3-D Digital Imaging of Breast Calcifications: Improvements in Image Quality, and Development of Automated Reconstruction Methods

2002 ◽  
Author(s):  
Andrew D. Maidment
Keyword(s):  
Image Quality ◽  
Digital Imaging ◽  
Reconstruction Methods

scholarly journals Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1136
Author(s):  
David Augusto Ribeiro ◽  
Juan Casavílca Silva ◽  
Renata Lopes Rosa ◽  
Muhammad Saadi ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Intelligent Transportation Systems ◽  
Light Field ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Learning Framework ◽  
Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

Evaluating image reconstruction methods in improving effective parameters on image quality in IRI-microPET

2014 ◽  
Vol 57 (2) ◽  
pp. 218-221 ◽  
Author(s):  
S. Z. Islami rad ◽  
M. Shamsaei Zafarghandi ◽  
R. Gholipour Peyvandi ◽  
M. Ghannadi Maragheh
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Effective Parameters ◽  
Reconstruction Methods

Editorial - Dose and Image Quality in Digital Imaging and Interventional Radiology

2001 ◽  
Vol 94 (1) ◽  
pp. 9-9 ◽  
Author(s):  
J.F. Malone ◽  
G. Boyle ◽  
D.M. Marsh ◽  
D. Tuenen
Keyword(s):  
Interventional Radiology ◽  
Image Quality ◽  
Digital Imaging

Digital Imaging Exposure-Time Optimisation of Tripod-Camera Vibrating Systems Based on MTF

2008 ◽  
Author(s):  
Giulio Fanti ◽  
Roberto Basso
Keyword(s):  
Image Quality ◽  
Exposure Time ◽  
Digital Image ◽  
Digital Imaging ◽  
Digital Images ◽  
Noise Sources ◽  
Initial Analysis ◽  
Vibrating System ◽  
Time Optimization ◽  
Vibrating Systems

The problem of exposure-time optimization in digital images acquired by a tripod-camera vibrating system is examined in this paper and an initial analysis is presented. The different noise sources concerning both the acquisition sensor in the camera and external vibrations were studied and quantified in some specific cases. The digital image quality is then discussed in terms of the MTF function evaluated at 50% level in order to define what the optimum ranges of exposure-times are.

scholarly journals Digital imaging in pathology: the case for standardization

2005 ◽  
Vol 11 (3) ◽  
pp. 109-116 ◽  
Author(s):  
Yukako Yagi ◽  
John R Gilbertson
Keyword(s):  
Image Quality ◽  
Sample Preparation ◽  
Digital Image ◽  
Digital Imaging ◽  
Public Organization ◽  
Extensive Literature ◽  
Direct Examination ◽  
Transmission Of Information ◽  
Complete Process

The process of digital imaging in microscopy is a series of operations, each contributing to the quality of the final image that is displayed on the computer monitor. The operations include sample preparation and staining by histology, optical image formation by the microscope, digital image sampling by the camera sensor, postprocessing and compression, transmission across the network and display on the monitor. There is an extensive literature about digital imaging and each step of the process is fairly well understood. However, the complete process is very hard to standardize or even to understand fully. The important concepts for pathology imaging standards are: (1) systems should be able to share image files, (2) the standards should allow the transmission of information on baseline colours and recommended display parameters, (3) the images should be useful to the pathologist, not necessarily better or worse than direct examination of a slide under the microscope, (4) a mechanism to evaluate image quality objectively should be present, (5) a mechanism to adjust and correct the minor errors of tissue processing should be developed, (6) a public organization should support pathologists in the development of standards.

Digital Imaging for Photographic Collections: Foundations for Technical Standard

2000 ◽  
Vol 29 (1) ◽  
Author(s):  
Robin Dale
Keyword(s):  
Image Quality ◽  
Digital Imaging ◽  
Digital Images ◽  
National Endowment ◽  
Technical Standard ◽  
Digital Form ◽  
Project Report

The goal of this project report, sponsored by The National Endowment for the Humanities, Division of Preservation and Access, is “to offer some guidance to libraries, archives, and museums in their efforts to convert photographic collections to digital form.” To date, there are no standards for measuring the quality of digital images created from photographs. Therefore, this report is primarily concerned with developing tools to measure image quality. Other technical and managerial issues related to digital imaging projects in general are also addressed.

scholarly journals Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

2021 ◽  
Author(s):  
Xinzeng Wang ◽  
Jingfei Ma ◽  
Priya Bhosale ◽  
Juan J. Ibarra Rovira ◽  
Aliya Qayyum ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Reconstruction Method ◽  
Anatomical Landmarks ◽  
Resonance Imaging ◽  
Endorectal Coil ◽  
Reconstruction Methods

Abstract Introduction Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIRTM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts. Methods This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERCDLR, ERCConv, Non-ERCDLR, and Non-ERCConv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor. Results The Non-ERCDLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor. Conclusion Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

Image quality assessment for an Intel digital imaging chip set prototype

1998 ◽  
Author(s):  
Lawrence A. Booth, Jr. ◽  
Phillip G. Austin ◽  
Caren Firsty ◽  
Werner A. Metz
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Digital Imaging ◽  
Image Quality Assessment

scholarly journals Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction

2021 ◽  
Vol 15 ◽  
pp. 611
Author(s):  
Samara Oliveira Pinto ◽  
Paulo R. R. V. Caribe ◽  
Lucas Narciso ◽  
Ana Maria Marques da Silva
Keyword(s):  
Image Quality ◽  
Fdg Pet ◽  
Head Movements ◽  
Patient Comfort ◽  
Acquisition Time ◽  
Brain Images ◽  
Minimal Impact ◽  
Reconstruction Methods ◽  
Scan Time ◽  
18F Fdg

Iterative image reconstruction methods are widely used in PET due to their better image quality when compared to analytical methods. However, inaccurate quantification occurs in low activity concentration regions, which leads to biased quantification of PET images. The diagnosis of some neurodegenerative diseases, such as Alzheimer’s disease, is based on identifying such low-uptake regions. Furthermore, PET imaging in these populations should be as short as possible to limit head movements and improve patient comfort. This work aims to identify optimized reconstruction parameters of [18F]FDG PET brain images aiming to reduce image acquisition time with minimal impact on quantification. For this, [18F]FDG PET images of a Hoffman 3-D brain phantom were acquired. Analytical and iterative reconstruction methods were compared utilizing image quality and quantitative accuracy metrics. OSEM reconstruction algorithm was optimized (4 iterations and 32 subsets). It resulted in remarkably similar images compared to the current clinical settings, with a 50% reduction in scan time (5 min with a post-reconstruction filter of 4 mm). Future clinical studies are needed to confirm the results presented here.

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