Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images

2016 ◽  
Vol 43 (10) ◽  
pp. 5330-5338 ◽  
Author(s):  
Jeremiah Sanders ◽  
Lynne Hurwitz ◽  
Ehsan Samei
Keyword(s):  
Image Quality ◽  
Spatial Resolution ◽  
Ct Images ◽  
Patient Specific ◽  
Automated Method

scholarly journals P.169 Reduced radiation CT imaging for augmented reality spinal surgery applications

2021 ◽  
Vol 48 (s3) ◽  
pp. S68-S68
Author(s):  
M de Lotbiniere-Bassett ◽  
E Schonfeld ◽  
T Jansen ◽  
D Anthony ◽  
A Veeravagu
Keyword(s):  
Image Quality ◽  
Augmented Reality ◽  
Radiation Dose ◽  
Spinal Surgery ◽  
Ct Images ◽  
Patient Specific ◽  
Future Research ◽  
Pedicle Screw Placement ◽  
Quality Threshold ◽  
Ct Image Quality

Background: There is growing evidence for the use of augmented reality (AR) in pedicle screw placement in spinal surgery to increase surgical accuracy, improve clinical outcomes and reduce the radiation exposure required for intraoperative navigation. Auto-segmentation is the cornerstone of AR applications because it correlates patient-specific anatomy to structures segmented from preoperative computed tomography (pCT) images. These AR techniques allow for a reduction in the radiation dose required to acquire CT images while maintaining accurate segmentation. Methods: In this study, we methodically increase the noise that is introduced into CT images to determine the image quality threshold that is required for auto-segmentation on pCT. We then enhance the images with denoising algorithms to evaluate the effect on the segmentation. Results: The pCT radiation dose is decreased to below the current lowest clinical threshold and the resulting images still produce segmentations that are appropriate for input into AR applications. The application of denoising algorithms to the images resulted in increased artifacts and decreased bone density. Conclusions: The CT image quality that is required for successful AR auto-segmentation is lower than that which is currently employed in spine surgery. Future research is required to identify the specific, clinically relevant radiation dose thresholds.

scholarly journals Synthetic Aperture Imaging Using High-Frequency Convex Array for Ophthalmic Ultrasound Applications

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2275
Author(s):  
Hae Gyun Lim ◽  
Hyung Ham Kim ◽  
Changhan Yoon
Keyword(s):  
Image Quality ◽  
Penetration Depth ◽  
Spatial Resolution ◽  
High Frequency ◽  
Imaging System ◽  
Ex Vivo ◽  
Synthetic Aperture ◽  
Single Element ◽  
High Frequency Ultrasound ◽  
Synthetic Aperture Imaging

High-frequency ultrasound (HFUS) imaging has emerged as an essential tool for pre-clinical studies and clinical applications such as ophthalmic and dermatologic imaging. HFUS imaging systems based on array transducers capable of dynamic receive focusing have considerably improved the image quality in terms of spatial resolution and signal-to-noise ratio (SNR) compared to those by the single-element transducer-based one. However, the array system still suffers from low spatial resolution and SNR in out-of-focus regions, resulting in a blurred image and a limited penetration depth. In this paper, we present synthetic aperture imaging with a virtual source (SA-VS) for an ophthalmic application using a high-frequency convex array transducer. The performances of the SA-VS were evaluated with phantom and ex vivo experiments in comparison with the conventional dynamic receive focusing method. Pre-beamformed radio-frequency (RF) data from phantoms and excised bovine eye were acquired using a custom-built 64-channel imaging system. In the phantom experiments, the SA-VS method showed improved lateral resolution (>10%) and sidelobe level (>4.4 dB) compared to those by the conventional method. The SNR was also improved, resulting in an increased penetration depth: 16 mm and 23 mm for the conventional and SA-VS methods, respectively. Ex vivo images with the SA-VS showed improved image quality at the entire depth and visualized structures that were obscured by noise in conventional imaging.

Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

2021 ◽  
Author(s):  
Khalid Labib Alsamadony ◽  
Ertugrul Umut Yildirim ◽  
Guenther Glatz ◽  
Umair bin Waheed ◽  
Sherif M. Hanafy
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Transfer Learning ◽  
Temporal Resolution ◽  
Exposure Time ◽  
Ct Images ◽  
Loss Functions ◽  
High Quality ◽  
X Ray ◽  
Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

Deep learning-accelerated T2-weighted imaging of the prostate: impact of further acceleration with lower spatial resolution on image quality

2021 ◽  
pp. 110012
Author(s):  
Eu Hyun Kim ◽  
Moon Hyung Choi ◽  
Young Joon Lee ◽  
Dongyeob Han ◽  
Mahmoud Mostapha ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Spatial Resolution

scholarly journals Enhancing Patient Experience With Internet Protocol Addressable Digital LED Lighting in Imaging Environments: A Phase I Study (Preprint)

2018 ◽  
Author(s):  
Melanie U Knopp ◽  
Katherine Binzel ◽  
Chadwick L Wright ◽  
Jun Zhang ◽  
Michael V Knopp
Keyword(s):  
Image Quality ◽  
Patient Experience ◽  
Patient Comfort ◽  
Patient Specific ◽  
Diagnostic Image Quality ◽  
Imaging Studies ◽  
Diagnostic Image ◽  
Clinical Imaging ◽  
Ambient Lighting ◽  
Led Lights

