Article
Engineering, Multidisciplinary
Sophia B. Coban, William R. B. Lionheart, Philip J. Withers
Summary: The paper discusses the performance assessment of CT reconstruction algorithms and introduces a evaluation technique called physical quantification, which measures the features of the test object to assess the reconstruction efficacy. The study highlights the importance of choosing the optimal reconstruction strategy based on the features extracted from the scan.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Jose J. Calvino, Elena Fernandez, Miguel Lopez-Haro, Juan M. Munoz-Ocana, Antonio M. Rodriguez-Chia
Summary: This paper introduces an l(1)-norm model based on Total Variation Minimization for tomographic reconstruction. The reconstructions produced by this model are more accurate than classical reconstruction models based on the l(2)-norm. The model can be linearized and solved using linear programming, and the dimension of the formulation can be further reduced by exploiting complementary slackness conditions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
A. Duvenage, W. P. du Plessis
Summary: This article introduces the spin-scan tomographic scanning imager and its limitations, and proposes a new tomographic scanning imager based on similar concepts, which can achieve different trade-offs between resolution and frame rate by dynamically changing parameters.
ELECTRONICS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
A. Duvenage, W. P. du Plessis
Summary: Although spin-scan tomographic scanning imagers have limitations due to their fixed nature, an alternative rosette-scan tomographic scanning imager based on similar concepts allows for dynamic changes in parameters to achieve different trade-offs between resolution and frame rate. Initial results demonstrate the feasibility of this approach.
ELECTRONICS LETTERS
(2023)
Article
Materials Science, Characterization & Testing
Kyle M. Champley, Trevor M. Willey, Hyojin Kim, Karina Bond, Steven M. Glenn, Jerel A. Smith, Jeffrey S. Kallman, William D. Brown, Isaac M. Seetho, Lionel Keene, Stephen G. Azevedo, Larry D. McMichael, George Overturf, Harry E. Martz
Summary: This paper introduces Livermore Tomography Tools (LTT), a customizable scientific software package for computed tomography (CT) research. LTT is capable of processing various CT data accurately and rapidly, with support for multiple CPUs and GPUs. It offers 88 algorithms for pre-processing, reconstruction, post-processing, and simulation, and supports different scanner geometries. Several applications demonstrate LTT's accuracy, speed, and flexibility compared to other solutions.
NDT & E INTERNATIONAL
(2022)
Article
Chemistry, Analytical
Alexey I. Chulichkov, Dmitriy A. Balakin
Summary: The development of new methods to reconstruct object images given their sinogram and additional information is important due to the possibility of artifacts or lack of sharpness in the reconstructed image. This problem is addressed in the field of computer-aided measuring systems, and methods have been developed to solve it by narrowing down the range of possible images using less artifact-inducing information. These methods include local correction of unfiltered backprojection and direct processing of the sinogram using iterative implementation of measurement reduction technique. Examples of these methods applied to teeth sinograms are provided.
Article
Optics
Jiaji Li, Ning Zhou, Zhidong Bai, Shun Zhou, Qian Chen, Chao Zuo
Summary: The study introduces an optimization analysis of illumination pattern in partially coherent optical diffraction tomography (PC-ODT), demonstrating a custom-build quantitative criterion to maximize the performance of phase optical transfer function (POTF). Experimental results show that the optimized illumination pattern outperforms other suboptimal patterns in terms of both signal-to-noise ratio (SNR) and spatial resolution.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Microscopy
Xiaohui Huang, Dzmitry Hlushkou, Di Wang, Ulrich Tallarek, Christian Kubel
Summary: This study systematically investigates the reconstruction reliability of three mainstream algorithms in mesoporous materials. The results show that DART outperforms the other two methods in reliably revealing small pores and narrow channels, especially when the number of projections is strongly constrained.
Article
Mathematics, Applied
Gustav Zickert, Ozan Oktem, Can Evren Yarman
Summary: This paper proposes a method for recovering images from ill-posed tomographic imaging problems using a Gaussian mixture representation. The study focuses on the choice of initial guess and proposes an initialization procedure based on a filtered back projection operator tailored for the Gaussian dictionary. The proposed method is evaluated using simulated data.
