Inverse scattering for reflection intensity phase microscopy

Reflection phase imaging provides label-free, high-resolution characterization of biological samples, typically using interferometric-based techniques. Here, we investigate reflection phase microscopy from intensity-only measurements under diverse illumination. We evaluate the forward and inverse scattering model based on the first Born approximation for imaging scattering objects above a glass slide. Under this design, the measured field combines linear forward-scattering and height-dependent nonlinear back-scattering from the object that complicates object phase recovery. Using only the forward-scattering, we derive a linear inverse scattering model and evaluate this model's validity range in simulation and experiment using a standard reflection microscope modified with a programmable light source. Our method provides enhanced contrast of thin, weakly scattering samples that complement transmission techniques. This model provides a promising development for creating simplified intensity-based reflection quantitative phase imaging systems easily adoptable for biological research.https://arxiv.org/abs/1912.07709Accepted manuscrip

SIMBA: scalable inversion in optical tomography using deep denoising priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf

Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall

SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue. AIM: We aim to reduce the chest wall\u27s effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH: We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall. RESULTS: The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth. CONCLUSIONS: Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties

Optimization Methods for Inverse Problems

Optimization plays an important role in solving many inverse problems. Indeed, the task of inversion often either involves or is fully cast as a solution of an optimization problem. In this light, the mere non-linear, non-convex, and large-scale nature of many of these inversions gives rise to some very challenging optimization problems. The inverse problem community has long been developing various techniques for solving such optimization tasks. However, other, seemingly disjoint communities, such as that of machine learning, have developed, almost in parallel, interesting alternative methods which might have stayed under the radar of the inverse problem community. In this survey, we aim to change that. In doing so, we first discuss current state-of-the-art optimization methods widely used in inverse problems. We then survey recent related advances in addressing similar challenges in problems faced by the machine learning community, and discuss their potential advantages for solving inverse problems. By highlighting the similarities among the optimization challenges faced by the inverse problem and the machine learning communities, we hope that this survey can serve as a bridge in bringing together these two communities and encourage cross fertilization of ideas.Comment: 13 page

Advancing combined radiological and optical scanning for breast-conserving surgery margin guidance

Breast cancer is one of the most common types of cancer worldwide, and standard-of-care for early-stage disease typically involves a lumpectomy or breast-conserving surgery (BCS). BCS involves the local resection of cancerous tissue, while sparring as much healthy tissue as possible. State-of-the-art methods for intraoperatively evaluating BCS margins are limited. Approximately 20% of BCS cases result in a tissue resection with cancer at or near the resection surface (i.e., a positive margin). A two-fold increase in ipsilateral breast cancer recurrence is associated with the presence of one or more positive margins. Consequently, positive margins often necessitate costly re-excision procedures to achieve a curative outcome. X-ray micro-computed tomography (CT) is emerging as a powerful ex vivo specimen imaging technology, as it provides robust three-dimensional sensing of tumor morphology rapidly. However, X-ray attenuation lacks contrast between soft tissues that are important for surgical decision making during BCS. Optical structured light imaging, including spatial frequency domain imaging and active line scan imaging, can act as adjuvant tools to complement micro-CT, providing wide field-of-view, non-contact sensing of relevant breast tissue subtypes on resection margins that cannot be differentiated by micro-CT alone. This thesis is dedicated to multimodal imaging of BCS tissues to ultimately improve intraoperative BCS margin assessment, reducing the number of positive margins after initial surgeries and thereby reducing the need for costly follow-up procedures. Volumetric sensing of micro-CT is combined with surface-weighted, sub-diffuse optical reflectance derived from high spatial frequency structured light imaging. Sub-diffuse reflectance plays the key role of providing enhanced contrast to a suite of normal, abnormal benign, and malignant breast tissue subtypes. This finding is corroborated through clinical studies imaging BCS specimen slices post-operatively and is further investigated through an observational clinical trial focused on combined, intraoperative micro-CT and optical imaging of whole, freshly resected BCS tumors. The central thesis of this work is that combining volumetric X-ray imaging and sub-diffuse optical scanning provides a synergistic multimodal imaging solution to margin assessment, one that can be readily implemented or retrofitted in X-ray specimen imaging systems and that could meaningfully improve surgical guidance during initial BCS procedures