Dataset Information

Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume.

ABSTRACT: The reduction of the electron dose in electron tomography of biological samples is of high significance to diminish radiation damages. Simulations have shown that sparse data collection can perform efficient electron dose reduction. Frameworks based on compressive-sensing or inpainting algorithms have been proposed to accurately reconstruct missing information in sparse data. The present work proposes a practical implementation to perform tomographic collection of block-based sparse images in scanning transmission electron microscopy. The method has been applied on sections of chemically-fixed and resin-embedded Trypanosoma brucei cells. There are 3D reconstructions obtained from various amounts of downsampling, which are compared and eventually the limits of electron dose reduction using this method are explored.

SUBMITTER: Trepout S

PROVIDER: S-EPMC6679239 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume.

Trépout Sylvain S

Materials (Basel, Switzerland) 20190716 14

The reduction of the electron dose in electron tomography of biological samples is of high significance to diminish radiation damages. Simulations have shown that sparse data collection can perform efficient electron dose reduction. Frameworks based on compressive-sensing or inpainting algorithms have been proposed to accurately reconstruct missing information in sparse data. The present work proposes a practical implementation to perform tomographic collection of block-based sparse images in sc ...[more]

PMID: 31315199

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Project description:BACKGROUND:Since most prostatic diseases are associated with the organ's enlargement, evaluation of prostatic size is a main criterion in the diagnosis of prostatic state of health. While enlargement is a non-uniform process, volumetric measurements are believed to be advantageous to any single dimensional parameter for the diagnosis of prostatomegaly. In a previous study, volume was analysed with a slice addition technique (SAT), which was validated as highly accurate. Irrespective of high accuracy, SAT represents a complex and time-consuming procedure, which limits its clinical use. Thus, demand exists for more practical volume assessment methods. In this study, the prostatic volume of 95 canine patients (58 intact males, 37 neutered males) were analysed retrospectively by using the ellipsoid formula (Formula) and an imaging "wrap" function tool (Wrap) to help assess accuracy and applicability. Accuracy was checked against phantom measurements and results were compared to SAT measurements of the same patient pool obtained from a previously published paper. Patients were grouped according to prostatic structure (H?=?homogeneous, I?=?inhomogeneous, C?=?cystic) and volume using the SAT (volume group?=?vg: 1, 2 and 3). RESULTS:High correlation between the Formula or Wrap volume and the phantom volume was found, the values being higher for the Formula. Mean Formula volumes (vg 1: 2.2?cm3, vg 2: 14.5?cm3, vg 3: 109.4?cm3, respectively) were significantly underestimated, while mean Wrap volumes (vg 1: 3.8?cm3, vg 2: 19.5?cm3, vg 3: 159.2?cm3) were statistically equivalent to SAT measurements (vg 1: 3.1?cm3, vg 2: 18.6?cm3, vg 3: 157.2?cm3, respectively). Differences between Formula and SAT volumes ranged from 22.4-31.1%, while differences between Wrap and SAT volumes were highest in small prostates (vg 1: 22.1%) and fell with increasing prostatic size (vg 3: 1.3%). CONCLUSION:The Wrap function is highly accurate, less time-consuming and complex compared to SAT and could serve as beneficial tool for measuring prostatic volume in clinical routine after further validation in future studies. The Formula method cannot be recommended as an alternative for volumetric measurements of the prostate gland due to its underestimation of volumes compared to SAT results.

Project description:Summary Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors. Highlights • A multi-domain integrative Swin transformer was proposed for spare-data imaging• A trainable image edge enhancement filter recovers sharp image edges and features• A modified vision transformer first demonstrates its great potential in tomography• Patient clinical cardiac and numerical data demonstrate our MIST power The bigger picture Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve reconstructed image quality from sparse-view data, we develop a multi-domain integrative Swin transformer (MIST) network in this study. The proposed MIST-net incorporates lavish domain features from data, residual data, image, and residual image using flexible network architectures, which help deeply mine the data and image features. To detect image features and protect image edges, the trainable edge enhancement filter is further incorporated to the network for improving encode-decode ability. A high-quality reconstruction transformer was designed to improve the ability of global feature extraction. Our results from both simulation and real cardiac data demonstrated the great potential of MIST. A multi-domain integrative Swin transformer network (MIST-net) demonstrates feasibility, advantages, and great potential in sparse-view tomographic reconstruction. MIST-net mainly integrates data, image, residual data, and residual image domain information into a unified model with a trainable edge enhancement filter and advanced transformer AI technique to realize ultra-sparse-data (48 views) high-fidelity imaging.

Dataset Information

Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume.

Publications

Tomographic Collection of Block-Based Sparse STEM Images: Practical Implementation and Impact on the Quality of the 3D Reconstructed Volume.

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