Article
Automation & Control Systems
Yanyi Liu, Chen Wang, Yingyou Wen, Yixiang Huo, Jun Liu
Summary: The cellular image analysis system is crucial for disease diagnosis and pharmaceutical research. However, due to the differences in cellular image distribution, the analysis requires customized algorithms and parameter tuning, leading to low automation levels. This study proposes an efficient end-to-end cell segmentation algorithm, ECS-Net, which introduces the proposal focus module (PFM) and enhance mask feature head (EMFH) to improve segmentation accuracy. The algorithm achieves better detection and segmentation accuracy with fewer parameters and computational cost, enhancing cellular image analysis systems. Moreover, considering the medical IoT scenario, the scaled-down model with only 5.8M parameters has a minor decrease in accuracy, but significant application value.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Chengzhu Zhang, Guang-Hong Chen
Summary: This study developed a new method using a single trained deep neural network model to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes. The proposed method utilizes an analytical weighted backprojection reconstruction algorithm and a supervised learning technique to achieve accurate reconstruction of small ROIs within the scanning FOV with high quantitative reconstruction accuracy.
Article
Computer Science, Interdisciplinary Applications
Sayantan Bhadra, Varun A. Kelkar, Frank J. Brooks, Mark A. Anastasio
Summary: This study explores the use of deep neural networks to learn prior information for image reconstruction and their ability to generalize to data outside the training distribution. Inaccurate priors may lead to false structures in the reconstructed image, and the concept of a hallucination map is introduced to understand the impact of prior in regularized reconstruction methods. The behavior of different reconstruction methods is discussed based on numerical studies in a stylized tomographic imaging modality.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Editorial Material
Computer Science, Interdisciplinary Applications
Ge Wang, Mathews Jacob, Xuanqin Mou, Yongyi Shi, Yonina C. Eldar
Summary: This editorial introduces the second special issue of IEEE Transactions on Medical Imaging on deep tomographic reconstruction, highlighting the motivation, summary of included papers, and verification of shared deep learning codes. It also discusses important research topics to facilitate further investigation and collaboration in this rapidly emerging field.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Artificial Intelligence
Jevgenija Rudzusika, Thomas Koehler, Ozan Oktem
Summary: This work introduces an approach for image reconstruction in clinical low-dose tomography by combining principles from sparse signal processing with ideas from deep learning. Firstly, it describes sparse signal representation from a statistical perspective and interprets dictionary learning as aligning the distribution generated by a model with the empirical distribution of true signals. The work also shows that dictionary learning can benefit from computational advancements in deep learning. Furthermore, it demonstrates that regularization with dictionaries achieves competitive performance in computed tomography reconstruction.
SIAM JOURNAL ON IMAGING SCIENCES
(2022)
Article
Biochemical Research Methods
Qiufu Li, Linlin Shen
Summary: The paper introduces a 3D wavelet and deep learning-based method for neuron segmentation, utilizing 3D WaveUNet to process neuronal cubes and improve performance in noisy neuronal images. The integrated 3D wavelets efficiently assist in 3D neuron segmentation and reconstruction.
