Optimization of Three-Dimensional (3D) Chemical Imaging by Soft X-Ray Spectro-Tomography Using a Compressed Sensing Algorithm.

Soft X-ray spectro-tomography provides three-dimensional (3D) chemical mapping based on natural X-ray absorption properties. Since radiation damage is intrinsic to X-ray absorption, it is important to find ways to maximize signal within a given dose. For tomography, using the smallest number of tilt series images that gives a faithful reconstruction is one such method. Compressed sensing (CS) methods have relatively recently been applied to tomographic reconstruction algorithms, providing faithful 3D reconstructions with a much smaller number of projection images than when conventional reconstruction methods are used. Here, CS is applied in the context of scanning transmission X-ray microscopy tomography. Reconstructions by weighted back-projection, the simultaneous iterative reconstruction technique, and CS are compared. The effects of varying tilt angle increment and angular range for the tomographic reconstructions are examined. Optimization of the regularization parameter in the CS reconstruction is explored and discussed. The comparisons show that CS can provide improved reconstruction fidelity relative to weighted back-projection and simultaneous iterative reconstruction techniques, with increasingly pronounced advantages as the angular sampling is reduced. In particular, missing wedge artifacts are significantly reduced and there is enhanced recovery of sharp edges. Examples of using CS for low-dose scanning transmission X-ray microscopy spectroscopic tomography are presented.

Non-negative matrix analysis for effective feature extraction in X-ray spectromicroscopy.

X-Ray absorption spectromicroscopy provides rich information on the chemical organization of materials down to the nanoscale. However, interpretation of this information in studies of "natural" materials such as biological or environmental science specimens can be complicated by the complex mixtures of spectroscopically complicated materials present. We describe here the shortcomings that sometimes arise in previously-employed approaches such as cluster analysis, and we present a new approach based on non-negative matrix approximation (NNMA) analysis with both sparseness and cluster-similarity regularizations. In a preliminary study of the large-scale biochemical organization of human spermatozoa, NNMA analysis delivers results that nicely show the major features of spermatozoa with no physically erroneous negative weightings or thicknesses in the calculated image.

MANTiS: a program for the analysis of X-ray spectromicroscopy data.

Spectromicroscopy combines spectral data with microscopy, where typical datasets consist of a stack of images taken across a range of energies over a microscopic region of the sample. Manual analysis of these complex datasets can be time-consuming, and can miss the important traits in the data. With this in mind we have developed MANTiS, an open-source tool developed in Python for spectromicroscopy data analysis. The backbone of the package involves principal component analysis and cluster analysis, classifying pixels according to spectral similarity. Our goal is to provide a data analysis tool which is comprehensive, yet intuitive and easy to use. MANTiS is designed to lead the user through the analysis using story boards that describe each step in detail so that both experienced users and beginners are able to analyze their own data independently. These capabilities are illustrated through analysis of hard X-ray imaging of iron in Roman ceramics, and soft X-ray imaging of a malaria-infected red blood cell.

Dynamic shape instantiation for intra-operative guidance.

Primary liver cancer and oligometastatic liver disease are one of the major causes of mortality worldwide and its treatment ranges from surgery to more minimally invasive ablative procedures. With the increasing availability of minimally invasive hepatic approaches, a real-time method of determining the 3D structure of the liver and its location during the respiratory cycle is clinically important. However, during treatment, it is difficult to acquire images spanning the entire 3D volume rapidly. In this paper, a dynamic 3D shape instantiation scheme is developed for providing subject-specific optimal scan planning. Using only limited planar information, it is possible to instantiate the entire 3D geometry of the organ of interest. The efficacy of the proposed method is demonstrated with both detailed numerical simulation and a liver phantom with known ground-truth data. Preliminary clinical application of the technique is evaluated on a patient group with metastatic liver tumours.

From medical images to minimally invasive intervention: Computer assistance for robotic surgery.

Minimally invasive surgery has been established as an important way forward in surgery for reducing patient trauma and hospitalization with improved prognosis. The introduction of robotic assistance enhances the manual dexterity and accuracy of instrument manipulation. Further development of the field in using pre- and intra-operative imaging guidance requires the integration of the general anatomy of the patient with clear pathologic indications and geometrical information for preoperative planning and intra-operative manipulation. It also requires effective visualization and the recreation of haptic and tactile sensing with dynamic active constraints to improve consistency and safety of the surgical procedures. This paper describes key technical considerations of tissue deformation tracking, 3D reconstruction, subject-specific modeling, image guidance and augmented reality for robotic assisted minimally invasive surgery. It highlights the importance of adapting preoperative surgical planning according to intra-operative data and illustrates how dynamic information such as tissue deformation can be incorporated into the surgical navigation framework. Some of the recent trends are discussed in terms of instrument design and the usage of dynamic active constraints and human-robot perceptual docking for robotic assisted minimally invasive surgery.

Dynamic active constraints for hyper-redundant flexible robots.

In robot-assisted procedures, the surgeon's ability can be enhanced by navigation guidance through the use of virtual fixtures or active constraints. This paper presents a real-time modeling scheme for dynamic active constraints with fast and simple mesh adaptation under cardiac deformation and changes in anatomic structure. A smooth tubular pathway is constructed which provides assistance for a flexible hyper-redundant robot to circumnavigate the heart with the aim of undertaking bilateral pulmonary vein isolation as part of a modified maze procedure for the treatment of debilitating arrhythmia and atrial fibrillation. In contrast to existing approaches, the method incorporates detailed geometrical constraints with explicit manipulation margins of the forbidden region for an entire articulated surgical instrument, rather than just the end-effector itself. Detailed experimental validation is conducted to demonstrate the speed and accuracy of the instrument navigation with and without the use of the proposed dynamic constraints.

