Exploring the visualisation of the cervicothoracic junction in lateral spine radiography using high dynamic range techniques

The C7/T1 junction is an important landmark for spinal injuries. It is traditionally difficult to visualise in a lateral X-ray image due to the rapid change in the bodys anatomy at the level of the junction, where the shoulders cause a large increase in attenuation. To explore methods of enhancing the appearance of this important area, lateral radiographs of a shoulder girdle phantom were subjected to high dynamic range (HDR) processing and tone mapping. A shoulder girdle phantom was constructed using Perspex, shoulder girdle and vertebral bones and water to reproduce the attenuation caused by soft tissue. The design allowed for the removal of the shoulder girdle in order for the cervical vertebrae to be imaged separately. HDR was explored for single and dual-energy X-ray images of the phantom. In the case of single-image HDR, the HDR image of the phantom without water was constructed by combining images created with varying contrast windows throughout the contrast range of an X-ray image. It was found that an overlap of larger contrast windows with a lower number of images performed better than smaller contrast windows and more images when creating an HDR to be tone mapped. Poor results on the phantom without water precluded further testing of single-image HDR on images of the phantom with water, which would have higher attenuation. Dual energy HDR image construction explored images of the phantom both with and without water. A set of images acquired at lower attenuation (phantom without water) was used to evaluate the performance of the various tone mapping algorithms. The tone mapping was then performed on the phantom images containing water. These results showed how each tone mapping algorithm differs and the effects of global vs. local processing. The results revealed that the built-in MatLab algorithm, based on an improved Ward histogram adjustment approach, produces the most desirable result. None of the HDR tone mapped images produced were diagnostically useful. Signal to noise ratio (SNR) analysis was performed on the cervical region of the HDR tone mapped image. It used the scan of the phantom without the shoulder girdle obstruction (imaged under the same conditions) as a reference image. The SNR results quantitatively show that the selection of exposure values affects the visualisation of the tone mapped image. The highest SNR was produced for the 100 - 120 kV dual energy X-ray image pair. The study was limited by the range of HDR image construction techniques employed and the tone mapping algorithms explored. Future studies could explore other HDR image construction techniques and the combination of global and local tone mapping algorithms. Furthermore, the phantom can be replaced by a cadaver for algorithm testing under more realistic conditions

Variational image fusion

The main goal of this work is the fusion of multiple images to a single composite that offers more information than the individual input images. We approach those fusion tasks within a variational framework. First, we present iterative schemes that are well-suited for such variational problems and related tasks. They lead to efficient algorithms that are simple to implement and well-parallelisable. Next, we design a general fusion technique that aims for an image with optimal local contrast. This is the key for a versatile method that performs well in many application areas such as multispectral imaging, decolourisation, and exposure fusion. To handle motion within an exposure set, we present the following two-step approach: First, we introduce the complete rank transform to design an optic flow approach that is robust against severe illumination changes. Second, we eliminate remaining misalignments by means of brightness transfer functions that relate the brightness values between frames. Additional knowledge about the exposure set enables us to propose the first fully coupled method that jointly computes an aligned high dynamic range image and dense displacement fields. Finally, we present a technique that infers depth information from differently focused images. In this context, we additionally introduce a novel second order regulariser that adapts to the image structure in an anisotropic way.Das Hauptziel dieser Arbeit ist die Fusion mehrerer Bilder zu einem Einzelbild, das mehr Informationen bietet als die einzelnen Eingangsbilder. Wir verwirklichen diese Fusionsaufgaben in einem variationellen Rahmen. Zunächst präsentieren wir iterative Schemata, die sich gut für solche variationellen Probleme und verwandte Aufgaben eignen. Danach entwerfen wir eine Fusionstechnik, die ein Bild mit optimalem lokalen Kontrast anstrebt. Dies ist der Schlüssel für eine vielseitige Methode, die gute Ergebnisse für zahlreiche Anwendungsbereiche wie Multispektralaufnahmen, Bildentfärbung oder Belichtungsreihenfusion liefert. Um Bewegungen in einer Belichtungsreihe zu handhaben, präsentieren wir folgenden Zweischrittansatz: Zuerst stellen wir die komplette Rangtransformation vor, um eine optische Flussmethode zu entwerfen, die robust gegenüber starken Beleuchtungsänderungen ist. Dann eliminieren wir verbleibende Registrierungsfehler mit der Helligkeitstransferfunktion, welche die Helligkeitswerte zwischen Bildern in Beziehung setzt. Zusätzliches Wissen über die Belichtungsreihe ermöglicht uns, die erste vollständig gekoppelte Methode vorzustellen, die gemeinsam ein registriertes Hochkontrastbild sowie dichte Bewegungsfelder berechnet. Final präsentieren wir eine Technik, die von unterschiedlich fokussierten Bildern Tiefeninformation ableitet. In diesem Kontext stellen wir zusätzlich einen neuen Regularisierer zweiter Ordnung vor, der sich der Bildstruktur anisotrop anpasst

Improving optical access, sampling speed, and resolution for in vivo multiphoton microscopy

Multiphoton microscopy is a powerful optical imaging modality renowned for its non-invasive nature and relatively affordable characteristics. In particular, it has found its niche in neuroimaging due to its ability to probe in vivo biological processes in scattering brain tissue approaching millimeter depths with cellular resolution. However, the brain is a large and complex organ, and in order to fully understand its heterogeneous architecture and associated functional roles, several distal regions must be imaged simultaneously. Moreover, due to the critical implications of organelle features in various macroscale processes, whole-brain imaging at subcellular resolution scales presents itself as one of the outstanding challenges faced by the neuroscientific community today. Primarily, this research aims to expand the depth, field-of-view, and temporal throughput of multiphoton microscopy to enable large volume imaging of microvasculature at greater acquisition speeds. To accomplish this, we combine multi-faceted efforts focused on the engineering and development of advanced multiphoton microscopy techniques and technologies. This includes the characterization of novel contrast agents, the optimization of scan system optics, and the integration of high-repetition rate lasers with a resonant galvanometer. In addition, we develop a two-color imaging system capable of enhancing excitation efficiency, improving signal-to- background ratio, and further extending imaging depth. Finally, we present a novel application for two-color non-degenerate mode mixing to effectively circumvent the diffraction-limited nature of optical resolution and enable subcellular imaging. Collectively, these efforts advance the state-of-the art of multiphoton microscopy for routine cerebrovascular and neuroimaging.Biomedical Engineerin