BACKGROUND Conventional approaches to improve the quality of clinical patient imaging studies focus predominantly on updating or replacing imaging equipment; however, it is often not considered that patients can also highly influence the diagnostic quality of clinical imaging studies. Patient-specific artifacts can limit the diagnostic image quality, especially when patients are uncomfortable, anxious, or agitated. Imaging facility or environmental conditions can also influence the patient’s comfort and willingness to participate in diagnostic imaging studies, especially when performed in visually unesthetic, anxiety-inducing, and technology-intensive imaging centers. When given the opportunity to change a single aspect of the environmental or imaging facility experience, patients feel much more in control of the otherwise unfamiliar and uncomfortable setting. Incorporating commercial, easily adaptable, ambient lighting products within clinical imaging environments allows patients to individually customize their environment for a more personalized and comfortable experience. OBJECTIVE The aim of this pilot study was to use a customizable colored light-emitting diode (LED) lighting system within a clinical imaging environment and demonstrate the feasibility and initial findings of enabling healthy subjects to customize the ambient lighting and color. Improving the patient experience within clinical imaging environments with patient-preferred ambient lighting and color may improve overall patient comfort, compliance, and participation in the imaging study and indirectly contribute to improving diagnostic image quality. METHODS We installed consumer-based internet protocol addressable LED lights using the ZigBee standard in different PET/CT scan rooms within a clinical imaging environment. We recruited healthy volunteers (n=35) to generate pilot data in order to develop a subsequent clinical trial. The visual perception assessment procedure utilized questionnaires with preprogrammed light/color settings and further assessed how subjects preferred ambient light and color within a clinical imaging setting. RESULTS Technical implementation using programmable LED lights was performed without any hardware or electrical modifications to the existing clinical imaging environment. Subject testing revealed substantial variabilities in color perception; however, clear trends in subject color preference were noted. In terms of the color hue of the imaging environment, 43% (15/35) found blue and 31% (11/35) found yellow to be the most relaxing. Conversely, 69% (24/35) found red, 17% (6/35) found yellow, and 11% (4/35) found green to be the least relaxing. CONCLUSIONS With the majority of subjects indicating that colored lighting within a clinical imaging environment would contribute to an improved patient experience, we predict that enabling patients to customize environmental factors like lighting and color to individual preferences will improve patient comfort and patient satisfaction. Improved patient comfort in clinical imaging environments may also help to minimize patient-specific imaging artifacts that can otherwise limit diagnostic image quality. CLINICALTRIAL ClinicalTrials.gov NCT03456895; https://clinicaltrials.gov/ct2/show/NCT03456895

scholarly journals Image Quality Differences In CT Scan Thorax By Using Slice Thickness Variation

2017 ◽  
Vol 8 (2) ◽  
pp. 87-91
Author(s):  
Samsun Samsun ◽  
Legia Prananto ◽  
Novita Wulandari
Keyword(s):  
Image Quality ◽  
Ct Scan ◽  
Spatial Resolution ◽  
Optimal Parameter ◽  
Thickness Variation ◽  
Slice Thickness ◽  
Picture Quality ◽  
Thorax Ct ◽  
Research Show ◽  
Selection Of

The picture quality get from CT Scan of Thorax which required optimal parameter selection that’s right, one of them the selection of slice thickness. The method taken from theses that have been publish in the year 2013. The results of the research show the percentage of the value of the average spatial resolution of 2.5 mm slice thickness is (33.3%), noise (17.8%), artefact (1%). On the thickness of the slices 5 mm spatial resolution is (17%), noise (8.9%), artefacts (0%). On the thickness of slices of 7.5 mm spatial resolution is (8.9%), noise (11.1%), artefacts (53.3%). While the thickness of the slices the spatial resolution is 10 mm (8.9%), noise (22.2%), artefacts (68.9%). Based on the research results obtained the conclusion that thickness 2.5 mm slices on Thorax CT-Scan images produce better picture quality than with the thickness of the slices 5 mm, 7.5 mm, 10 mm, because the spatial resolution is more clear so as to reduce noise and artifacts.

An Automatic System for Analyzing Phantom Images to Determine the Reliability of PET/SPECT Cameras

2015 ◽  
Author(s):  
Miri Weiss Cohen ◽  
John A. Kennedy ◽  
Archil Pirmisashvili ◽  
Gleb Orlikov
Keyword(s):  
Quality Control ◽  
Image Quality ◽  
Spatial Resolution ◽  
Automatic System ◽  
Quality Parameters ◽  
Tomographic Image ◽  
Performance Parameters ◽  
Pet Ct ◽  
Ct Scanners

This paper describes an automatic system for analyzing phantom images from two types of PET/CT scanners. The system was developed for the purpose of obtaining tomographic image quality parameters, which determine a number of different performance parameters, primarily scanner sensitivity, tomographic uniformity, contrast and spatial resolution. The system provides a method for generating and altering image masks used for the analysis of PET images, which are then automatically aligned with the PET data. The system automatically generates Quality Control (QC) reports and is currently being used at clinical PET/CT center.

Sign in / Sign up

Export Citation Format

Share Document