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On Hallucinations in Tomographic Image reconstruction

IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第11期40卷 3249-3260页

作者： Bhadra, Sayantan Kelkar, Varun A. Brooks, Frank J. Anastasio, Mark A. Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Dept Bioengn Urbana IL 61801 USA

Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object properties from training images. However, an analysis of the prior information learned by these deep networks and their ability to generalize to data that may lie outside the training distribution is still being explored. An inaccurate prior might lead to false structures being hallucinated in the reconstructed image and that is a cause for serious concern in medical imaging. In this work, we propose to illustrate the effect of the prior imposed by a reconstruction method by decomposing the image estimate into generalized measurement and null components. The concept of a hallucination map is introduced for the general purpose of understanding the effect of the prior in regularized reconstruction methods. Numerical studies are conducted corresponding to a stylized tomographic imaging modality. The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.

关键词： Image reconstruction Imaging reconstruction algorithms Noise measurement Training Null space Superresolution Tomographic image reconstruction image quality assessment deep learning hallucinations

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A Compressed Sensing Network for Acquiring Human Pressure Information

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第2期14卷 388-402页

作者： Han, Tao Hao, Kuangrong Tang, Xue-Song Cai, Xin Wang, Tong Liu, Xiaoyan Donghua Univ Coll Informat Sci & Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China

In this article, sparse autoencoder in deep learning is integrated into the compressed sensing (CS) theory, and a reconstruction algorithm is designed based on the biological mechanism of human brain synaptic connections. The compressive sampling process is modeled as a neural network model. Then a biological mechanism-inspired stacked long short-term memory (LSTM) network model is proposed as a reconstruction algorithm of CS theory. Consequently, a CS network (ComsensNet) model is introduced, by integrating the compressive sampling process and reconstruction algorithm. ComsensNet can provide a bridge between sparse autoencoder in deep learning, synapses in human brain neurons and the CS theory. A deep neural network is designed based on the synaptic biological mechanism of human brain neurons, and then combine with the theory of CS. The effectiveness of ComsensNet is investigated by using acquired pressure data from the human body model. The experimental results demonstrate that the biological mechanism-inspired stacked LSTM network in ComsensNet can improve the reconstruction accuracy compared to other reconstruction algorithms.

关键词： reconstruction algorithms Logic gates Image reconstruction Sparse matrices Deep learning Compressed sensing Biological system modeling Biological mechanism compressed sensing (CS) deep learning human body long short-term memory (LSTM) neural network pressure array sparse autoencoder

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Enhanced image reconstruction of Fourier ptychographic microscopy with double-height illumination

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OPTICS EXPRESS 2021年第25期29卷 41655-41669页

作者： Wang, Dayong Han, Yaqian Zhao, Jie Rong, Lu Wang, Yunxin Lin, Shufeng Beijing Univ Technol Coll Phys & Optoelect Fac Sci 100 Ping Le Yuan Beijing 100124 Peoples R China Beijing Engn Res Ctr Precis Measurement Technol & 100 Ping Le Yuan Beijing 100124 Peoples R China

Fourier ptychographic microscopy (FPM) is a recently developed computational microscopy approach that can produce both wide field-of-view and high-resolution intensity and phase image of the sample. Traditionally, a periodic LED array is used as the illumination source. The frequency spectrum is a grid structure that is sparse in the low-middle frequency region and dense in the high-frequency region. However, for most biological samples, the useful information is mostly concentrated in the low-middle frequency region. So the traditional sampling method may lead to distortion in the reconstructed images because of insufficient sampling. Therefore, we propose a convenient and feasible method to continuously improve the sampling conditions without increasing the LED units and changing the pitch, termed double-height illumination FPM. The LED array is moved from one plane to another to achieve the sample illuminations at two different heights and the low-resolution intensity images are totally utilized to reconstruct the high-resolution complex amplitude image. Both simulation results and experimental results illustrate that the proposed method not only improves the reconstruction accuracy and efficiency, but also has strong robustness under noisy conditions. Furthermore, the rule of choosing the two illumination heights is discussed, and the physical insight of the proposed method is analyzed from the view of the data redundancy. In addition, a wave vector correction method is employed to reduce the artifacts of the reconstructed image at the edge of the large full field of view. (C) 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Image quality Image reconstruction Imaging systems LED lighting Phase retrieval reconstruction algorithms

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Low-Complexity and Low-Latency SVC Decoding Architecture Using Modified MAP-SP Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第4期69卷 1774-1787页

作者： Hong, Seungwoo Kam, Dongyun Yun, Sangbu Choe, Jeongwon Lee, Namyoon Lee, Youngjoo Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea

