X. Allen Li. Disclosure. DECT: What, how and Why Why dual-energy CT (DECT)? 7/30/2018. Improving delineation and response assessment using DECT in RT
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1 Improving delineation and response assessment using DECT in RT X. Allen Li Medical College of Wisconsin MO-A-DBRA-1, AAPM, July 30 th, 2018 Disclosure Research funding support: Siemens Healthineers Elekta DECT: What, how and Why Why dual-energy CT (DECT)? Material differentiation by dual energy CT: initial experience DOI: /s
2 Dual-Energy CT (DECT) By using two different energies, DECT allows material decomposition based on energy-dependent attenuation profiles of specific materials. Comparing to conventional single-energy CT, DECT allows additional comprehensive post-processing to generate a variety of other images: Virtual mono-energetic decomposition image, enabling a desired energy of kev to achieve better contrast) Virtual material-specific iodine images, enabling differentiation of hypoattenuating tumors from hypo- or hyperattenuating cysts (e.g., pancreatic masses and peritoneal disease, better defining tumor targets) Clinical DECT scanner introduced in Initially for diagnostic applications: differentiation of urinary stones imaging of pulmonary embolism neuro imaging or differentiation of pulmonary nodules Applications of DECT in RT Improved dose calculation accuracy (brachytherapy, proton) Metal artifact reduction Tissue characterization Improved delineation Enhanced response assessment Eliminating non-contrast scan when a contrast scan is acquired during simulation.. Hudobivnik MP 43:495 2
3 6 FH/MCW Rad Onc Siemens Drive: dual source DECT RT simulation Summed dose equals a standard CT Rapid acquisition minimizes motion effects No motion between the two scans Siemens Confidence: single source dual spiral DECT (2 systems) RT simulation Siemen Definition AS: single source dual spiral DECT (3 systems) Two for RT simulation One as CT-on rails for daily IGRT and response assessment during RT delivery AAPM 2018 SU-E-KDBRB1-4: G Noid, A Tai, D Schott, D Prah, N Mistry, J Robbins, XA Li. Advantages of Dual-Source Dual-Energy CT for Radiation Therapy Planning TH-AB-DBRB-1: G Noid, D Schott, T Schmidt, A Tai, XA Li. Optimal Energy of Virtual Monoenergetic Imaging From Dual-Energy CT for Target Delineation and Radiation Response Assessment Reducing metal artifacts Virtual Monoenergetic images can restore image quality Higher energies reduce beam hardening CT of 120 kvp and MEI of DECT with and without imar 3
4 Reducing metal artifacts 120 kvp 190 kev imar Virtual Monoenergetic images can restore image quality Higher energies reduce beam hardening Prostate cancer CT with 120 kvp and 190 kev MEI + imar Reducing image artifacts 120 kvp 140 kvp 190 kev Large mantle field with arms down imaged with 120 kvp, 140 kvp, and 190 kev MEI motion blurring reduction Sequential DECT Simultaneous DECT reduces motion related blurring effects Reduces uncertainty in segmentation Simultaneous DECT Pancreatic cancer (celiac) imaged with Sequential & Simultaneous DECT 4
5 motion blurring reduction Sequential DECT Simultaneous DECT reduces motion related blurring effects Reduces uncertainty in segmentation Simultaneous DECT Pancreatic cancer (celiac) imaged with Sequential & Simultaneous DECT 120 kvp non-contrast 120 kvp w contrast Registration error between contrast and non-contrast CTs Virtual unenhanced from DECT 50 kev No registration error, eliminating non-contrast scan Improving target delineation for pre-operative RT of breast cancer Subtracted 120 kev 40 kev 120 kev 40 kev Virtual Monoenergetic images enhance soft tissue contrast Subtracted images enhance further Invasive ductal carcinoma 5
6 Contrast (HU) 7/30/2018 Improving target delineation for pre-operative RT of breast cancer KeV T1 120 kev 40 kev pt 1 pt 2 pt 3 pt 4 pt 5 pt x-ray mono-energy (kev) Image contrast enhancement: Liver 120 kvp 120 kev 40 kev 120 kev 40 kev Improved target delineation: Thymic carcinoma Subtracted 120 kev 40 kev 120 kev Pet 40 kev 6
7 Pancreatic cancer 120keV-40keV 120 kev 40 kev 70 kev DECT with IV bolus I map Post-processing to quantify the concentration of Iodine Mapping vasculature PET Left tonsil SCC with nodal involvement Mapping Vasculature VNC I map Adenocarcinoma of pancreatic tail 70 kev Pet I map 7
8 Tissue composition Fat map 70 kev Fat maps quantify the adipose tissue present in a voxel via multi material decomposition Designed to work with liver Show potential to improve target delineation in brain Meningioma imaged with Fat map and 70 kev Tissue Differentiation g: kurtosis tumor pancreas a: 40 kev b: 50 kev c: 60 kev d: 70 kev e: 80 kev f: 90 kev DECT of a pancreatic cancer patient. Difference in kurtosis between the tumor in violet and the pancreatic stroma in cyan increases as X-ray energy decreases. DECT for response assessment 8
9 Pancreatic tumor during chemo-rt Correlation with pathological response P=0.046 P= patients P=0.042 A DECT for treatment response Pt# Target (HU) Aorta (HU) Target location pancreatic head pancreatic head pancreatic neck & body pancreatic head ampulla vater & pancretic invasion pancreatic head pancreatic head pancreatic head pancreatic head pancreatic head B A The change in the mean CTN between the first and last week of treatment for the 10 pancreas patients in the study as measured at 120 kvp and 40 kev. B. A table summarizing the mean CTN change (HU) in the target and aorta along with the target location for each patient 9
10 Pancreas DECT amplify treatment response signal DECT for pancreas treatment response MRI-based DECT-guided pre-operative RT PI: Adam Currey 10
11 DECT for treatment response a: 40 kev b: 50 kev c: 60 kev g: kurtosis last first d: 40 kev e: 50 kev f: 60 kev (a-c) first fraction (d-f) last fraction Arrow: tumor cellularity(%) ΔCTN40(HU) ΔCTN120(HU) Δcontrol(HU) Breast Treatment Response Patient # Summary The use of DECT improves RT planning Increase of soft-tissue contract using virtual monoenergetic images Reduction of image artifacts Mapping of vasculature Quantification of tissue composition Minimization of motion blurring The use of DECT should be the standard practice. 11
12 Acknowledgements George Noid, Ph.D An Tai, Ph.D Siemens: Nilesh Mistry, Ph.D for Technical support Adam Currey, MD Beth Erickson, MD Jared Robbins, MD Doug Prah, Ph.D Eric Paulson, Ph.D Diane Schott, Ph.D Ying Zhang, Ph.D Tia Plautz, Ph.D Taly Schmidt, Ph.D Yu Liu, PhD 12
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