Objective To quantitatively compare and determine the best pancreatic tumor contrast to noise ratio (CNR) in different dual-energy derived datasets. Methods In this retrospective, single center study, 16 patients (9 m...Objective To quantitatively compare and determine the best pancreatic tumor contrast to noise ratio (CNR) in different dual-energy derived datasets. Methods In this retrospective, single center study, 16 patients (9 male, 7 female, average age 59.4±13.2 years) with pathologically diagnosed pancreatic cancer were enrolled. All patients received an abdominal scan using a dual source CT scanner 7 to 31 days before biopsy or surgery. After injection of iodine contrast agent, arterial and pancreatic parenchyma phase were scanned consequently, using a dual-energy scan mode (100 kVp/230 mAs and Sn 140 kVp/178 mAs) in the pancreatic parenchyma phase. A series of derived dual-energy datasets were evaluated including non-liner blending (non-linear blending width 0-500 HU; blending center -500 to 500 HU), mono-energetic (40-190 keV), 100 kVp and 140 kVp. On each datasets, mean CT values of the pancreatic parenchyma and tumor, as well as standard deviation CT values of subcutaneous fat and psoas muscle were measured. Regions of interest of cutaneous fat and major psoas muscle of 100 kVp and 140 kVp images were calculated. Best CNR of subcutaneous fat (CNR F ) and CNR of the major psoas muscle (CNR M ) of non-liner blending and mono-energetic datasets were calculated with the optimal mono-energetic keV setting and the optimal blending center/width setting for the best CNR. One Way ANOVA test was used for comparison of best CNR between different dual-energy derived datasets. Results The best CNR F (4.48±1.29) was obtained from the non-liner blending datasets at blending center -16.6±103.9 HU and blending width 12.3±10.6 HU. The best CNR F (3.28±0.97) was obtained from the mono-energetic datasets at 73.3±4.3 keV. CNR F in the 100 kVp and 140 kVp were 3.02±0.91 and 1.56±0.56 respectively. Using fat as the noise background, all of these images series showed significant differences (P<0.01) except best CNR F of mono-energetic image sets vs. CNR F of 100 kVp image (P=0.460). Similar results were found using muscle as the no展开更多
Objective This study aimed to explore the feasibility of enhancing image quality in computed tomography(CT) pulmonary angiography (CTPA) and reducing radiation dose using the nonlinear blending (NLB)technique of dual-...Objective This study aimed to explore the feasibility of enhancing image quality in computed tomography(CT) pulmonary angiography (CTPA) and reducing radiation dose using the nonlinear blending (NLB)technique of dual-energy CT.Methods A total of 61 patients scheduled for CTPA were enrolled, and 30 patients underwent dual-energyscanning. Nonlinear blending images (NLB group) and three groups of linear blending images (LB group,80 kV group, and 140 kV group) were reconstructed after scanning;31 patients underwent single-energyscanning (120 kV group). The CT values and standard deviations of the pulmonary trunk, left and rightpulmonary arteries, and ipsilateral back muscle at the bifurcation level of the left and right pulmonaryarteries were measured. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the fivegroups were calculated. The subjective image quality of the five groups was assessed. The radiation dosesof the dual- and single-energy groups were recorded and calculated.Results The CNR and SNR values of blood vessels in the NLB group were significantly higher than thosein the LB, 140 kV, and 80 kV groups (CNR of pulmonary artery trunk: t = 3.50, 4.06, 7.17, all P < 0.05;SNRof pulmonary trunk: t = 3.76, 4.71, 6.92, all P < 0.05). There were no statistical differences in the CNR andSNR values between the NLB group and 120 kV group (P > 0.05). The effective radiation dose of the dualenergygroup was lower than that of the single-energy group (t = –4.52, P < 0.05). The subjective scores ofimages in the NLB group were the highest (4.28 ± 0.74).Conclusion The NLB technique of dual-energy CT can improve the image quality of CTPA and reducethe radiation dose, providing more reliable imaging data for the clinical diagnosis of pulmonary embolism.展开更多
