Michal E. Kulon, MD 1,2 1. Peter Komlosi, MD, PhD 3 3. Radiology Universe Institute, 2

Transcription

1 Blend+Proximity, a novel algorithm achieves high suppression of metallic streak artifacts and maximal preservation of contrast between soft tissues and iodinated contrast material on dual-energy CT scans Michal E. Kulon, MD 1,2 1 Radiology Universe Institute, 2 Yale School of Medicine, Dept. of Radiology and Biomedical Imaging Peter Komlosi, MD, PhD 3 3 University of Pittsburgh Medical Center

2 Disclosures and Notices Michal Kulon, MD Implemented Blend + Proximity TM Software (patent-pending) Implemented Lost Souls TM Software (patent-pending) Radiology Universe Institute, a public benefit corporation Founder, Director of Radiology Informatics, shareholder Peter Komlosi, MD PhD Contributed to Blend + Proximity TM Software (patent-pending) Radiology Universe Institute, a public benefit corporation Director of Radiology Research Radiology Universe, Blend + Proximity, Lost Souls are trademarks of Radiology Universe Institute.

3 Dual-Energy CT: Which Energy Level is Better? Low Energy Image (70 kev) High Energy Image (130 kev)

4 Dual-Energy CT: Metallic Artifacts Low Energy Image (70 kev) High Energy Image (130 kev) WINNER?

5 Dual-Energy CT: Soft Tissue Quality WINNER? Low Energy Image (70 kev) High Energy Image (130 kev)

6 Dual-Energy CT: Iodinated Contrast Material WINNER? Low Energy Image (70 kev) High Energy Image (130 kev)

7 Low Energy Image (70 kev) High Energy Image (130 kev) Mediocre Compromise Excels at Neither aspect Best of Both Worlds

8 Low Energy Image (70 kev) High Energy Image (130 kev) Mediocre Compromise Excels at Neither aspect Best of Both Worlds

9 Blend + Proximity Algorithm Overview

10 Algorithm Implementation Each output voxel (V) is a weighted average of the 2 corresponding voxels from the 70-keV and 130-keV mono-energetic images V Output = w V LowEnergy + (1 - w) V HighEnergy The weighting factor (w, range 0 to 1) is calculated individually for each output voxel Generally favors the low-energy input voxel, unless artifact is present. The likelihood and magnitude of artifact are considered to be increasing if: High Discrepancy in Voxel Intensities between corresponding low- & high- voxels. Implies an area of dark streak artifact. Proximity to Metal or other very dense material (>1100 Hounsfield Units). Similar to a gravity field Smooth transitions to avoid artifactual edges. Custom program using C++ and DCMTK.

11 Low Energy Image Calculated Blending Map Black: use High Energy Voxel White: use Low Energy Voxel High Energy Image

12 Low Energy Image Calculated Blending Map Black: use High Energy Voxel White: use Low Energy Voxel High Energy Image Voxel is: Far from metal. Similar intensities on the corresponding Low & High Energy Images. Artifact is Unlikely or Minimal: Weighting Factor Favors Low Energy Voxel because of generally better quality.

13 Low Energy Image Calculated Blending Map Black: use High Energy Voxel White: use Low Energy Voxel High Energy Image Voxel is: Near metal. Very different intensities on Low & High Energy Images. Artifact is Likely and Severe Weighting Factor Favors High Energy Voxel because generally more robust near metal.

14 Weighting Factors (W) Weighting Low Factor Energy (70 Calculation kev) Favored when W is near 1 High Energy (130 kev) Favored when W is near 0 Hybrid Monochromatic Images Reduce Artifacts eposter #: ep-151

15 Evaluation: Blinded Online Survey of Radiologist Rank by Order of Preference N = 22 Respondents

16 Survey Results: Condorcet Method

17 Survey Results: Pick-Top-Choice Method

18 Conclusions Most readers preferred Blend+Proximity reconstruction algorithm over any other single energy level (70, 100, 130 kev). This algorithm achieves high suppression of metallic artifacts while preserving contrast of soft tissues and good conspicuity of iodinated contrast. Additional research is desired, using: Images containing tumors or other pathologies. Utilizing even lower energy levels at approximately 40 kev 40 kev has been previously suggested as optimal for visualization of various tumor types2, 4, 5

19 References 1. Yu L, Leng S, Mccollough CH. Dual-energy CT-based monochromatic imaging. AJR Am J Roentgenol. 2012;199(5 Suppl):S9-S Bhosale P, Le O, Balachandran A, Fox P, Paulson E, Tamm E. Quantitative and Qualitative Comparison of Single-Source Dual-Energy Computed Tomography and 120-kVp Computed Tomography for the Assessment of Pancreatic Ductal Adenocarcinoma. J Comput Assist Tomogr Meier A, Wurnig M, Desbiolles L, Leschka S, Frauenfelder T, Alkadhi H. Advanced virtual monoenergetic images: improving the contrast of dual-energy CT pulmonary angiography. Clin Radiol Shuman WP, Green DE, Busey JM, et al. Dual-energy liver CT: effect of monochromatic imaging on lesion detection, conspicuity, and contrast-to-noise ratio of hypervascular lesions on late arterial phase. AJR Am J Roentgenol. 2014;203(3): Lam S, Gupta R, Levental M, Yu E, Curtin HD, et al. Optimal Virtual Monochromatic Images for Evaluation of Normal Tissues and Head and Neck Cancer Using Dual-Energy CT. AJNR Am J Neuroradiol Aug;36(8): Partha Dasgupta and Eric Maskin, The Fairest Vote of All. Scientific American March 2004.

20 The End Scan to Add Contact: Michal Kulon, MD