scholarly journals Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gábor Opposits ◽  
Marianna Nagy ◽  
Zoltán Barta ◽  
Csaba Aranyi ◽  
Dániel Szabó ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Ct Images ◽  
High Dose ◽  
Virtual Bronchoscopy ◽  
Ct Image ◽  
Pet Ct ◽  
Computer Aided ◽  
Image Registrations ◽  
Diagnostic Ct

Abstract Background Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. Results For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. Conclusion A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations.

scholarly journals Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-16
Author(s):  
Xiaowe Xu ◽  
Jiawei Zhang ◽  
Jinglan Liu ◽  
Yukun Ding ◽  
Tianchen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Denoising ◽  
Ct Images ◽  
High Dose ◽  
Ct Image ◽  
Paired Data ◽  
High Quality ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Global Cycle

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

An effective computer aided diagnosis model for pancreas cancer on PET/CT images

2018 ◽  
Vol 165 ◽  
pp. 205-214 ◽  
Author(s):  
Siqi Li ◽  
Huiyan Jiang ◽  
Zhiguo Wang ◽  
Guoxu Zhang ◽  
Yu-dong Yao
Keyword(s):  
Pancreas Cancer ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Pet Ct ◽  
Computer Aided ◽  
Diagnosis Model ◽  
Aided Diagnosis

Nonrigid registration of dynamic breast F-18-FDG PET/CT images using deformable FEM model and CT image warping

2007 ◽  
Author(s):  
Alphonso Magri ◽  
Andrzej Krol ◽  
Mehmet Unlu ◽  
Edward Lipson ◽  
James Mandel ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Ct Images ◽  
Image Warping ◽  
Nonrigid Registration ◽  
Ct Image ◽  
Fem Model ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Could Different Hydration Protocols Affect the Quality of 18F-FDG PET/CT Images?

2011 ◽  
Vol 39 (2) ◽  
pp. 77-82 ◽  
Author(s):  
L. Ceriani ◽  
S. Suriano ◽  
T. Ruberto ◽  
L. Giovanella
Keyword(s):  
Fdg Pet ◽  
Ct Images ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Thoracic-abdominal imaging with a novel dual-layer spectral detector CT: intra-individual comparison of image quality and radiation dose with 128-row single-energy acquisition

2018 ◽  
Vol 59 (12) ◽  
pp. 1458-1465 ◽  
Author(s):  
Stefan Haneder ◽  
Florian Siedek ◽  
Jonas Doerner ◽  
Gregor Pahn ◽  
Nils Grosse Hokamp ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Ct Images ◽  
Ct Image ◽  
Dose Length Product ◽  
Portal Venous Phase ◽  
Ct Dose ◽  
Automatic Tube Current Modulation ◽  
Lower Radiation Dose

Background A novel, multi-energy, dual-layer spectral detector computed tomography (SDCT) is commercially available now with the vendor’s claim that it yields the same or better quality of polychromatic, conventional CT images like modern single-energy CT scanners without any radiation dose penalty. Purpose To intra-individually compare the quality of conventional polychromatic CT images acquired with a dual-layer spectral detector (SDCT) and the latest generation 128-row single-energy-detector (CT128) from the same manufacturer. Material and Methods Fifty patients underwent portal-venous phase, thoracic-abdominal CT scans with the SDCT and prior CT128 imaging. The SDCT scanning protocol was adapted to yield a similar estimated dose length product (DLP) as the CT128. Patient dose optimization by automatic tube current modulation and CT image reconstruction with a state-of-the-art iterative algorithm were identical on both scanners. CT image contrast-to-noise ratio (CNR) was compared between the SDCT and CT128 in different anatomic structures. Image quality and noise were assessed independently by two readers with 5-point-Likert-scales. Volume CT dose index (CTDIvol), and DLP were recorded and normalized to 68 cm acquisition length (DLP68). Results The SDCT yielded higher mean CNR values of 30.0% ± 2.0% (26.4–32.5%) in all anatomic structures ( P < 0.001) and excellent scores for qualitative parameters surpassing the CT128 (all P < 0.0001) with substantial inter-rater agreement (κ ≥ 0.801). Despite adapted scan protocols the SDCT yielded lower values for CTDIvol (–10.1 ± 12.8%), DLP (−13.1 ± 13.9%), and DLP68 (–15.3 ± 16.9%) than the CT128 (all P < 0.0001). Conclusion The SDCT scanner yielded better CT image quality compared to the CT128 and lower radiation dose parameters.

scholarly journals Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method

2014 ◽  
Vol 55 (6) ◽  
pp. 1153-1162 ◽  
Author(s):  
Z. Jin ◽  
H. Arimura ◽  
Y. Shioyama ◽  
K. Nakamura ◽  
J. Kuwazuru ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Lung Tumor ◽  
Ct Images ◽  
Selection Method ◽  
Computer Assisted ◽  
Ct Image ◽  
Pet Ct ◽  
Planning Ct

scholarly journals Multimodal virtual bronchoscopy using PET/CT images

2008 ◽  
Vol 13 (2) ◽  
pp. 106-113 ◽  
Author(s):  
Karl-Hans Englmeier ◽  
Marcus D. Seemann
Keyword(s):  
Ct Images ◽  
Virtual Bronchoscopy ◽  
Pet Ct

Multimodal virtual bronchoscopy using PET/CT images

2008 ◽  
Vol 13 (2) ◽  
pp. 106-113 ◽  
Author(s):  
Karl-Hans Englmeier ◽  
Marcus Seemann
Keyword(s):  
Ct Images ◽  
Virtual Bronchoscopy ◽  
Pet Ct

scholarly journals SPNsim: A database of simulated solitary pulmonary nodule PET/CT images facilitating computer aided diagnosis

2016 ◽  
Vol 63 ◽  
pp. 357-365
Author(s):  
G. Tzanoukos ◽  
E. Athanasiadis ◽  
A. Gaitanis ◽  
A. Georgakopoulos ◽  
A. Chatziioannou ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Pet Ct ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Selection in a 3D microtomographic image the region with the highest quality

2019 ◽  
pp. 160-168 ◽  
Author(s):  
A S Kornilov ◽  
I V Safonov ◽  
A V Goncharova ◽  
I V Yakimchuk
Keyword(s):  
Flow Simulation ◽  
Visual Quality ◽  
Quality Measures ◽  
Ct Images ◽  
Ct Image ◽  
Micro Ct ◽  
X Ray ◽  
Image Slice ◽  
Selection Of

We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.

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