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

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

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

Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis

Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation,\ud which significantly increases image noise. This work investigates the influence of a denoising algorithm and the\ud Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a\ud variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent\ud Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms,\ud designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe\ud domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D\ud mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software\ud breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising\ud was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality\ud was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural\ud similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe\ud transformation and when denoising was applied to each projection image before reconstruction; in this case, an average\ud increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed\ud by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the\ud reconstruction algorithm modifies the noise properties of the DBT images.FAPESPCNP