scholarly journals Image Deconvolution with Hybrid Reweighted Adaptive Total Variation (HRATV) for Optoacoustic Tomography

Photonics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 25
Author(s):  
Chen Yang ◽  
Yang Jiao ◽  
Xiaohua Jian ◽  
Yaoyao Cui
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Imaging Modality ◽  
Standard Test ◽  
Image Deconvolution ◽  
Low Contrast ◽  
Limited Bandwidth ◽  
Optoacoustic Tomography ◽  
Adaptive Total Variation

Optoacoustic tomography (OAT) is a hybrid biomedical imaging modality that usually employs a transducer array to detect laser-generated ultrasonic signals. The reconstructed image suffers low contrast and degraded resolution due to the limited bandwidth and the spatial directivity of the transducer element. Here, we introduce a modified image deconvolution method with a hybrid reweighted adaptive total variation tailored to improve the image quality of OAT. The effectiveness and the parameter dependency of the proposed method are verified on standard test images. The performance of the proposed method in OAT is then characterized on both simulated phantoms and in vivo mice experiments, which demonstrates that the modified deconvolution algorithm is able to restore the sharp edges and fine details in OAT simultaneously. The signal-to-noise ratios (SNRs) of the target structures in mouse liver and brain were improved by 4.90 and 12.69 dB, respectively. We also investigated the feasibility of using Fourier ring correlation (FRC) as an indicator of the image quality to monitor the deconvolution progress in OAT. Based on the experimental results, a practical guide for image deconvolution in OAT was summarized. We anticipate that the proposed method will be a promising post-processing tool to enhance the visualization of micro-structures in OAT.

scholarly journals Correlation of image quality parameters with tube voltage in X-ray dark-field chest radiography: a phantom study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andreas P. Sauter ◽  
Jana Andrejewski ◽  
Manuela Frank ◽  
Konstantin Willer ◽  
Julia Herzen ◽  
...  
Keyword(s):  
Image Quality ◽  
Imaging Modality ◽  
Signal Strength ◽  
Dark Field ◽  
Tube Voltage ◽  
X Ray ◽  
Dose Area Product ◽  
Dark Field Imaging ◽  
Field Imaging

AbstractGrating-based X-ray dark-field imaging is a novel imaging modality with enormous technical progress during the last years. It enables the detection of microstructure impairment as in the healthy lung a strong dark-field signal is present due to the high number of air-tissue interfaces. Using the experience from setups for animal imaging, first studies with a human cadaver could be performed recently. Subsequently, the first dark-field scanner for in-vivo chest imaging of humans was developed. In the current study, the optimal tube voltage for dark-field radiography of the thorax in this setup was examined using an anthropomorphic chest phantom. Tube voltages of 50–125 kVp were used while maintaining a constant dose-area-product. The resulting dark-field and attenuation radiographs were evaluated in a reader study as well as objectively in terms of contrast-to-noise ratio and signal strength. We found that the optimum tube voltage for dark-field imaging is 70 kVp as here the most favorable combination of image quality, signal strength, and sharpness is present. At this voltage, a high image quality was perceived in the reader study also for attenuation radiographs, which should be sufficient for routine imaging. The results of this study are fundamental for upcoming patient studies with living humans.

scholarly journals Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 570 ◽  
Author(s):  
Xuhui Ye ◽  
Gongping Wu ◽  
Le Huang ◽  
Fei Fan ◽  
Yongxiang Zhang
Keyword(s):  
Image Quality ◽  
Power Transmission ◽  
Low Frequency ◽  
Speckle Noise ◽  
Standard Test ◽  
Low Contrast ◽  
Inspection Robot ◽  
Fuzzy Enhancement ◽  
Enhancement Method ◽  
Frequency Components

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

scholarly journals Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3494
Author(s):  
Yongchae Kim ◽  
Hiroyuki Kudo
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Second Order ◽  
Ct Image ◽  
Low Contrast ◽  
New Class ◽  
Nonlocal Total Variation ◽  
Intensity Changes ◽  
Adequate Image Quality ◽  
The Cost

We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty’s framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes.

scholarly journals Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound

2018 ◽  
Author(s):  
Oren Solomon ◽  
Regev Cohen ◽  
Yi Zhang ◽  
Yi Yang ◽  
He Qiong ◽  
...  
Keyword(s):  
Image Quality ◽  
Network Architecture ◽  
Imaging Modality ◽  
Super Resolution ◽  
Low Rank ◽  
Contrast Enhanced Ultrasound ◽  
Robust Pca ◽  
Deep Network ◽  
Contrast Enhanced

AbstractContrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of a low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

scholarly journals Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yaduan Ruan ◽  
Houzhang Fang ◽  
Qimei Chen
Keyword(s):  
Total Variation ◽  
Spatial Information ◽  
Total Variation Regularization ◽  
Image Deconvolution ◽  
Split Bregman ◽  
Edge Information ◽  
Spatially Adaptive ◽  
Comparative Results ◽  
Simulated Images ◽  
Adaptive Total Variation

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

scholarly journals Morphology of Peripheral Vitreoretinal Interface Abnormalities Imaged with Spectral Domain Optical Coherence Tomography

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Rachel L. Chu ◽  
Nicole A. Pannullo ◽  
Christopher R. Adam ◽  
Mohammad R. Rafieetary ◽  
Eric J. Sigler
Keyword(s):  
Optical Coherence Tomography ◽  
Image Quality ◽  
Imaging Modality ◽  
Structural Features ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Vitreoretinal Interface ◽  
Retinal Breaks ◽  
Sd Oct

The objective of this study is to describe the clinical utility and morphologic characteristics of peripheral vitreoretinal interface abnormalities with spectral domain optical coherence tomography (SD-OCT). A prospective imaging analysis of 43 patients with peripheral vitreoretinal interface abnormalities seen on binocular indirect examination with scleral indentation was done. SD-OCT was evaluated for image quality and structural findings. Laser retinopexy was performed to surround all retinal breaks containing a full-thickness component via SD-OCT. Acceptable image quality for inclusion was obtained in 39/43 (91%) patients. Mean age was 41 ± 22 years, and mean follow-up was 14 ± 1.6 months. Decision to treat was altered following SD-OCT in 5% of the patients. Two cases of previously diagnosed operculated holes were found on SD-OCT to be partial-thickness operculated breaks or focal operculated schisis. Peripheral SD-OCT is a reliable and useful technique to examine the structural features of vitreoretinal interface abnormalities in vivo. This imaging modality is useful in the clinical management of suspected retinal breaks identified with indirect ophthalmoscopy.

A Systematic Review of Double Low-dose CT Pulmonary Angiography in Pulmonary Embolism

2019 ◽  
Vol 15 (5) ◽  
pp. 453-460 ◽  
Author(s):  
Sultan Aldosari ◽  
Zhonghua Sun
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Image Quality ◽  
Radiation Dose ◽  
Contrast Medium ◽  
Low Dose ◽  
Pulmonary Angiography ◽  
Ct Pulmonary Angiography ◽  
Low Contrast ◽  
Low Contrast Medium

Background: The aim of this study is to perform a systematic review of the feasibility and clinical application of double low-dose CT pulmonary angiography (CTPA) in the diagnosis of patients with suspected pulmonary embolism. Discussion: A total of 13 studies were found to meet selection criteria reporting both low radiation dose (70 or 80 kVp versus 100 or 120 kVp) and low contrast medium dose CTPA protocols. Lowdose CTPA resulted in radiation dose reduction from 29.6% to 87.5% in 12 studies (range: 0.4 to 23.5 mSv), while in one study, radiation dose was increased in the dual-energy CT group when compared to the standard 120 kVp group. CTPA with use of low contrast medium volume (range: 20 to 75 ml) was compared to standard CTPA (range: 50 to 101 ml) in 12 studies with reduction between 25 and 67%, while in the remaining study, low iodine concentration was used with 23% dose reduction achieved. Quantitative assessment of image quality (in terms of signal-to-noise ratio and contrast-to-noise ratio) showed that low-dose CTPA was associated with higher, lower and no change in image quality in 3, 3 and 6 studies, respectively when compared to the standard CTPA protocol. The subjective assessment indicated similar image quality in 11 studies between low-dose and standard CTPA groups, and improved image quality in 1 study with low-dose CTPA. Conclusion: This review shows that double low-dose CTPA is feasible in the diagnosis of pulmonary embolism with significant reductions in both radiation and contrast medium doses, without compromising diagnostic image quality.

scholarly journals Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1548 ◽  
Author(s):  
Kyuseok Kim ◽  
Hyun-Woo Jeong ◽  
Youngjin Lee
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Near Infrared ◽  
Imaging System ◽  
Histogram Equalization ◽  
Evaluation Performance ◽  
Visual Evaluation ◽  
Évaluation Performance ◽  
Nir Imaging ◽  
Relative Total Variation

Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

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