Initial Results from a High-Energy X-Ray Inline Phase Sensitive Breast Tomosynthesis (PBT) Prototype System
15TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI2020)(2020)
Univ Oklahoma
Abstract
X-ray phase sensitive imaging has been employed in the preclinical settings for more than two decades. Advancements in the technology has allowed to potentially translate this innovative imaging technique to the clinical environment. Inline phase sensitive imaging technique has shown promising potential to be used for breast cancer imaging. A high energy phase sensitive breast tomosynthesis (PBT) prototype system based on the inline phase sensitive imaging technique has been developed for the potential imaging in clinical environment. The prototype system incorporates a microfocus x-ray tube and a flat panel detector having a pixel pitch of 70 mu m. The microfocus x-ray tube has a tungsten (W) anode, Beryllium (Be) output window and a focal spot size that ranges from 18-50 mu m, depending on the output power. The x-ray tube/detector configuration produces a geometric magnification (M) of 2.2 and acquires 9 projection views within 15 degrees or 30 projection views within 30 degrees in stop-and-shoot scanning mode. A single distance phase retrieval scheme method based on the Phase-Attenuation Duality (PAD) principle is applied to the angular projection views. A filtered back-projection operation reconstructs a set of tomogram slices at 1mm incremental depth within the breast along the z-direction. American College of Radiology phantom images demonstrate that both 2D and tomosynthesis images acquired on the prototype system meet the minimum criteria set by the Mammography Quality Standard Act. We have also imaged mastectomy specimens with the PBT prototype system at the University of Utah Huntsman Cancer Hospital. PBT 2D images and tomosynthesis image slices demonstrate image quality comparable to a conventional digital breast tomosynthesis clinical system.
MoreTranslated text
Key words
Phase Contrast Imaging,ACR Phantom,Mastectomy,Edge Enhancement,Phase Retrieval
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined