Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

Unconstrained simultaneous scheme to fully couple reconstruction and registration for digital breast tomosynthesis: a feasible study

Digital breast tomosynthesis (DBT) provides a pseudo-3D reconstruction which addresses the limitation of superimposition of dense fibro-glandular tissue associated with conventional mammography. Reg- istration of temporal DBT volumes searches for the optimum deforma- tion to transform two observed images of the same object into a common reference frame. This aligns the two images via minimising an objective function that calculates the similarity between the two datasets. In this paper, we present a novel algorithm which combines recon- struction of a pair of temporal DBT acquisitions with their simultaneous registration. We approach this nonlinear inverse problem using a generic unconstrained optimisation scheme. To evaluate the performance of our method we use 2D and 3D software phantoms and demonstrate that this simultaneous approach has comparable results to performing these tasks sequentially or iteratively w.r.t both the reconstruction fidelity and the registration accuracy

A Digital X-Ray Tomosynthesis Coupled Near Infrared Spectral Tomography System for Dual-Modality Breast Imaging

A Near Infrared Spectral Tomography (NIRST) system has been developed and integrated into a commercial Digital Breast Tomosynthesis (DBT) scanner to allow structural and functional imaging of breast in vivo. The NIRST instrument uses an 8-wavelength continuous wave (CW) laser-based scanning source assembly and a 75-element silicon photodiode solid-state detector panel to produce dense spectral and spatial projection data from which spectrally constrained 3D tomographic images of tissue chromophores are produced. Integration of the optical imaging system into the DBT scanner allows direct co-registration of the optical and DBT images, while also facilitating the synergistic use of x-ray contrast as anatomical priors in optical image reconstruction. Currently, the total scan time for a combined NIRST-DBT exam is ~50s with data collection from 8 wavelengths in the optical scan requiring ~42s to complete. The system was tested in breast simulating phantoms constructed using intralipid and blood in an agarose matrix with a 3 cm x 2 cm cylindrical inclusion at 1 cm depth from the surface. Diffuse image reconstruction of total hemoglobin (HbT) concentration resulted in accurate recovery of the lateral size and position of the inclusion to within 6% and 8%, respectively. Use of DBT structural priors in the NIRST reconstruction process improved the quantitative accuracy of the HbT recovery, and led to linear changes in imaged versus actual contrast, underscoring the advantages of dual-modality optical imaging approaches. The quantitative accuracy of the system can be further improved with independent measurements of scattering properties through integration of frequency or time domain data

Calibration and Optimization of 3D Digital Breast Tomosynthesis Guided Near Infrared Spectral Tomography

Calibration of a three-dimensional multimodal digital breast tomosynthesis (DBT) x-ray and non-fiber based near infrared spectral tomography (NIRST) system is challenging but essential for clinical studies. Phantom imaging results yielded linear contrast recovery of total hemoglobin (HbT) concentration for cylindrical inclusions of 15 mm, 10 mm and 7 mm with a 3.5% decrease in the HbT estimate for each 1 cm increase in inclusion depth. A clinical exam of a patient\u27s breast containing both benign and malignant lesions was successfully imaged, with greater HbT was found in the malignancy relative to the benign abnormality and fibroglandular regions (11 μM vs. 9.5 μM). Tools developed improved imaging system characterization and optimization of signal quality, which will ultimately improve patient selection and subsequent clinical trial results

Unconstrained Simultaneous Scheme to Fully Couple Reconstruction and Registration for Digital Breast Tomosynthesis: A Feasible Study

Digital breast tomosynthesis (DBT) provides a pseudo-3D reconstruction which addresses the limitation of superimposition of dense fibro-glandular tissue associated with conventional mammography. Registration of temporal DBT volumes searches for the optimum deformation to transform two observed images of the same object into a common reference frame. This aligns the two images via minimising an objective function that calculates the similarity between the two datasets. In this paper, we present a novel algorithm which combines reconstruction of a pair of temporal DBT acquisitions with their simultaneous registration. We approach this nonlinear inverse problem using a generic unconstrained optimisation scheme. To evaluate the performance of our method we use 2D and 3D software phantoms and demonstrate that this simultaneous approach has comparable results to performing these tasks sequentially or iteratively w.r.t both the reconstruction fidelity and the registration accuracy

First‐arrival traveltime sound speed inversion with a priori information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134972/1/mp5955.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134972/2/mp5955_am.pd

Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram is a challenging task. Three-dimensional DBT, however, enables us to step through the forest, i.e., the breast, reducing the confounding effect of superimposed tissue and so (potentially) increasing the sensitivity and specificity of cancer detection. The workflow in which DBT would be used clinically, involves two key tasks: reconstruction, to generate a 3D image of the breast, and registration, to enable images from different visits to be compared as is routinely performed by radiologists working with conventional mammograms. Conventional approaches proposed in the literature separate these steps, solving each task independently. This can be effective if reconstructing using a complete set of data. However, for ill-posed limited-angle problems such as DBT, estimating the deformation is difficult because of the significant artefacts associated with DBT reconstructions, leading to severe inaccuracies in the registration. The aim of my work is to find and evaluate methods capable of allying these two tasks, which will enhance the performance of each process as a result. Consequently, I prove that the processes of reconstruction and registration of DBT are not independent but reciprocal. This thesis proposes innovative numerical approaches combining reconstruction of a pair of temporal DBT acquisitions with their registration iteratively and simultaneously. To evaluate the performance of my methods I use synthetic images, breast MRI, and DBT simulations with in-vivo breast compressions. I show that, compared to the conventional sequential method, jointly estimating image intensities and transformation parameters gives superior results with respect to both reconstruction fidelity and registration accuracy

A Nonlinear Least Squares Method for Solving the Joint Reconstruction and Registration Problem in Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) offers potential insight into the fine details of normal fibroglandular tissues and abnormal lesions, e.g., masses and micro-calcifications associated with breast cancer, by the production of a pseudo-3D image. In addition, it avoids the superposition, which is usually found in X-ray mammography, with a comparable radiation dose. Algorithms to aid the human observer process DBT data sets involve two key tasks: reconstruction and registration. In established medical image modalities these tasks are normally performed sequentially; the images are reconstructed and then registered. In this paper, we hypothesise that, for DBT in particular, combining the optimisation processes of reconstruction and registration into a single algorithm will offer satisfactory for both tasks. Based on this hypothesis, we have devised a mathematical framework to combine these two tasks, and have implemented both affine and non-linear B-spline registration transformation models as plug-ins. By applying our algorithm to various simulated data, we demonstrate the success of our method in terms of both reconstruction fidelity and in the registration accuracy of the recovered transformations

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