Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
by
Hashemi, SayedMasoud
, Paul, Narinder S.
, Mehrez, Hatem
, Cobbold, Richard S. C.
in
Accuracy
/ Algorithms
/ Biomedical engineering
/ Calcification
/ Chest
/ Computer Simulation
/ Diagnostic Radiology
/ Humans
/ Image Processing, Computer-Assisted
/ Imaging
/ Internal Medicine
/ Interventional Radiology
/ Lung cancer
/ Medicine
/ Medicine & Public Health
/ Mortality
/ Multiple Pulmonary Nodules - diagnostic imaging
/ Neuroradiology
/ Phantoms, Imaging
/ Radiology
/ Reproducibility of Results
/ Simulation
/ Tomography
/ Tomography, X-Ray Computed - methods
/ Ultrasound
2014
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
by
Hashemi, SayedMasoud
, Paul, Narinder S.
, Mehrez, Hatem
, Cobbold, Richard S. C.
in
Accuracy
/ Algorithms
/ Biomedical engineering
/ Calcification
/ Chest
/ Computer Simulation
/ Diagnostic Radiology
/ Humans
/ Image Processing, Computer-Assisted
/ Imaging
/ Internal Medicine
/ Interventional Radiology
/ Lung cancer
/ Medicine
/ Medicine & Public Health
/ Mortality
/ Multiple Pulmonary Nodules - diagnostic imaging
/ Neuroradiology
/ Phantoms, Imaging
/ Radiology
/ Reproducibility of Results
/ Simulation
/ Tomography
/ Tomography, X-Ray Computed - methods
/ Ultrasound
2014
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
by
Hashemi, SayedMasoud
, Paul, Narinder S.
, Mehrez, Hatem
, Cobbold, Richard S. C.
in
Accuracy
/ Algorithms
/ Biomedical engineering
/ Calcification
/ Chest
/ Computer Simulation
/ Diagnostic Radiology
/ Humans
/ Image Processing, Computer-Assisted
/ Imaging
/ Internal Medicine
/ Interventional Radiology
/ Lung cancer
/ Medicine
/ Medicine & Public Health
/ Mortality
/ Multiple Pulmonary Nodules - diagnostic imaging
/ Neuroradiology
/ Phantoms, Imaging
/ Radiology
/ Reproducibility of Results
/ Simulation
/ Tomography
/ Tomography, X-Ray Computed - methods
/ Ultrasound
2014
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
Journal Article
Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT
2014
Request Book From Autostore
and Choose the Collection Method
Overview
Objectives
To optimize the slice thickness/overlap parameters for image reconstruction and to study the effect of iterative reconstruction (IR) on detectability and characterization of small non-calcified pulmonary nodules during low-dose thoracic CT.
Materials and methods
Data was obtained from computer simulations, phantom, and patient CTs. Simulations and phantom CTs were performed with 9 nodules (5, 8, and 10 mm with 100, −630, and −800 HU). Patient data were based on 11 ground glass opacities (GGO) and 9 solid nodules. For each analysis the nodules were reconstructed with filtered back projection and IR algorithms using 10 different combinations of slice thickness/overlap (0.5–5 mm). The attenuation (CT#) and the contrast to noise ratio (CNR) were measured. Spearman’s coefficient was used to correlate the error in CT# measurements and slice thickness. Paired Student’s
t
test was used to measure the significance of the errors.
Results
CNR measurements
: CNR increases with increasing slice thickness/overlap for large nodules and peaks at 4.0/2.0 mm for smaller ones. Use of IR increases the CNR of GGOs by 60 %.
CT# measurements
: Increasing slice thickness/overlap above 3.0/1.5 mm results in decreased CT# measurement accuracy.
Conclusion
Optimal detection of small pulmonary nodules requires slice thickness/overlap of 4.0/2.0 mm. Slice thickness/overlap of 2.0/2.0 mm is required for optimal nodule characterization. IR improves conspicuity of small ground glass nodules through a significant increase in nodule CNR.
Key Points
• Slice thickness/overlap affects the accuracy of pulmonary nodule detection and characterization.
• Slice thickness ≥3 mm increases the risk of misclassifying small nodules.
• Optimal nodule detection during low-dose CT requires 4.0/2.0-mm reconstructions.
• Optimal nodule characterization during low-dose CT requires 2.0/2.0-mm reconstructions.
• Iterative reconstruction improves the CNR of ground glass nodules by 60 %.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
This website uses cookies to ensure you get the best experience on our website.