Wen-Hui Huang1,2, Kai-Jie Jhan3, Ching-Ching Yang4,5. 1. Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan. 2. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan. 3. Department of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan. 4. Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan. 5. Department of Medical Research, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, Taiwan.
Abstract
INTRODUCTION: This study aimed to investigate the feasibility of generating pseudo dual-energy CT (DECT) from one 120-kVp CT by using convolutional neural network (CNN) to derive additional information for quantitative image analysis through phantom study. METHODS: Dual-energy scans (80/140 kVp) and single-energy scans (120 kVp) were performed for five calibration phantoms and two evaluation phantoms on a dual-source DECT scanner. The calibration phantoms were used to generate training dataset for CNN optimization, while the evaluation phantoms were used to generate testing dataset. A CNN model which takes 120-kVp images as input and creates 80/140-kVp images as output was built, trained, and tested by using Caffe CNN platform. An in-house software to quantify contrast enhancement and synthesize virtual monochromatic CT (VMCT) for CNN-generated pseudo DECT was implemented and evaluated. RESULTS: The CT numbers in 80-kVp pseudo images generated by CNN are differed from the truth by 11.57, 16.67, 13.92, 12.23, 10.69 HU for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. The corresponding results for 140-kVp CT are 3.09, 9.10, 7.08, 9.81, 7.59 HU. The estimates of iodine concentration calculated based on the proposed method are differed from the truth by 0.104, 0.603, 0.478, 0.698, 0.795 mg/ml for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. With regards to image quality enhancement, VMCT synthesized by using pseudo DECT shows the best contrast-to-noise ratio at 40 keV. CONCLUSION: In conclusion, the proposed method should be a practicable strategy for iodine quantification in contrast enhanced 120-kVp CT without using specific scanner or scanning procedure.
INTRODUCTION: This study aimed to investigate the feasibility of generating pseudo dual-energy CT (DECT) from one 120-kVp CT by using convolutional neural network (CNN) to derive additional information for quantitative image analysis through phantom study. METHODS: Dual-energy scans (80/140 kVp) and single-energy scans (120 kVp) were performed for five calibration phantoms and two evaluation phantoms on a dual-source DECT scanner. The calibration phantoms were used to generate training dataset for CNN optimization, while the evaluation phantoms were used to generate testing dataset. A CNN model which takes 120-kVp images as input and creates 80/140-kVp images as output was built, trained, and tested by using Caffe CNN platform. An in-house software to quantify contrast enhancement and synthesize virtual monochromatic CT (VMCT) for CNN-generated pseudo DECT was implemented and evaluated. RESULTS: The CT numbers in 80-kVp pseudo images generated by CNN are differed from the truth by 11.57, 16.67, 13.92, 12.23, 10.69 HU for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. The corresponding results for 140-kVp CT are 3.09, 9.10, 7.08, 9.81, 7.59 HU. The estimates of iodine concentration calculated based on the proposed method are differed from the truth by 0.104, 0.603, 0.478, 0.698, 0.795 mg/ml for syringes filled with iodine concentration of 2.19, 4.38, 8.75, 17.5, 35 mg/ml, respectively. With regards to image quality enhancement, VMCT synthesized by using pseudo DECT shows the best contrast-to-noise ratio at 40 keV. CONCLUSION: In conclusion, the proposed method should be a practicable strategy for iodine quantification in contrast enhanced 120-kVp CT without using specific scanner or scanning procedure.
Authors: Carlo Nicola De Cecco; Anna Darnell; Marco Rengo; Giuseppe Muscogiuri; Davide Bellini; Carmen Ayuso; Andrea Laghi Journal: AJR Am J Roentgenol Date: 2012-11 Impact factor: 3.959
Authors: Ji Yeon Kim; Seung Soo Lee; Jae Ho Byun; So Yeon Kim; Seong Ho Park; Young Moon Shin; Moon-Gyu Lee Journal: AJR Am J Roentgenol Date: 2013-08 Impact factor: 3.959