Ms. Nikkole M. Weber Profile

Nikkole M. Weber

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 7 April 2023 Poster + Paper

Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images

Chelsea A. Dunning, Prabhakar Shantha Rajiah, Scott Hsieh, Andrea Esquivel, Mariana Yalon, Nikkole Weber, Hao Gong, Joel Fletcher, Cynthia McCollough, Shuai Leng

Proceedings Volume 12465, 124652T (2023) https://doi.org/10.1117/12.2654412

KEYWORDS: Radiomics, Image segmentation, Iodine, Computed tomography, Cooccurrence matrices, Feature extraction, Arteries, Photon counting, Cardiac imaging

Read Abstract +

Coronary plaque risk classification in images acquired with photon-counting-detector (PCD) CT was performed using a radiomics-based machine learning (ML) model. With IRB approval, 17 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 4.56 mGy. Four types of images: 120-kV PCD-CT image, virtual monoenergetic images (VMIs) at 50-keV and 100-keV, and iodine maps were reconstructed using an iterative reconstruction algorithm, a vascular kernel (Bv40) and 0.6-mm/0.4-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 1674 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Feature reduction was performed with a clustering algorithm and 6 non-redundant features were input into a linear support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the area under the ROC curve (AUC) was computed. Twelve low-risk and 5 high-risk plaques were identified by the radiologist. A total of 80, 66, 183, and 48 out of 1674 features in 120-kV, 50-keV, 100-keV, and iodine map images were statistically significant. The SVM classified 16/17 plaques correctly in the 120-kV PCD-CT and 50-keV VMI images. The AUC was 0.967, 0.967, 0.917, and 0.833 in 120-kV, 50-keV, 100-keV, and iodine map images, respectively. A ML model using coronary PCD-CTA images at 120-kV and 50-keV best automatically differentiated low- and high-risk coronary plaques.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years