No description available.

No description available.

Complexes of the radionuclides (^67)Ga and (^III)In, or of the paramagnetic contrast agent Gd, used in MRI, provide a means of imaging tumours. The stability of the (^71)Ga- NOTA complex was verified by in vivo NMR spectroscopy. The novel phosphinic acid NOTA analogue, bearing an isopropyl substituent on phosphorus was prepared and its lipophilicity and (^III)In biodistribution in mice determined. The crystal structure of the yttrium complex of N,N"-bis(benzylcarbamoylmethyl)diethylenetriamine-N,N',N"-triacetic acid revealed amide carbonyl ligation in a distorted mono-capped square antiprismatic structure, with one metal-bound water. The biodistribution of the analogous Gd complex was examined. A novel series of 9N3 based ligands incorporating three further N donor atoms, carboxymethyl groups and a potentially larger cavity size were synthesised. The analogous series containing phosphinic acid groups and the 12N3 counterparts were also prepared. The former series formed complexes with Gd and the biodistribution in mice was studied. The 12N3 analogues failed to form Gd complexes.2-Nitroimidazoles are known to selectively target hypoxic tumour tissue. Two conjugates of 2-nitroimidazole for tumour imaging were prepared, the Gd complex of a DTPA-bis(2-nitroimidazole) amide and the (^III)In complex of a C-functionalised NOTA- nitroimidazole conjugate. The biodistribution in mice of each was studied and luminescence experiments on the Tb complex of the former revealed one metal bound water molecule. Novel conjugates of the tetrapeptide tuftsin and a complexing agent based on the 12N4 skeleton and an N-linked NOTA derivative were synthesised. Biodistributions of the Gd and In complexes respectively are being carried out. Acridine intercalators reversibly bind DNA, possibly enhancing the effectiveness of tumour targeting conjugates. Novel multifunctionally labelled acridines based on tris(2- aminoethyl) amine were sought. The p-nitrophenolate active ester of 9-acridine carbamoyl-2-(2-aminoethyl)-2-methyl amine was also prepared as a versatile agent for acridine labelling.

<p dir="ltr">Advancements in machine learning are narrowing the gap in visual capabilities between machines and healthcare professionals, resulting in a transformation of the way we understand and address health challenges. Despite these advances, underlying limitations persist in addressing real-world problems, particularly in the precise capture of biological and physiological information. This is primarily because traditional trichromatic cameras fall short of representing reflectance spectra due to their limited spectral information. To overcome these limitations, hyperspectral imaging has emerged as a powerful tool for biomedical applications. By collecting a wealth of information at different wavelengths, hyperspectral imaging provides a comprehensive view of electromagnetic spectra, allowing non-invasive clinical analysis for accurate diagnostics. Snapshot hyperspectral imaging, in particular, is a competitive alternative to traditional cameras as it can capture a hyperspectral image in a single shot without the need for scanning individual wavelengths. Here, we introduce a computational snapshot hyperspectral imaging method, achieved through the integration of a machine learning approach with a streamlined optical system. We design an explainable machine learning algorithm by incorporating optical and biological knowledge into the algorithm. Therefore, the algorithm can reconstruct hyperspectral images with high spectralspatial resolution comparable to those of scientific spectrometers, despite the use of sparse information captured from the optical system. To demonstrate its versatility in biomedical applications, we extract hemodynamic parameters of peripheral microcirculation from embryonic model systems, tissue phantom samples, and human conjunctivas. Furthermore, we validate high accuracy of the results using conventional hyperspectral imaging and functional near-infrared spectroscopy. This learning-powered imaging method, characterized by high resolution and simplified hardware requirements, has the potential to offer solutions for various biomedical challenges by surpassing the constraints of conventional cameras and hyperspectral imaging.</p>

No description available.