The compressive sensing (CS) based sparse vector coding (SVC) method is one of the promising ways for the next-generation ultra-reliable and low-latency communications. In this paper, we present advanced algorithm-hardware co-optimization schemes for realizing a cost-effective SVC decoding architecture. The previous maximum a posteriori subspace pursuit (MAP-SP) algorithm is newly modified to relax the computational overheads by applying novel residual forwarding and LLR approximation schemes. A fully-pipelined parallel hardware is also developed to support the modified decoding algorithm, reducing the overall processing latency, especially at the support identification step. In addition, an advanced least-square-problem solver is presented by utilizing the parallel Cholesky decomposer design, further reducing the decoding latency with parallel updates of support values. The implementation results from a 22nm FinFET technology showed that the fully-optimized design is 9.6 times faster while improving the area efficiency by 12 times compared to the baseline realization.

关键词： Static VAr compensators Decoding Computer architecture reconstruction algorithms Matching pursuit algorithms Encoding Ultra reliable low latency communication Compressive sensing subspace pursuit parallel architecture ultra reliable and low latency communications

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Parallel data acquisition and reconstruction method of near-field ptychography for large samples

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OPTICS EXPRESS 2021年第26期29卷 43342-43352页

作者： Lin, Huixiang Xu, Wenhui Zhang, Fucai Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

Near-field ptychography is an attractive modality of coherent diffraction imaging, which can provide quantitative phase of samples at sub-pixel resolution and shows lowrequirement on beam coherence and detector dynamic range. In the case of studying extensive samples, a large dataset would be recorded, resulting in a long data acquisition time and high requirements for computer memory and computing power. Here, we proposed a simple experimental arrangement for parallel data acquisition and the corresponding image reconstruction algorithm. The scheme can dramatically increase the overall imaging speed. The algorithm can be efficiently implemented on graphic processing units (GPUs). The feasibility and effectiveness of the method have been validated with numerical simulation and optical experiments. The proposed approach would be helpful for imaging using large-array cameras. (C) 2021 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Image processing Image quality Image reconstruction Numerical simulation Phase imaging reconstruction algorithms

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Phase Current reconstruction With Dual-Sensor for Switched Reluctance Motor Drive System

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IEEE ACCESS 2021年 9卷 114095-114103页

作者： Cheng, He Mi, Shuai Wang, Zelu Shao, Dali Wu, Haonan Yan, Wenju Yu, Dongsheng China Univ Min & Technol Sch Elect & Power Engn Xuzhou 221116 Jiangsu Peoples R China

Due to the rugged construction, high starting torque, wide speed range, inherent fault-tolerance and high operating efficiency, switched reluctance motors (SRMs) have been used in many fields such as household appliances, electric vehicles and industrial drives. At low and medium speeds, the current chopping control (CCC) are usually used for SRMs. The current sensors must be connected in series with each phase winding for detecting the phase current, and the number of sensors is usually equal to the number of motor phases. In order to reduce the number of current sensors and the cost of the drive system, this paper propose a general phase current reconstruction method for SRM drive system with two current sensors. Firstly, the excitation, freewheeling and demagnetization circuits of the asymmetric half-bridge power converter are separated into two different paths. The sensors placement method, winding connection method and calculation formula of each phase current are introduced in detail. Then the influence of the turn-off angles on the overlap interval of each phase current is analyzed, and the proposed reconstruction method is further extended to multi-phase SRM. This method can simplify the current decoupling process and is easy to implement without changing the asymmetric half-bridge topology and operating states. Finally, the feasibility of the proposed phase current reconstruction algorithm is verified by simulation and experiment.

关键词： Sensors Inductance Rotors Windings reconstruction algorithms Electron tubes Switched reluctance motors Switched reluctance motor phase current reconstruction two current sensors

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Multiple-scattering suppressive refractive index tomography for the label-free quantitative assessment of multicellular spheroids

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BIOMEDICAL OPTICS EXPRESS 2022年第2期13卷 962-979页

作者： Yasuhiko, Osamu Takeuchi, Kozo Yamada, Hidenao Ueda, Yukio Hamamatsu Photon KK Cent Res Lab Hamakita Ku Shizuoka Shizuoka 4348601 Japan

Refractive index (RI) tomography is a quantitative tomographic technique used to visualize the intrinsic contrast of unlabeled biological samples. Conventional RI reconstruction algorithms are based on weak-scattering approximation, such as the Born or Rytov approximation. Although these linear algorithms are computationally efficient, they are invalid when the fields are strongly distorted by multiple scattering (MS) of specimens. Herein, we propose an approach to reconstruct the RI distributions of MS objects even under weak-scattering approximation using an MS-suppressive operation. The operation converts the distorted fields into MS-suppressed fields, where weak-scattering approximation is applicable. Using this approach, we reconstructed a whole multicellular spheroid and successfully visualized its internal subcellular structures. Our work facilitates the realization of RI tomography of MS specimens and label-free quantitative analysis of 3D multicellular specimens. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Adaptive optics Heterodyne detection Image quality Multiple scattering Optical imaging reconstruction algorithms

