title: Image quality comparison between single energy and dual energy CT protocols for hepatic imaging.
abstract: OBJECTIVE
  Multi-detector computed tomography (MDCT) enables volumetric scans in a single breath hold and is clinically useful for hepatic imaging. For simple tasks, conventional single energy (SE) computed tomography (CT) images acquired at the optimal tube potential are known to have better quality than dual energy (DE) blended images. However, liver imaging is complex and often requires imaging of both structures containing iodinated contrast media, where atomic number differences are the primary contrast mechanism, and other structures, where density differences are the primary contrast mechanism. Hence it is conceivable that the broad spectrum used in a dual energy acquisition may be an advantage. In this work we are interested in comparing these two imaging strategies at equal-dose and more complex settings.
  
  
  METHODS
  We developed numerical anthropomorphic phantoms to mimic realistic clinical CT scans for medium size and large size patients. MDCT images based on the defined phantoms were simulated using various SE and DE protocols at pre- and post-contrast stages. For SE CT, images from 60 kVp through 140 with 10 kVp steps were considered; for DE CT, both 80/140 and 100/140 kVp scans were simulated and linearly blended at the optimal weights. To make a fair comparison, the mAs of each scan was adjusted to match the reference radiation dose (120 kVp, 200 mAs for medium size patients and 140 kVp, 400 mAs for large size patients). Contrast-to-noise ratio (CNR) of liver against other soft tissues was used to evaluate and compare the SE and DE protocols, and multiple pre- and post-contrasted liver-tissue pairs were used to define a composite CNR. To help validate the simulation results, we conducted a small clinical study. Eighty-five 120 kVp images and 81 blended 80/140 kVp images were collected and compared through both quantitative image quality analysis and an observer study.
  
  
  RESULTS
  In the simulation study, we found that the CNR of pre-contrast SE image mostly increased with increasing kVp while for post-contrast imaging 90 kVp or lower yielded higher CNR images, depending on the differential iodine concentration of each tissue. Similar trends were seen in DE blended CNR and those from SE protocols. In the presence of differential iodine concentration (i.e., post-contrast), the CNR curves maximize at lower kVps (80-120), with the peak shifted rightward for larger patients. The combined pre- and post-contrast composite CNR study demonstrated that an optimal SE protocol has better performance than blended DE images, and the optimal tube potential for SE scan is around 90 kVp for a medium size patients and between 90 and 120 kVp for large size patients (although low kVp imaging requires high x-ray tube power to avoid photon starvation). Also, a tin filter added to the high kVp beam is not only beneficial for material decomposition but it improves the CNR of the DE blended images as well. The dose adjusted CNR of the clinical images also showed the same trend and radiologists favored the SE scans over blended DE images.
  
  
  CONCLUSIONS
  Our simulation showed that an optimized SE protocol produces up to 5% higher CNR for a range of clinical tasks. The clinical study also suggested 120 kVp SE scans have better image quality than blended DE images. Hence, blended DE images do not have a fundamental CNR advantage over optimized SE images.
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California 94305 and Department of Radiology, Stanford University, Stanford, California 94305.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yao
authors.affiliation: Department of Radiology, Hackensack University Medical Center, Hackensack, New Jersey 07601.
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Ng
authors.affiliation: Department of Radiology, NYU Langone Medical Center, New York, New York 10016.
authors.forename: Alec J
authors.initials: AJ
authors.lastname: Megibow
authors.affiliation: Department of Bioengineering, Stanford University, Stanford, California 94305 and Department of Radiology, Stanford University, Stanford, California 94305.
authors.forename: Norbert J
authors.initials: NJ
authors.lastname: Pelc
mesh_terms: D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D011786:Quality Control; D011829:Radiation Dosage; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*