In the medical field, the detection and diagnosis of diseases continue to improve. Developments in diagnostic techniques have helped to improve treatment in the early stages and avoid many risks to patients. One relatively new diagnostic technique is optical coherence tomography (OCT), which is used in many medical applications to perform internal microstructure imaging of the human body at high resolution (typically 10 micro metre), at high speed and in real time. OCT is non-invasive and can be used as a contact or non-contact technique to obtain an image. In medicine, there are many applications that involve OCT, such as in ophthalmology, gastroenterology, cardiology and oncology. This work demonstrates the design, development and implementation of a high resolution swept laser OCT system for the imaging and diagnosis of tissues in laboratory and clinical experiments. It reports an investigation to measure the thickness of the peritoneal membrane and the use of optical imaging contrast agents such as gold nanorods. There is also an account of the design of an endoscope-catheter fast scanning OCT system for biomedical studies of the gastrointestinal tract and gynaecological areas. These results were achieved by using a swept tuneable laser source with a very high tuning speed of 16 kHz over a wide range of wavelengths: 1260 nm to 1390 nm. The laser sweeps across 110 nm at a 16 kHz repetition rate. The real axial line speed is limited by the source that is used in the OCT system. The axial resolution of the system is 7 µm and its transverse resolution is 15 µm. The bandwidth of the source is up to DeltaGamma = 110 nm, centred at Gamma0 = 1325 nm, and the coherent length is 7 µm. On the sample arm of the interferometer, the swept laser OCT technique is combined with an optical probe and endoscope in order to develop a novel diagnostic imaging device to visualize tissue in vivo for animal and human experimental trials.

No description available.

Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.

Based on a study of existing solar filament and tracking methods, a fully automated solar filament detection and tracking method is presented. An adaptive thresholding technique is used in a segmentation phase to identify candidate filament pixels. This phase is followed by retrieving the actual filament area from a region grown filament by using statistical parameters and morphological operations. This detection technique gives the opportunity to develop an accurate spine extraction algorithm. Features including separation distance, orientation and average intensities are extracted and fed to a Neural Network (NN) classifier to merge broken filament components. Finally, the results for two consecutive images are compared to detect filament disappearance events, taking advantage of the maps resulting from converting solar images to Heliographic Carrington co-ordinates. The study has demonstrated the novelty of the algorithms developed in terms of them now all being fully automated; significantly the algorithms do not require any empirical values to be used whatsoever unlike previous techniques. This combination of features gives the opportunity for these methods to work in real-time. Comparisons with other researchers shows that the present algorithms represent the filaments more accurately and evaluate computationally faster - which could lead to a more precise tracking practice in real-time. An additional development phase developed in this dissertation in the process of detecting solar filaments is the detection of filament disappearances. Some filaments and prominences end their life with eruptions. When this occurs, they disappear from the surface of the Sun within a few hours. Such events are known as disappearing filaments and it is thought that they are associated with coronal mass ejections (CMEs). Filament disappearances are generally monitored by observing and analysing successive solar H-alpha images. After filament regions are obtained from individual H-alpha images, a NN classifier is used to categorize the detected filaments as Disappeared Filaments (DFs) or Miss-Detected Filaments (MDFs). Features such as Area, Length, Mean, Standard Deviation, Skewness and Kurtosis are extracted and fed to this neural network which achieves a confidence level of at least 80%. Comparing the results with other researchers shows high divergence between the results. The NN method shows better convergence with the results of the National Geophysical Data Centre (NGDC) than the results of the others researchers.

Optical imaging techniques have improved much over the last fifty years since the invention of the laser. With a high brightness source many imaging applications which were once inaccessible to researchers have now become a reality. Among these techniques, the most beneficial one is the use of lasers for both wide-field and confocal imaging systems. The aim of this study was to design a laser imaging system based on the concept of laser scanning confocal microscopy. Specifically the optical system was based on optical fibers allowing the user to image remote areas such as the inner surface of rifled gun barrels and/or pipes with a high degree of precision (+/- 0.01 mm). In order to build such a system, initially the theoretical foundation for a confocal as well as a wide-field imaging system was analyzed. Using this basis a free-space optical confocal system was built and analyzed. The measurements support the fact that both the objective numerical aperture and pinhole size play an important role in the radial and axial resolution of the system as well as the quality of the images obtained. To begin construction of a confocal, optical-fiber based imaging system first an all fiber wide-field imaging system was designed and tested at a working wavelength of 1550 nm. Then an all fiber confocal system was designed at a working wavelength of 808 nm. In both cases results showed that while lateral resolution was adequate, axial resolution suffered since it was found that the design of the optical system needs to take into account under-filling of the objective lens, a result common with the use of laser beams whose divergence is not at all like that of a point source. The work done here will aid technology that will be used in the elimination process of faulty rifling fabrication in defense industry. The reason why the confocal technique is preferred to the conventional wide-field one is the need for better resolution in all directions. Theoretical concepts and mathematical background are discussed as well as the experimental results and the practical advantages of such a system.