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Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

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IEEE ACCESS 2022年 10卷 85002-85018页

作者： Ahmed, Irfan Khalil, Amaad Ahmed, Ishtiaque Frnda, Jaroslav Univ Engn & Technol Peshawar Dept Elect Engn Jalozai Campus Peshawar 25000 Pakistan Univ Engn & Technol Peshawar Dept Comp Syst Engn Peshawar 25000 Pakistan Univ Engn & Technol Peshawar Natl Ctr Big Data & Cloud Comp NCBC Peshawar 25000 Pakistan Univ Zilina Fac Operat & Econ Transport & Commun Dept Quantitat Methods & Econ Informat Zilina 01026 Slovakia VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Telecommun Ostrava 70800 Czech Republic

Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of application areas. The use of an efficient sampling matrix for high-performance recovery algorithms improves the performance of the compressive sensing framework significantly. This paper presents the underlying concepts of compressive sensing as well as previous work done in targeted domains in accordance with the various application areas. To develop prospects within the available functional blocks of compressive sensing frameworks, a diverse range of application areas are investigated. The three fundamental elements of a compressive sensing framework (signal sparsity, subsampling, and reconstruction) are thoroughly reviewed in this work by becoming acquainted with the key research gaps previously identified by the research community. Similarly, the basic mathematical formulation is used to outline some primary performance evaluation metrics for 1D and 2D compressive sensing.

关键词： Compressed sensing Transforms Sparse matrices Sensors Signal representation Matching pursuit algorithms Compressed sensing compressive sampling reconstruction algorithms sensing matrix

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Impact of skin tone on photoacoustic oximetry and tools to minimize bias

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BIOMEDICAL OPTICS EXPRESS 2022年第2期13卷 875-887页

作者： Mantri, Yash Jokerst, Jesse, V Univ Calif San Diego Dept Bioengn 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif San Diego Dept Nanoengn 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif San Diego Mat Sci Dept 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif San Diego Dept Radiol 9500 Gilman Dr La Jolla CA 92093 USA

The major optical absorbers in tissue are melanin and oxy/deoxy-hemoglobin, but the impact of skin tone and pigmentation on biomedical optics is still not completely understood or adequately addressed. Melanin largely governs skin tone with higher melanin concentration in subjects with darker skin tones. Recently, there has been extensive debate on the bias of pulse oximeters when used with darker subjects. Photoacoustic (PA) imaging can measure oxygen saturation similarly as pulse oximeters and could have value in studying this bias. More importantly, it can deconvolute the signal from the skin and underlying tissue. Here, we studied the impact of skin tone on PA signal generation, depth penetration, and oximetry. Our results show that subjects with darker skin tones exhibit significantly higher PA signal at the skin surface, reduced penetration depth, and lower oxygen saturation compared to subjects with lighter skin tones. We then suggest a simple way to compensate for these signal differences.

关键词： Absorption coefficient Optical imaging reconstruction algorithms Reflectance spectroscopy Skin Ultraviolet radiation

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A Feature Extraction Algorithm for Rolling Bearing Faults and Its Application

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IEEE ACCESS 2022年 10卷 83498-83506页

作者： Zhang, Zhen Liu, Baoguo Du, Wenliao Feng, Wei Henan Univ Technol Sch Mech & Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Light Ind Sch Mech & Elect Engn Zhengzhou 450002 Peoples R China

Focusing on the difficulty of completely extracting the surface damage caused by rolling bearing lubrication failure, an algorithm for extracting bearing lubrication fault is proposed, which is based on periodic optimum singular value decomposition (O-SVD) cascaded fast spectral correlation (FSC). Initially, conventional T-SVD with energy leakage defects was modified into O-SVD, which was used as the preprocessing unit for signal processing. Then, FSC calculation was performed on the reconstructed signals, eventually obtaining enhanced envelope spectrum with obvious features that could well preserve local details. Simulation and experimental results show that the proposed algorithm allows rather complete extraction of slight fault features resulting from poor lubrication under small sampling length and low signal-to-noise ratio (SNR), and has good applicability in extracting compound and composite fault features. The extracted signals have advantages over existing algorithms regarding fault resolution and local details preservation.

关键词： Power capacitors Lubrication Feature extraction Signal resolution Signal processing algorithms Spectral estimation reconstruction algorithms Rolling bearing singular value decomposition fast spectral correlation slight fault feature extraction underlubrication fault

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