cancers. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as a promising technique for breast cancer diagnosis and has been reported as being superior to mammography for screening of high-risk women and evaluation of extent of disease. At the same time, low and variable specificity has been documented in the literature as well as a rising number of mastectomies possibly due to the increasing use of DCE-MRI. In this study, we developed and characterized a dual-modality, x-ray and DCE-MRI, anthropomorphic breast phantom for the quantitative assessment of breast imaging protocols. X-ray properties of the phantom were quantitatively compared with patient data, including attenuation coefficients, which matched human values to within the measurement error, and tissue structure using spatial covariance matrices of image data, which were found to be similar in size to patient data. Simulations of the phantom scatter-to-primary ratio (SPR) were produced and experimentally validated then compared with published SPR predictions for homogeneous phantoms. SPR values were as high as 85% in some areas and were heavily influenced by the heterogeneous tissue structure. MRI properties of the phantom, T1 and T2 relaxation values and tissue structure, were also quantitatively compared with patient data and found to match within two error bars. Finally, a dynamic lesion that mimics lesion border shape and washout curve shape was included in the phantom. High spatial and temporal resolution x-ray measurements of the washout curve shape were performed to determine the true contrast agent concentration as a function of time. DCE-MRI phantom measurements using a clinical imaging protocol were compared against the x-ray truth measurements. MRI signal intensity curves were shown to be less specific to lesion type than the x-ray derived contrast agent concentration curves. This phantom allows, for the first time, for quantitative evaluation of and direct comparisons between x-ray and MRI breast imaging modalities in the context of lesion detection and characterization

Image reconstruction and processing for stationary digital tomosynthesis systems

Digital tomosynthesis (DTS) is an emerging x-ray imaging technique for disease and cancer screening. DTS takes a small number of x-ray projections to generate pseudo-3D images, it has a lower radiation and a lower cost compared to the Computed Tomography (CT) and an improved diagnostic accuracy compared to the 2D radiography. Our research group has developed a carbon nanotube (CNT) based x-ray source. This technology enables packing multiple x-ray sources into one single x-ray source array. Based on this technology, our group built several stationary digital tomosynthesis (s-DTS) systems, which have a faster scanning time and no source motion blur. One critical step in both tomosynthesis and CT is image reconstruction, which generates a 3D image from the 2D measurement. For tomosynthesis, the conventional reconstruction method runs fast but fails in image quality. A better iterative method exists, however, it is too time-consuming to be used in clinics. The goal of this work is to develop fast iterative image reconstruction algorithm and other image processing techniques for the stationary digital tomosynthesis system, improving the image quality affected by the hardware limitation. Fast iterative reconstruction algorithm, named adapted fan volume reconstruction (AFVR), was developed for the s-DTS. AFVR is shown to be an order of magnitude faster than the current iterative reconstruction algorithms and produces better images over the classical filtered back projection (FBP) method. AFVR was implemented for the stationary digital breast tomosynthesis system (s-DBT), the stationary digital chest tomosynthesis system (s-DCT) and the stationary intraoral dental tomosynthesis system (s-IOT). Next, scatter correction technique for stationary digital tomosynthesis was investigated. A new algorithm for estimating scatter profile was developed, which has been shown to improve the image quality substantially. Finally, the quantitative imaging was investigated, where the s-DCT system was used to assess the coronary artery calcium score.Doctor of Philosoph

Image Quality Comparison between Digital and Synthetic 2D Mammograms: A Qualitative and Quantitative Phantom Study

The recent introduction of digital breast tomosynthesis (DBT) have lead to improvements in sensitivity and specificity of breast cancer detection, especially in cases of tumors developed in dense breasts. Since DBT provides tomographic slices of an entire tissue volume, it reduces the inherent tissue overlapping limitation of digital mammography (DM). In addition, DBT combined with DM has been proven to decrease recall and increase invasive cancer detection rates in breast cancer screening. However, the employment of DBT+DM implies a not negligible increment of patients absorbed dose. Therefore, Synthesized mammograms (SMs) generated from the DBT data have been recently introduced to eliminate the need of an additional DM. However, several studies showed differences between DM and SM images and some studies found contrasting results in terms of image quality when DM and SM images were compared. In our phantom study, we objectively compare image quality of SM and DM images in terms of noise, spatial resolution and contrast properties. Additionally, a qualitative analysis of the ACR mammographic phantom was performed in both modalities to assess the detectability of different features. SM images were characterized by different texture with respect to DM images, showing lower overall performances in terms of contrast-to-noise ratio and modulation transfer function. However, the goal of SM images is to provide a useful two-dimensional guide complementary to the DBT dataset and the performances in terms of high-contrast features detectability were satisfactory in comparison to those obtained in DM

Modeling the Anisotropic Resolution and Noise Properties of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) is a 3D imaging modality in which a reconstruction of the breast is generated from various x-ray projections. Due to the newness of this technology, the development of an analytical model of image quality has been on-going. In this thesis, a more complete model is developed by addressing the limitations found in the previous linear systems (LS) model [Zhao, Med. Phys. 2008, 35(12): 5219-32]. A central assumption of the LS model is that the angle of x-ray incidence is approximately normal to the detector in each projection. To model the effect of oblique x-ray incidence, this thesis generalizes Swank\u27s calculations of the transfer functions of x-ray fluorescent screens to arbitrary incident angles. In the LS model, it is also assumed that the pixelation in the reconstruction grid is the same as the detector; hence, the highest frequency that can be resolved is the detector alias frequency. This thesis considers reconstruction grids with smaller pixelation to investigate super-resolution, or visibility of higher frequencies. A sine plate is introduced as a conceptual test object to analyze super-resolution. By orienting the long axis of the sine plate at various angles, the feasibility of oblique reconstruction planes is also investigated. This formulation differs from the LS model in which reconstruction planes are parallel to the breast support. It is shown that the transfer functions for arbitrary angles of x-ray incidence can be modeled in closed form. The high frequency modulation transfer function (MTF) and detective quantum efficiency (DQE) are degraded due to oblique x-ray incidence. In addition, using the sine plate, it is demonstrated that a reconstruction can resolve frequencies exceeding the detector alias frequency. Experimental images of bar patterns verified the existence of super-resolution. Anecdotal clinical examples showed that super-resolution improves the visibility of microcalcifications. The feasibility of oblique reconstructions was established theoretically with the sine plate and was validated experimentally with bar patterns. This thesis develops a more complete model of image quality in DBT by addressing the limitations of the LS model. In future studies, this model can be used as a tool for optimizing DBT