Optical flow and image segmentation analysis for noninvasive precise mapping of microwave thermal ablation in X-ray CT scans - <i>ex vivo</i> study

Ziv, Omri; Goldberg, S. Nahum; Nissenbaum, Yitzhak; Weiss, Noam; Azhari, Haim

doi:10.1080/02656736.2017.1375160

Cited by 3 publications

(11 citation statements)

References 46 publications

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“…Finally, it should be emphasized that, despite the fact that the method is noninvasive and applied in vivo, the obtained < 2 mm accuracy is comparable to the inter-observer variability achieved by manual tracing under ex vivo conditions (1.35 AE 0.91 [mm]). 17 Hence indicating the clinical potential of the method (see also for comparison 32 ). Potentially, the accuracy can be further improved by using higher quality images with better resolution and contrast and by fine tuning the algorithm parameters.…”

Section: Discussionmentioning

confidence: 90%

“…From that point on, the algorithm titled Radial Optical Flow (ROF) described in details in Ref. [17] was applied. Accordingly, the following steps were implemented: All images were converted into a polar presentation positioned around the needle central point, (where the radial coordinate corresponds to the distance from the needle central point) and using 10° angular sectors. The Lucas–Kanade optical flow method 31 was applied onto every two temporally consecutive scans of the same slice.…”

Section: Methodsmentioning

confidence: 99%

“…From that point on, the algorithm titled Radial Optical Flow (ROF) described in details in Ref. [17] was applied. Accordingly, the following steps were implemented:…”

Section: D Estimating the Ablation Zone Sizementioning

confidence: 99%

“…The potential ability to map the ablated zone by CT has been established on ex-vivo experiments previously. 17 Those experiments demonstrated the capability of obtaining detailed contouring of the ablated zone which is superior to mere estimation of the average ablation radius. [18][19][20] Such detailed contouring is highly important in thermal ablation procedures of tumors in problematic locations and in proximity to valuable and sensitive organs.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, discordances and inhomogeneity between temperature mapping and actual thermal damage suggest that this approach may be less beneficial than direct segmentation of the ablated tissue area, that is, contouring the area within the CT image which presents irreversible tissue damage. The potential ability to map the ablated zone by CT has been established on ex‐vivo experiments previously 17 . Those experiments demonstrated the capability of obtaining detailed contouring of the ablated zone which is superior to mere estimation of the average ablation radius 18–20 .…”

Section: Introductionmentioning

confidence: 99%

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In vivo noninvasive three‐dimensional (3D) assessment of microwave thermal ablation zone using non‐contrast‐enhanced x‐ray CT

et al. 2020

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Purpose: To develop an image processing methodology for noninvasive three-dimensional (3D) quantification of microwave thermal ablation zones in vivo using x-ray computed tomography (CT) imaging without injection of a contrast enhancing material. Methods: Six microwave (MW) thermal ablation procedures were performed in three pigs. The ablations were performed with a constant heating duration of 8 min and power level of 30 W. During the procedure images from sixty 1 mm thick slices were acquired every 30 s. At the end of all ablation procedures for each pig, a contrast-enhanced scan was acquired for reference. Special algorithms for addressing challenges stemming from the 3D in vivo setup and processing the acquired images were prepared. The algorithms first rearranged the data to account for the oblique needle orientation and for breathing motion. Then, the gray level variance changes were analyzed, and optical flow analysis was applied to the treated volume in order to obtain the ablation contours and reconstruct the ablation zone in 3D. The analysis also included a special correction algorithm for eliminating artifacts caused by proximal major blood vessels and blood flow. Finally, 3D reference reconstructions from the contrast-enhanced scan were obtained for quantitative comparison. Results: For four ablations located >3 mm from a large blood vessel, the mean dice similarity coefficient (DSC) and the mean absolute radial discrepancy between the contours obtained from the reference contrast-enhanced images and the contours produced by the algorithm were 0.82 AE 0.03 and 1.92 AE 1.47 mm, respectively. In two cases of ablation adjacent to large blood vessels, the average DSC and discrepancy were: 0.67 AE 0.6 and 2.96 AE 2.15 mm, respectively. The addition of the special correction algorithm utilizing blood vessels mapping improved the mean DSC and the mean absolute discrepancy to 0.85 AE 0.02 and 1.19 AE 1.00 mm, respectively. Conclusions: The developed algorithms provide highly accurate detailed contours in vivo (average error < 2.5 mm) and cope well with the challenges listed above. Clinical implementation of the developed methodology could potentially provide real time noninvasive 3D accurate monitoring of MW thermal ablation in-vivo, provided that the radiation dose can be reduced.

