scholarly journals Manual versus automated image fusion of real-time ultrasonography and MR/CT images for radiofrequency ablation of hepatic tumors: Results of a randomized prospective trial

2020 ◽  
Author(s):  
Moon Hyung Choi ◽  
JOON-IL CHOI ◽  
Young Joon Lee
2017 ◽  
Vol 43 ◽  
pp. S162
Author(s):  
Moon Hyung Choi ◽  
Joon Il Choi ◽  
Young Joon Lee ◽  
Sung Eun Rha ◽  
Jae Young Byun

2017 ◽  
Vol 152 (5) ◽  
pp. S1173
Author(s):  
Nobuyuki Toshikuni ◽  
Yasuhiro Matsue ◽  
Kazuaki Ozaki ◽  
Nobuhiko Hayashi ◽  
Mutsumi Tsuchishima ◽  
...  

Author(s):  
M. J. van Amerongen ◽  
P. Mariappan ◽  
P. Voglreiter ◽  
R. Flanagan ◽  
S. F. M. Jenniskens ◽  
...  

Abstract Objectives Radiofrequency ablation (RFA) can be associated with local recurrences in the treatment of liver tumors. Data obtained at our center for an earlier multinational multicenter trial regarding an in-house developed simulation software were re-evaluated in order to analyze whether the software was able to predict local recurrences. Methods Twenty-seven RFA ablations for either primary or secondary hepatic tumors were included. Colorectal liver metastases were shown in 14 patients and hepatocellular carcinoma in 13 patients. Overlap of the simulated volume and the tumor volume was automatically generated and defined as positive predictive value (PPV) and additionally visually assessed. Local recurrence during follow-up was defined as gold standard. Sensitivity and specificity were calculated using the visual assessment and gold standard. Results Mean tumor size was 18 mm (95% CI 15–21 mm). Local recurrence occurred in 5 patients. The PPV of the simulation showed a mean of 0.89 (0.84–0.93 95% CI). After visual assessment, 9 incomplete ablations were observed, of which 4 true positives and 5 false positives for the detection of an incomplete ablation. The sensitivity and specificity were, respectively, 80% and 77% with a correct prediction in 78% of cases. No significant correlation was found between size of the tumor and PPV (Pearson Correlation 0.10; p = 0.62) or between PPV and recurrence rates (Pearson Correlation 0.28; p = 0.16). Conclusions The simulation software shows promise in estimating the completeness of liver RFA treatment and predicting local recurrence rates, but could not be performed real-time. Future improvements in the field of registration could improve results and provide a possibility for real-time implementation.


2017 ◽  
Vol 58 (11) ◽  
pp. 1349-1357 ◽  
Author(s):  
Dong Ik Cha ◽  
Min Woo Lee ◽  
Ah Yeong Kim ◽  
Tae Wook Kang ◽  
Young-Taek Oh ◽  
...  

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3–16 s] vs. 32 s [range, 21–38 s]; P < 0.001] and complete (median, 34.0 s [range, 26–66 s] vs. 47.5 s [range, 32–90]; P = 0.001] image fusion. Registration error of Positioning auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0–84.6 mm] vs. 18.2 mm [6.7–73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7–9.9 mm] vs. 5.8 mm [range, 2.0–13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2–3] vs. 1 [range, 1–2]; P = 0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.


2009 ◽  
Author(s):  
J. Bieberstein ◽  
C. Schumann ◽  
A. Weihusen ◽  
T. Boehler ◽  
S. Wirtz ◽  
...  

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