Clinical optical diagnostics – Status and perspectives

Johansson, Ann; Kromer, Katharina; Sroka, Ronald; Stepp, Herbert

doi:10.1016/j.mla.2008.08.002

Cited by 47 publications

(21 citation statements)

References 123 publications

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“…More recently, great efforts have been made to image nontransparent tissues, thus enabling OCT to be applied in a wide range of medical specialities such as urology, dermatology, and gynecology [19][20][21][22]. In non-transparent tissues, the imaging depth is limited to approximately 2 mm due to optical attenuation from tissue scattering and absorption.…”

mentioning

confidence: 99%

Intraoperative optical coherence tomography imaging to identify parathyroid glands

et al. 2014

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OCT is capable of distinguishing between parathyroid, thyroid, and adipose tissue. An accurate differentiation between parathyroid tissue and lymph nodes was not possible. The disappointing results compared to the previous ex vivo study are related to problems handling the endoscopic probe intraoperatively. However, further refinement of this new technology may lead to OCT systems with higher resolution and intraoperative probes that are easier to handle.

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confidence: 99%

Intraoperative optical coherence tomography imaging to identify parathyroid glands

et al. 2014

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“…Photons traveling through tissue and interacting with tissue components, which including absorption, emission, scattering and fluorescence form the basis of optical imaging techniques. A key challenge for optical imaging probes and instrumentation, particularly those targeted toward eventual clinical applications, is to overcome the attenuation and scattering of light by tissues [23][24][25] . Recently, photoacoustic tomography, ultrasound-modulated optical tomography and near-infrared fluorescence (NIRF) imaging have been successfully developed to image the suspected lesion in deeper tissues [26,27] .…”

Section: The Optical Imaging and Microscopymentioning

confidence: 99%

Recent progress in medical photonics

Xie

Hui

2009

Sci. China Ser. G-Phys. Mech. Astron.

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The field of medical photonics is rapidly expanding, and a wide variety of optical technologies and instruments have recently been developed for diagnostic, therapeutic and basic science applications in medicine. This review presents the recent advances and application of medical photonics, and the obtained results from our laboratory are highlighted. Finally, the challenges and future prospects for the transition from technological exploration to clinical studies are discussed.

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“…Autofluorescence imaging is receiving an increasing attention in an examining the functional and structural properties of the live cells [1]. This approach utilizes fluorescence of compounds containing intrinsic fluorophores that are present in a varying extent in almost every live organism.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, autofluorescence was rather considered as undesirable artifact distorting imaging of the live cells, particularly in the case of imaging based on the fluorescence labeling. However, the recent advances in the imaging techniques and continuously growing knowledge about molecular composition of the cells and tissues rapidly change the rank of autofluorescence detection between the diagnostics tools used in life science [1].…”

Section: Introductionmentioning

confidence: 99%

Analysis of spectrally resolved autofluorescence images by support vector machines

2013

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Spectral analysis of the autofluorescence images of isolated cardiac cells was performed to evaluate and to classify the metabolic state of the cells in respect to the responses to metabolic modulators. The classification was done using machine learning approach based on support vector machine with the set of the automatically calculated features from recorded spectral profile of spectral autofluorescence images. This classification method was compared with the classical approach where the individual spectral components contributing to cell autofluorescence were estimated by spectral analysis, namely by blind source separation using non-negative matrix factorization. Comparison of both methods showed that machine learning can effectively classify the spectrally resolved autofluorescence images without the need of detailed knowledge about the sources of autofluorescence and their spectral properties.

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Clinical optical diagnostics – Status and perspectives

Cited by 47 publications

References 123 publications

Intraoperative optical coherence tomography imaging to identify parathyroid glands

Intraoperative optical coherence tomography imaging to identify parathyroid glands

Recent progress in medical photonics

Analysis of spectrally resolved autofluorescence images by support vector machines

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