Dynamic view expansion for enhanced navigation in Natural Orifice Transluminal Endoscopic Surgery.

Natural Orifice Transluminal Endoscopic Surgery (NOTES) is an emerging surgical technique with increasing global interest. It has recently transcended the boundaries of clinical experiments towards initial clinical evaluation. Although profound benefits to the patient have been demonstrated, NOTES requires highly skilled endoscopists for it to be performed safely and successfully. This predominantly reflects the skill required to navigate a flexible endoscope through a spatially complex environment. This paper presents a method to extend the visual field of the surgeon without compromising on the safety of the patient. The proposed dynamic view expansion uses a novel parallax correction scheme to provide enhanced visual cues that aid the navigation and orientation during NOTES surgery in periphery, while leaving the focal view undisturbed. The method was validated using a natural orifice simulated surgical environment and demonstrated on in vivo porcine data.

Super resolution in robotic-assisted minimally invasive surgery.

In minimally invasive surgery, a small field of view is often required to achieve a large magnification factor during micro-scale tasks such as coronary anastomosis. However, constantly changing the orientation and focal length of the laparoscope camera is cumbersome, and can impose extra visual and cognitive load on the operating surgeon in terms of realigning the visual pathways and anatomical landmarks. The purpose of this paper is to investigate the use of fixational movements in robotic-assisted minimally invasive surgery, such that the perceived resolution of the foveal field of view is greater than the intrinsic resolution of the laparoscope camera. The proposed technique is based on super resolution imaging using projection onto convex sets for monochrome images, and a maximum a posteriori method with a novel YIQ space-based prior for color images. Validation with both phantom and in vivo data from totally endoscopic coronary artery bypass surgery is provided.

Pq-space based non-photorealistic rendering for augmented reality.

The increasing use of robotic assisted minimally invasive surgery (MIS) provides an ideal environment for using Augmented Reality (AR) for performing image guided surgery. Seamless synthesis of AR depends on a number of factors relating to the way in which virtual objects appear and visually interact with a real environment. Traditional overlaid AR approaches generally suffer from a loss of depth perception. This paper presents a new AR method for robotic assisted MIS, which uses a novel pq-space based non-photorealistic rendering technique for providing see-through vision of the embedded virtual object whilst maintaining salient anatomical details of the exposed anatomical surface. Experimental results with both phantom and in vivo lung lobectomy data demonstrate the visual realism achieved for the proposed method and its accuracy in providing high fidelity AR depth perception.

Motion-compensated MR valve imaging with COMB tag tracking and super-resolution enhancement.

Understanding the morphology and function of heart valves is important to the study of underlying causes of heart failure. Existing techniques such as those based on echocardiography are limited by the relatively low signal-to-noise ratio (SNR), attenuation artefacts, and restricted access. The alternative of cardiovascular MR imaging offers versatility and accuracy in 3D localisation, but is hampered by large movements of the valves throughout the cardiac cycle. This paper presents a motion-compensated adaptive imaging approach for MR valve imaging. To illustrate its clinical potential, 3D motion of the aortic valve plane is first captured through a single breath-hold COMB tag pre-scan and then tracked in real-time with an automatic method based on multi-resolution image registration. Motion-compensated coverage of the aortic valve is then acquired prospectively, thus allowing its clear 3D reconstruction and visualisation. To provide isotropic voxel coverage of the imaging volume, retrospective projection onto convex sets (POCS) super-resolution enhancement is applied to the slice-select direction. In vivo results demonstrate the effectiveness of the proposed motion-compensation and super-resolution schemes for depicting the structure of the valve leaflets throughout the cardiac cycle. The proposed method fundamentally changes the way MR imaging is performed by transforming it from a spatially to materially localised imaging method. This also has important implications for quantifying blood flow and myocardial perfusion, as well as tracking anatomy and function of the heart.

Motion-compensated MR valve imaging with COMB tag tracking and super-resolution enhancement.

MR imaging of the heart valve leaflets is a challenging problem due to their large movements throughout the cardiac cycle. This paper presents a motion-compensated imaging approach with COMB tagging for valve imaging. It involves an automatic method for tracking the full 3D motion of the valve plane so as to provide a motion-tracked acquisition scheme. Super-resolution enhancement is then applied to the slice-select direction so that the partial volume effect is minimised. In vivo results have shown that in terms of slice positioning, the method has equivalent accuracy to that of a manual approach whilst being quicker and more consistent. The use of multiple parallel COMB tags will permit adaptive imaging that follows tissue motion. This will have significant implications for quantification of myocardial perfusion and tracking anatomy, functions that are traditionally difficult in MRI.

The use of super resolution in robotic assisted minimally invasive surgery.

In minimally invasive surgery, a small field-of-view is often required for achieving a large magnification factor during micro-scale tasks such as coronary anastomosis. Constant change of the orientation and focal length of the laparoscope camera, however, is cumbersome and can impose extra visual and cognitive load to the operating surgeon in realigning the visual pathways and anatomical landmarks. The purpose of this paper is to investigate the use of fixational movements for robotic assisted minimal invasive surgery such that the perceived resolution of the foveal field-of-view is greater than the intrinsic resolution of the laparoscope camera. The proposed technique is based on super resolution imaging using projection onto convex sets. Validation with both phantom and in vivo data from totally endoscopic coronary artery bypass surgery is provided.