Fiber-bundle-basis sparse reconstruction for high resolution wide-field microendoscopy

In order to observe deep regions of the brain, we propose the use of a fiber bundle for microendoscopy. Fiber bundles allow for the excitation and collection of fluorescence as well as wide field imaging while remaining largely impervious to image distortions brought on by bending. Furthermore, their thin diameter, from 200–500 µm, means their impact on living tissue, though not absent, is minimal. Although wide field imaging with a bundle allows for a high temporal resolution since no scanning is involved, the largest criticism of bundle imaging is the drastically lowered spatial resolution. In this paper, we make use of sparsity in the object being imaged to up sample the low resolution images from the fiber bundle with compressive sensing. We take each image in a single shot by using a measurement basis dictated by the quasi-crystalline arrangement of the bundle’s cores. We find that this technique allows us to increase the resolution of a typical image taken through a fiber bundle

On Using and Improving Gradient Domain Processing for Image Enhancement

Ph.DDOCTOR OF PHILOSOPH

Detection and classification in the compressed domain for multispectral images

Various applications would benefit from rapid inference on multispectral images at the point of sensing. However, the acquisition of a full-resolution multispectral image requires advanced spectrometers and prohibitive sensing time. Also, performing the high-level vision tasks such as classification and segmentation on the multispectral data consumes more computation power than on the common RGB images. Compressed sensing (CS) circumvents this sensing process usually using a random sensing matrix to acquire fewer measurements and reconstructs the multispectral image based on a sparsity assumption. The further high-level analysis of images is performed on the reconstructed high-dimensional images. And a random sensing matrix may not be physically realizable or the best fit for extracting information pertaining to a high-level vision task. A realizable low-cost data acquisition scheme and a fast processing system that makes inference based on the acquired signal are desired for multispectral images. In this thesis, we present a systematic way to jointly optimize the sensing scheme subject to optical realizability constraints, and make inference of the multispectral image in the compressed domain. In the first part of the thesis, we state some open questions in compressed inference. We review the theory on inference in the compressed domain. We formulate the problem for compressed inference and state metrics to evaluate the inference performance. We then review some existing realizable optical compressed sensing imaging systems designed for multispectral images and derive the forward model of data acquisition. The feasibility of performing detection, classification and segmentation in the compressed domain directly is then discussed for the multispectral images. Using tools from detection and estimation theory, we derive the optimal decision rule to perform compressed detection, classification and segmentation in a simple data setting. Also, the feasibility of adjusting the optical acquisition schemes jointly with the neural network is discussed. The architecture of neural networks that can achieve the performance of the optimal decision rule is proposed and the existence of optimal weights is discussed. Next, we use a synthetic dataset to compare the performance of the proposed neural network and the optimal decision rule. Several synthetic multispectral image datasets and a clinical tumor biopsy dataset are used to verify the improvement of the obtained sensing scheme and compare the performance of the neural network with that of a known optimal decision rule

Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder

The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the manual approach of ICA using the correlation coefficient, a comparative affect on the SNR using both approaches and a humanistic evaluation using visual inspections of the components of the stacked dense autoencoder reconstruction to that of the post ICA reconstruction where an inverse RMSE allowed for a further statistical evaluation of this comparison. It was found that the stacked dense autoencoder was unable to reconstruct random signal segments in any meaningful capacity when compared to that of ICA

Dictionaries for fast and informative dynamic MRI acquisition

Magnetic resonance (MR) imaging is an invaluable tool for medical research and diagnosis but suffers from inefficiencies. The speed of its acquisition mechanism, based on sequentially probing the interactions between nuclear atom spins and a changing magnetic field, is limited by atomic properties and scanner physics. Modern sampling techniques termed compressed sensing have nevertheless demonstrated how near perfect reconstructions are possible from undersampled, accelerated acquisitions, showing promise for more efficient MR acquisition paradigms. At the same time, information extraction from MR images through image analysis implies a considerable dimensionality reduction, in which an image is processed for the extraction of a few clinically useful parameters. This signals an inefficient handling of information in the separated treatment of acquisition and analysis that could be tackled by joining these two essential stages of the imaging pipeline. In this thesis, we explore the use of adaptive sparse modelling for novel acquisition strategies of cardiac cine MR data. Conventional compressed sensing MR acquisition relies on fixed basis transforms for sparse modelling, which are only able to guarantee suboptimal sparse modelling. We introduce spatio-temporal dictionaries that are able to optimally adapt sparse modelling by absorbing salient features of cardiac cine data, and demonstrate how they can outperform sampling methods based on fixed basis transforms. Additionally, we extend the framework introduced to handle parallel data acquisition. Given the flexibility of the formulation, we show how it can be combined with a labelling model that provides a segmentation of the image as a by-product of the reconstruction, hence performing joint reconstruction and analysis.Open Acces

3D shape instantiation for intra-operative navigation from a single 2D projection

Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs). For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies. For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed. For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique. The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces