Spatio-Temporal Reconstruction Techniques for Optical Microscopy

Optical microscopy offers the unique possibility to study living samples under conditions akin to their native state. However, the technique is not void of inherent problems such as optical blur due to light diffraction, contamination with out-of-focus light from adjacent focal planes, and spherical aberrations. Furthermore, with a dearth of techniques that are capable of imaging multiple focal sections in quick succession, the multi-dimensional capture of dynamically changing samples remains a challenge of its own. Computational techniques that use auxiliary knowledge about the imaging system and the sample to mitigate these problems are hence of great interest in optical microscopy.The first part of this thesis deals with the design of a discrete model to characterize light propagation. Following the scalar diffraction theory in optics, we propose a discrete algorithm, based on generalized sampling theory, to reverse the coherent diffraction process via back propagation. The algorithm consists of a wavelet-based model for the spherical waves emanating from the object of interest and an optimized multi-rate filtering protocol for reconstruction from the diffraction data recorded by non-ideal detectors. The second part of this thesis describes a spatial registration tool designed for multi-view microscopy. Here, the imaged sample is rotated about a lateral axis for the acquisition of multiple 3D datasets from different views in order to subsequently alleviate the severe axial blur found in each such dataset. Automatic algorithms that only rely on maximizing pixel-based similarity provide poor results in such applications owing to the anisotropic point-spread-function (PSF) of optical microscopes. We propose a pyramid-based spatial registration algorithm that re-blurs the multi-view datasets with transformed forms of the PSF in order to make them comparable, before maximizing their pixel-based similarity for registration.The third part of this thesis describes a fast converging iterative multi-view deconvolution technique that can be applied to the spatially registered forms of the 3D datasets acquired using multi-view microscopy. Our sparsity based algorithm solves a non-linear objective function to jointly deconvolve and fuse the multi-view datasets to finally produce a single deblurred 3D result that has nearly isotropic spatial resolution.The fourth part of this thesis addresses problems due to spherical aberrations encountered during the imaging of thick samples in optical microscopy. The depth-varying nature of the optical blur found in such cases renders fast and efficient shift-invariant deconvolution techniques to be inapplicable. Here, we propose a fast iterative-shrinkage-thresholding shift-variant 3D deconvolution method that uses depth-dependent PSFs to reconstruct a 3D deblurred form of the imaged thick specimen. The final part of this thesis describes a non-rigid temporal registration tool that aids in the multi-dimensional imaging of quasi-periodic processes such as cardiac cycles. We propose a variant of dynamic time warping that is capable of both temporally warping and wrapping an input sequence by allowing for jump discontinuities in the non-linear temporal alignment function akin to those found in wrapped phase functions. This work provides a new set of tools for spatio-temporal reconstruction in optical microscopy and we anticipate them to be useful for a wide range of problems in practice

Left ventricular long axis function assessed during cine-cardiovascular magnetic resonance is an independent predictor of adverse cardiac events

Left ventricular pump function requires a complex interplay involving myocardial fibers orientated in the longitudinal, oblique and circumferential directions. Long axis dysfunction appears to be an early marker for a number of pathological states. We hypothesized that mitral annular plane systolic excursion (MAPSE) measured during cine-cardiovascular magnetic resonance (CMR) reflects changes in long axis function and may be an early marker for adverse cardiovascular outcomes. The aims of this study were therefore: 1) To assess the feasibility and reproducibility of MAPSE measurements during routine cine-CMR; and 2) To assess whether MAPSE, as a surrogate for long axis function, is a predictor of major adverse cardiovascular events (MACE)

Spatio-Temporal Reconstruction Techniques for Optical Microscopy

Optical microscopy offers the unique possibility to study living samples under conditions akin to their native state. However, the technique is not void of inherent problems such as optical blur due to light diffraction, contamination with out-of-focus light from adjacent focal planes, and spherical aberrations. Furthermore, with a dearth of techniques that are capable of imaging multiple focal sections in quick succession, the multi-dimensional capture of dynamically changing samples remains a challenge of its own. Computational techniques that use auxiliary knowledge about the imaging system and the sample to mitigate these problems are hence of great interest in optical microscopy.The first part of this thesis deals with the design of a discrete model to characterize light propagation. Following the scalar diffraction theory in optics, we propose a discrete algorithm, based on generalized sampling theory, to reverse the coherent diffraction process via back propagation. The algorithm consists of a wavelet-based model for the spherical waves emanating from the object of interest and an optimized multi-rate filtering protocol for reconstruction from the diffraction data recorded by non-ideal detectors. The second part of this thesis describes a spatial registration tool designed for multi-view microscopy. Here, the imaged sample is rotated about a lateral axis for the acquisition of multiple 3D datasets from different views in order to subsequently alleviate the severe axial blur found in each such dataset. Automatic algorithms that only rely on maximizing pixel-based similarity provide poor results in such applications owing to the anisotropic point-spread-function (PSF) of optical microscopes. We propose a pyramid-based spatial registration algorithm that re-blurs the multi-view datasets with transformed forms of the PSF in order to make them comparable, before maximizing their pixel-based similarity for registration.The third part of this thesis describes a fast converging iterative multi-view deconvolution technique that can be applied to the spatially registered forms of the 3D datasets acquired using multi-view microscopy. Our sparsity based algorithm solves a non-linear objective function to jointly deconvolve and fuse the multi-view datasets to finally produce a single deblurred 3D result that has nearly isotropic spatial resolution.The fourth part of this thesis addresses problems due to spherical aberrations encountered during the imaging of thick samples in optical microscopy. The depth-varying nature of the optical blur found in such cases renders fast and efficient shift-invariant deconvolution techniques to be inapplicable. Here, we propose a fast iterative-shrinkage-thresholding shift-variant 3D deconvolution method that uses depth-dependent PSFs to reconstruct a 3D deblurred form of the imaged thick specimen. The final part of this thesis describes a non-rigid temporal registration tool that aids in the multi-dimensional imaging of quasi-periodic processes such as cardiac cycles. We propose a variant of dynamic time warping that is capable of both temporally warping and wrapping an input sequence by allowing for jump discontinuities in the non-linear temporal alignment function akin to those found in wrapped phase functions. This work provides a new set of tools for spatio-temporal reconstruction in optical microscopy and we anticipate them to be useful for a wide range of problems in practice

Intensity-Based Point-Spread-Function-Aware Registration for Multi-View Applications in Optical Microscopy

We present an algorithm to spatially register two volumetric datasets related via a rigid-body transform and degraded by an anisotropic point-spread-function (PSF). Registration is necessary, for example, when fusing data in multi-view microscopy. Automatic algorithms that only rely on maximizing pixel similarity, without accounting for the anisotropic image formation process, provide poor results in such applications. We propose to solve this problem by re-blurring the reference and test data with transformed forms of the PSF, in order to make them comparable, before minimizing the mean squared intensity difference between them. Our approach extends the pyramid-based sub-pixel registration algorithm proposed by Thévenaz et al., 1998, that employs an improved form of the Marquardt-Levenberg algorithm. We show, via simulations, that our method is more accurate than the conventional approach that does not account for the PSF. We demonstrate our algorithm in practice by registering multi-view volumes of a zebrafish larva acquired using a wide-field microscope

Management of acute alveolar abscess: A case report

The article is a case report on the management of acute periapical abscess associated with maxillary left first premolar by conventional root canal treatment and incision drainage. Radiographic examination on follow up showed healing of the endodontic lesion. Clinical evaluation also revealed no pain and sensitivity in relation to the upper left first premolar. Early diagnosis and proper management of the endodontic lesion led to successful resolution of this infection