Article
Cell Biology
Isabel Fernandez de Castro, Jose J. Fernandez, Daniel Barajas, Peter D. Nagy, Cristina Risco
JOURNAL OF CELL SCIENCE
(2017)
Article
Cell Biology
Maria Rosario Fernandez-Fernandez, Desire Ruiz-Garcia, Eva Martin-Solana, Francisco Javier Chichon, Jose L. Carrascosa, Jose-Jesus Fernandez
JOURNAL OF CELL SCIENCE
(2017)
Article
Biochemical Research Methods
J. J. Moreno, A. Martinez-Sanchez, J. A. Martinez, E. M. Garzon, J. J. Fernandez
Article
Biochemistry & Molecular Biology
Jose-Jesus Fernandez, Sam Li, Tanmay A. M. Bharat, David A. Agard
JOURNAL OF STRUCTURAL BIOLOGY
(2018)
Article
Biochemistry & Molecular Biology
Jose-Jesus Fernandez, Sam Li, David A. Agard
JOURNAL OF STRUCTURAL BIOLOGY
(2019)
Article
Biology
Sam Li, Jose-Jesus Fernandez, Wallace F. Marshall, David A. Agard
Article
Biochemical Research Methods
Jose-Jesus Fernandez, Teobaldo E. Torres, Eva Martin-Solana, Gerardo F. Goya, Maria-Rosario Fernandez-Fernandez
Article
Cell Biology
Isabel Fernandez de Castro, Raquel Tenorio, Paula Ortega-Gonzalez, Jonathan J. Knowlton, Paula F. Zamora, Christopher H. Lee, Jose J. Fernandez, Terence S. Dermody, Cristina Risco
JOURNAL OF CELL BIOLOGY
(2020)
Article
Biochemistry & Molecular Biology
Jose-Jesus Fernandez, Sam Li
Summary: TomoAlign is a software package that integrates tools to mitigate the resolution limiting factors in cryoET, focusing on thick specimens. It corrects sample motion and CTF, allowing for efficient workflow from initial alignment to final subtomogram averaging.
JOURNAL OF STRUCTURAL BIOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Gines Avila-Perez, Maria Teresa Rejas, Francisco Javier Chichon, Milagros Guerra, Jose Jesus Fernandez, Dolores Rodriguez
Summary: This study used electron tomography to characterize the replication organelles of toroviruses, revealing the formation of a reticulovesicular network in BEV-infected cells. The outer membranes of DMVs were found to be interconnected with each other and with the ER, with paired ER membranes likely representing early structures that evolve into DMVs. Late-stage paired membranes forming small spherule-like invaginations were also observed, showing similarities but also differences with true spherules described for coronaviruses.
MOLECULAR MICROBIOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
V. Gonzalez-Ruiz, J. P. Garcia-Ortiz, M. R. Fernandez-Fernandez, J. J. Fernandez
Summary: In this study, an Optical Flow (OF)-driven interpolation strategy was developed to address the significant changes between consecutive images in FIB-SEM stacks. The approach compensated for variations by aligning the spatial regions of the biological structures using OF, resulting in sharp interpolated images. The OF-driven interpolation outperformed classical interpolation methods and produced isotropic resolution in anisotropic data.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Jose -Jesus Fernandez, A. Martinez-Sanchez
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Microscopy
V. Gonzalez-Ruiz, M. R. Fernandez-Fernandez, J. J. Fernandez
Summary: FIB-SEM is an imaging technique that allows 3D ultra-structural analysis of cells and tissues at the nanoscale. Our study developed a new approach to structure-preserving noise reduction, which combines the simplicity of Gaussian filtering with the adaptability to biological structures. The results showed that our denoising approach outperforms standard Gaussian filtering and is competitive with state-of-the-art methods in terms of noise reduction and preservation of structure sharpness.
Article
Computer Science, Software Engineering
Vicente Gonzalez-Ruiz, Jose-Jesus Fernandez
Summary: FlowDenoising is a software tool that utilizes an adaptive Gaussian denoising filter to preserve visually appreciable structures in 3D electron microscopy volumes. It achieves this by nonrigidly aligning 2D slices in each dimension using an optical flow estimator, followed by applying a standard separable Gaussian filter. Developed in Python, FlowDenoising makes use of well-known public domain libraries like OpenCV and NumPy. It also utilizes data-level parallelism to greatly reduce processing times, allowing for efficient denoising of large volumes on standard multicore computers. It proves to be a valuable tool in 3DEM for exploring the interior of cells and tissues at the nanoscale.
Article
Biology
Isabel Fernandez de Castro, Jose Jesus Fernandez, Terence S. Dermody, Cristina Risco
Summary: This study utilized TEM, ET, and 3D reconstruction to visualize the egress process of reoviruses in infected HBMECs. It was found that reovirus mature virions are recruited from Vls to SOs, then to MCs, and eventually achieve nonlytic egress through fusion with the plasma membrane.