文摘Objective To quantitatively compare and determine the best pancreatic tumor contrast to noise ratio (CNR) in different dual-energy derived datasets. Methods In this retrospective, single center study, 16 patients (9 male, 7 female, average age 59.4±13.2 years) with pathologically diagnosed pancreatic cancer were enrolled. All patients received an abdominal scan using a dual source CT scanner 7 to 31 days before biopsy or surgery. After injection of iodine contrast agent, arterial and pancreatic parenchyma phase were scanned consequently, using a dual-energy scan mode (100 kVp/230 mAs and Sn 140 kVp/178 mAs) in the pancreatic parenchyma phase. A series of derived dual-energy datasets were evaluated including non-liner blending (non-linear blending width 0-500 HU; blending center -500 to 500 HU), mono-energetic (40-190 keV), 100 kVp and 140 kVp. On each datasets, mean CT values of the pancreatic parenchyma and tumor, as well as standard deviation CT values of subcutaneous fat and psoas muscle were measured. Regions of interest of cutaneous fat and major psoas muscle of 100 kVp and 140 kVp images were calculated. Best CNR of subcutaneous fat (CNR F ) and CNR of the major psoas muscle (CNR M ) of non-liner blending and mono-energetic datasets were calculated with the optimal mono-energetic keV setting and the optimal blending center/width setting for the best CNR. One Way ANOVA test was used for comparison of best CNR between different dual-energy derived datasets. Results The best CNR F (4.48±1.29) was obtained from the non-liner blending datasets at blending center -16.6±103.9 HU and blending width 12.3±10.6 HU. The best CNR F (3.28±0.97) was obtained from the mono-energetic datasets at 73.3±4.3 keV. CNR F in the 100 kVp and 140 kVp were 3.02±0.91 and 1.56±0.56 respectively. Using fat as the noise background, all of these images series showed significant differences (P<0.01) except best CNR F of mono-energetic image sets vs. CNR F of 100 kVp image (P=0.460). Similar results were found using muscle as the no
基金Supported by a grant from the Science and Technology Plan of Sichuan Province(No.2021YFS0225)the Science and Technology Plan of Chengdu(No.2021-YF05-01507-SN).
文摘Objective This study aimed to explore the feasibility of enhancing image quality in computed tomography(CT) pulmonary angiography (CTPA) and reducing radiation dose using the nonlinear blending (NLB)technique of dual-energy CT.Methods A total of 61 patients scheduled for CTPA were enrolled, and 30 patients underwent dual-energyscanning. Nonlinear blending images (NLB group) and three groups of linear blending images (LB group,80 kV group, and 140 kV group) were reconstructed after scanning;31 patients underwent single-energyscanning (120 kV group). The CT values and standard deviations of the pulmonary trunk, left and rightpulmonary arteries, and ipsilateral back muscle at the bifurcation level of the left and right pulmonaryarteries were measured. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the fivegroups were calculated. The subjective image quality of the five groups was assessed. The radiation dosesof the dual- and single-energy groups were recorded and calculated.Results The CNR and SNR values of blood vessels in the NLB group were significantly higher than thosein the LB, 140 kV, and 80 kV groups (CNR of pulmonary artery trunk: t = 3.50, 4.06, 7.17, all P < 0.05;SNRof pulmonary trunk: t = 3.76, 4.71, 6.92, all P < 0.05). There were no statistical differences in the CNR andSNR values between the NLB group and 120 kV group (P > 0.05). The effective radiation dose of the dualenergygroup was lower than that of the single-energy group (t = –4.52, P < 0.05). The subjective scores ofimages in the NLB group were the highest (4.28 ± 0.74).Conclusion The NLB technique of dual-energy CT can improve the image quality of CTPA and reducethe radiation dose, providing more reliable imaging data for the clinical diagnosis of pulmonary embolism.