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Section: Discussionmentioning

confidence: 90%

Section: Methodsmentioning

confidence: 99%

“…From that point on, the algorithm titled Radial Optical Flow (ROF) described in details in Ref. [17] was applied. Accordingly, the following steps were implemented:…”

Section: D Estimating the Ablation Zone Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In vivo noninvasive three‐dimensional (3D) assessment of microwave thermal ablation zone using non‐contrast‐enhanced x‐ray CT

et al. 2020

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Feature‐based automated segmentation of ablation zones by fuzzy c‐mean clustering during low‐dose computed tomography

Bedoya

White

et al. 2020

Medical Physics

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Intra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT). Methods: Low-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice's coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference. Results: Automatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 AE 0.02; Manu.-Path. = 0.76 AE 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 AE 3.2%, 11.1 AE 3.2% and 16.2 AE 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 AE 16.8%, 13.0 AE 9.8% and 21.8 AE 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm 2 , but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm 2. Conclusion: Fuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.

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Radiofrequency and microwave ablation in a porcine liver model: non-contrast CT and ultrasound radiologic-pathologic correlation

Ziemlewicz

Hinshaw

Lubner

et al. 2020

International Journal of Hyperthermia

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Purpose: The goal of this study was to compare intra-procedural radiofrequency (RF) and microwave ablation appearance on non-contrast CT (NCCT) and ultrasound to the zone of pathologic necrosis. Materials and methods: Twenty-one 5-min ablations were performed in vivo in swine liver with (1) microwave at 140 W, (2) microwave at 70 W, or (3) RF at 200 W (n ¼ 7 each). CT and US images were obtained simultaneously at 1, 3, and 5 min during ablation and 2, 5, and 10 min post-ablation. Each ablation was sectioned in the plane of the ultrasound image and underwent vital staining to delineate cellular necrosis. CT was reformatted to the same plane as the ultrasound transducer and transverse diameters of gas and hypoechoic/hypoattenuating zones at each time point were measured. CT, ultrasound and gross pathologic diameter measurements were compared using Student's t-tests and linear regression. Results: Visible gas and the hypoechoic zone on US images were more predictive of the pathologic ablation zone than on NCCT images (p < 0.05). The zone of necrosis was larger than the zone of visible gas on US (mean 3.2 mm for microwave, 6.4 mm for RF) and NCCT (7.6 mm microwave, 13.9 mm RF) images (p < 0.05). The zone of visible gas and hypoechoic zone on US are more predictive of pathology with microwave ablations when compared with RF ablations (p < 0.05). Conclusion: When evaluating images during energy delivery, US is more accurate than CT and microwave-more predictable than RF-ablation based on correlation with in-plane pathology.

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Optical flow and image segmentation analysis for noninvasive precise mapping of microwave thermal ablation in X-ray CT scans - ex vivo study

Cited by 3 publications

References 46 publications

In vivo noninvasive three‐dimensional (3D) assessment of microwave thermal ablation zone using non‐contrast‐enhanced x‐ray CT

In vivo noninvasive three‐dimensional (3D) assessment of microwave thermal ablation zone using non‐contrast‐enhanced x‐ray CT

Feature‐based automated segmentation of ablation zones by fuzzy c‐mean clustering during low‐dose computed tomography

Radiofrequency and microwave ablation in a porcine liver model: non-contrast CT and ultrasound radiologic-pathologic correlation

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