2,030 research outputs found
Image Enhancement and Noise Reduction Using Modified Delay-Multiply-and-Sum Beamformer: Application to Medical Photoacoustic Imaging
Photoacoustic imaging (PAI) is an emerging biomedical imaging modality
capable of providing both high contrast and high resolution of optical and
UltraSound (US) imaging. When a short duration laser pulse illuminates the
tissue as a target of imaging, tissue induces US waves and detected waves can
be used to reconstruct optical absorption distribution. Since receiving part of
PA consists of US waves, a large number of beamforming algorithms in US imaging
can be applied on PA imaging. Delay-and-Sum (DAS) is the most common
beamforming algorithm in US imaging. However, make use of DAS beamformer leads
to low resolution images and large scale of off-axis signals contribution. To
address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS),
which was used as a reconstruction algorithm in confocal microwave imaging for
breast cancer detection, was introduced for US imaging. Consequently, DMAS was
used in PA imaging systems and it was shown this algorithm results in
resolution enhancement and sidelobe degrading. However, in presence of high
level of noise the reconstructed image still suffers from high contribution of
noise. In this paper, a modified version of DMAS beamforming algorithm is
proposed based on DAS inside DMAS formula expansion. The quantitative and
qualitative results show that proposed method results in more noise reduction
and resolution enhancement in expense of contrast degrading. For the
simulation, two-point target, along with lateral variation in two depths of
imaging are employed and it is evaluated under high level of noise in imaging
medium. Proposed algorithm in compare to DMAS, results in reduction of lateral
valley for about 19 dB followed by more distinguished two-point target.
Moreover, levels of sidelobe are reduced for about 25 dB.Comment: This paper was accepted and presented at Iranian Conference on
Electrical Engineering (ICEE) 201
Beyond backscattering: Optical neuroimaging by BRAD
Optical coherence tomography (OCT) is a powerful technology for rapid
volumetric imaging in biomedicine. The bright field imaging approach of
conventional OCT systems is based on the detection of directly backscattered
light, thereby waiving the wealth of information contained in the angular
scattering distribution. Here we demonstrate that the unique features of
few-mode fibers (FMF) enable simultaneous bright and dark field (BRAD) imaging
for OCT. As backscattered light is picked up by the different modes of a FMF
depending upon the angular scattering pattern, we obtain access to the
directional scattering signatures of different tissues by decoupling
illumination and detection paths. We exploit the distinct modal propagation
properties of the FMF in concert with the long coherence lengths provided by
modern wavelength-swept lasers to achieve multiplexing of the different modal
responses into a combined OCT tomogram. We demonstrate BRAD sensing for
distinguishing differently sized microparticles and showcase the performance of
BRAD-OCT imaging with enhanced contrast for ex vivo tumorous tissue in
glioblastoma and neuritic plaques in Alzheimer's disease
Optical Coherence Tomography and Its Non-medical Applications
Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications
Development, Optimization and Clinical Evaluation Of Algorithms For Ultrasound Data Analysis Used In Selected Medical Applications.
The assessment of soft and hard tissues is critical when selecting appropriate protocols for restorative and regenerative therapy in the field of dental surgery. The chosen treatment methodology will have significant ramifications on healing time, success rate and overall long-time oral health. Currently used diagnostic methods are limited to visual and invasive assessments; they are often user-dependent, inaccurate and result in misinterpretation. As such, the clinical need has been identified for objective tissue characterization, and the proposed novel ultrasound-based approach was designed to address the identified need. The device prototype consists of a miniaturized probe with a specifically designed ultrasonic transducer, electronics responsible for signal generation and acquisition, as well as an optimized signal processing algorithm required for data analysis. An algorithm where signals are being processed and features extracted in real-time has been implemented and studied. An in-depth algorithm performance study has been presented on synthetic signals. Further, in-vitro laboratory experiments were performed using the developed device with the algorithm implemented in software on animal-based samples. Results validated the capabilities of the new system to reproduce gingival assessment rapidly and effectively. The developed device has met clinical usability requirements for effectiveness and performance
Image Fusion via Sparse Regularization with Non-Convex Penalties
The L1 norm regularized least squares method is often used for finding sparse
approximate solutions and is widely used in 1-D signal restoration. Basis
pursuit denoising (BPD) performs noise reduction in this way. However, the
shortcoming of using L1 norm regularization is the underestimation of the true
solution. Recently, a class of non-convex penalties have been proposed to
improve this situation. This kind of penalty function is non-convex itself, but
preserves the convexity property of the whole cost function. This approach has
been confirmed to offer good performance in 1-D signal denoising. This paper
demonstrates the aforementioned method to 2-D signals (images) and applies it
to multisensor image fusion. The problem is posed as an inverse one and a
corresponding cost function is judiciously designed to include two data
attachment terms. The whole cost function is proved to be convex upon suitably
choosing the non-convex penalty, so that the cost function minimization can be
tackled by convex optimization approaches, which comprise simple computations.
The performance of the proposed method is benchmarked against a number of
state-of-the-art image fusion techniques and superior performance is
demonstrated both visually and in terms of various assessment measures
Methods and Systems for Realizing High Resolution Three-Dimensional Optical Imaging
Methods and systems for realizing high resolution three-dimensional (3-D) optical imaging using diffraction limited low\u27 resolution optical signals. Using axial shift-based signal processing via computer based computation algorithm, three sets of high resolution optical data are detennined along the axial (or light beam propagation) direction using low resolution axial data. The three sets of low resolution data are generated by illuminating the 3~D object under observation along its three independent and orthogonal look directions (i.e., x. Y. and z) or by physically rotating the object by 90 degrees and also flipping the object by 90 degrees. The three sets of high resolution axial data is combined using a unique mathematical function to interpolate a 3-D image of the test object that is of much higher resolution than the diffiaction limited direct measurement 3-D resolution. Confocal microscopy or optical coherence tomography (OCT) are example methods to obtain the axial scan data sets
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