TY - GEN
T1 - Automated liver lesion characterization using fast kVp switching dual energy computed tomography imaging
AU - Santamaria-Pang, Alberto
AU - Dutta, Sandeep
AU - Makrogiannis, Sokratis
AU - Hara, Amy
AU - Pavlicek, William
AU - Silva, Alvin
AU - Thomsen, Brian
AU - Robertson, Scott
AU - Okerlund, Darin
AU - Langan, David A.
AU - Bhotika, Rahul
N1 - Publisher Copyright:
© 2010 SPIE.
PY - 2010
Y1 - 2010
N2 - Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.
AB - Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.
KW - Gemstone Spectral Imaging CT
KW - Liver Lesion Classification
KW - Projection-Based Material Decomposition
UR - http://www.scopus.com/inward/record.url?scp=79953653269&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953653269&partnerID=8YFLogxK
U2 - 10.1117/12.844059
DO - 10.1117/12.844059
M3 - Conference contribution
AN - SCOPUS:79953653269
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2010
A2 - Summers, Ronald M.
A2 - Karssemeijer, Nico
PB - SPIE
T2 - Medical Imaging 2010: Computer-Aided Diagnosis
Y2 - 16 February 2010 through 18 February 2010
ER -