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dc.contributor.advisorJaya Prakash
dc.contributor.authorArumugaraj, M
dc.date.accessioned2022-11-11T09:04:05Z
dc.date.available2022-11-11T09:04:05Z
dc.date.submitted2022
dc.identifier.urihttps://etd.iisc.ac.in/handle/2005/5905
dc.description.abstractPhotoacoustic imaging (PAI) employed the special properties of light or photons to obtain detailed images of organs, tissues, cells, and even molecules. The method allowed for a non-invasive or minimally invasive examination within the body. The PAI used nearinfrared light (600 nm - 900 nm) as the scan media, which had the additional benefit of being a non-ionizing imaging modality. The PAI can be integrated with other imaging modalities, such as MRI or X-ray, to provide better information for complex diseases or researchers working on complex experiments. Photoacoustic imaging has already been widely used in pre-clinical research to image small animals. Although PAI is a multi-scale modality, it is challenging to use for clinical research and interventional applications due to the non-linear distribution of optical fluence. Quantitative Photoacoustic Imaging (QPAI) has remained problematic due to the influence of non-linear optical fluence distribution, which influences photoacoustic image representation. Non-linear optical fluence correction in PA imaging was highly ill-posed, leading to inaccurate recovery of optical absorption maps. Note that the traditional optical fluence correction method needs precise estimation of optical fluence map. Many different light transport models exist for estimating the optical fluence map when the optical properties, i.e., optical absorption and optical scattering are known. However, in reality the optical properties are unknown in advance, therefore fluence estimation becomes difficult during PA imaging. Moreover, optical light illumination at the target medium under the study is not uniform over the wavelength, the target medium introduce spectral distortion between the measured PA spectrum and the true target spectrum. Consequently, for true QPAI, the optical fluence must be simultaneously estimated and compensated. This requires not only an appropriate fluence model, but also an effective method to estimate the fluence distribution at each wavelength from PA measurements. Based on prior knowledge of the target medium’s optical properties, many different methods have been proposed for fluence compensation for a simple and homogeneous medium. Unfortunately, none translate into clinical usage. To translate to clinical usage, more complex and heterogeneous media need to be studied. And also the generated PA signal may also change dynamically based on the background tissue properties. Hence, consider complex, foreground and background non-homogeneity of the medium for accurate recovery of optical absorption coefficient. None of the research groups adapted all the above factors simultaneously for fluence compensation. This thesis study developed a deep learning-based optical fluence correction approach to solving this limitation. The main objective of this thesis was to investigate the non-linear distribution of optical fluence effect in 2D and 3D medium and compensate this effect by using deep learning (DL) models. This thesis explains the recovery of the optical absorption maps using deep learning approaches by correcting the fluence effect. In this thesis, different deep learning models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a non-homogeneous foreground and background medium. Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the non-linear optical fluence distribution. The trained deep learning models like U-Net, FD U-Net, Y-Net, FD Y-Net, Deep ResUnet, and GAN were tested to evaluate the performance of optical absorption coefficient recovery with in-silico and in-vivo dataset. The results indicated that DL-based deconvolution improves the reconstructed PAI in terms of PSNR and SSIM. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction and also able to compensate for nonlinear optical fluence distribution more effectively and improve the photoacoustic image quality.en_US
dc.language.isoen_USen_US
dc.rightsI grant Indian Institute of Science the right to archive and to make available my thesis or dissertation in whole or in part in all forms of media, now hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertationen_US
dc.subjectOptoacoustic Imagingen_US
dc.subjectoptical fluence
dc.subjectK-Wave
dc.subjectNIRFAST
dc.subjectDeep learning
dc.subject.classificationPhotoacoustic imagingen_US
dc.subject.classificationResearch Subject Categories::TECHNOLOGY::Information technology
dc.titleDeep learning methods for light fluence compensation in two-dimensional and three-dimensional photoacoustic imagingen_US
dc.typeThesisen_US
dc.degree.nameMTech (Res)en_US
dc.degree.levelMastersen_US
dc.degree.grantorIndian Institute of Scienceen_US
dc.degree.disciplineEngineeringen_US


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