Radiofrequency ablation (RFA) is becoming a standard procedure for minimally invasive tumor treatment in clinical practice. Due to its common technical procedure, low complication rate, and low cost, RFA has become an alternative to surgical resection in the liver. To evaluate the therapy success of RFA, thorough follow-up imaging is essential. Conventionally, shape, size, and position of tumor and coagulation are visually compared in a side-by-side manner using pre- and post-interventional images. To objectify the verification of the treatment success, a novel software assistant allowing for fast and accurate comparison of tumor and coagulation is proposed.
In this work, the clinical value of the proposed assessment software is evaluated. In a retrospective clinical study, 39 cases of hepatic tumor ablation are evaluated using the prototype software and conventional image comparison by four radiologists with different levels of experience. The cases are randomized and evaluated in two sessions to avoid any recall-bias. Self-confidence of correct diagnosis (local recurrence vs. no local recurrence) on a six-point scale is given for each case by the radiologists. Sensitivity, specificity, positive and negative predictive values as well as receiver operating curves are calculated for both methods. It is shown that the software-assisted method allows physicians to correctly identify local tumor recurrence with a higher percentage than the conventional method (sensitivity: 0.6 vs. 0.35), whereas the percentage of correctly identified successful ablations is slightly reduced (specificity: 0.83 vs. 0.89).
Image-guided radiofrequency ablation (RFA) is becoming a standard procedure for minimally invasive tumor
treatment in clinical practice. To verify the treatment success of the therapy, reliable post-interventional assessment
of the ablation zone (coagulation) is essential. Typically, pre- and post-interventional CT images have to
be aligned to compare the shape, size, and position of tumor and coagulation zone. In this work, we present
an automatic workflow for masking liver tissue, enabling a rigid registration algorithm to perform at least as
accurate as experienced medical experts. To minimize the effect of global liver deformations, the registration is
computed in a local region of interest around the pre-interventional lesion and post-interventional coagulation
necrosis. A registration mask excluding lesions and neighboring organs is calculated to prevent the registration
algorithm from matching both lesion shapes instead of the surrounding liver anatomy. As an initial registration
step, the centers of gravity from both lesions are aligned automatically. The subsequent rigid registration method
is based on the Local Cross Correlation (LCC) similarity measure and Newton-type optimization. To assess the
accuracy of our method, 41 RFA cases are registered and compared with the manually aligned cases from four
medical experts. Furthermore, the registration results are compared with ground truth transformations based on
averaged anatomical landmark pairs. In the evaluation, we show that our method allows to automatic alignment
of the data sets with equal accuracy as medical experts, but requiring significancy less time consumption and
variability.
Image guided radiofrequency ablation (RFA) is becoming a standard procedure as a minimally invasive method
for tumor treatment in the clinical routine. The visualization of pathological tissue and potential risk structures
like vessels or important organs gives essential support in image guided pre-interventional RFA planning. In this
work our aim is to present novel visualization techniques for interactive RFA planning to support the physician
with spatial information of pathological structures as well as the finding of trajectories without harming vitally
important tissue. Furthermore, we illustrate three-dimensional applicator models of different manufactures
combined with corresponding ablation areas in homogenous tissue, as specified by the manufacturers, to enhance
the estimated amount of cell destruction caused by ablation. The visualization techniques are embedded in
a workflow oriented application, designed for the use in the clinical routine. To allow a high-quality volume
rendering we integrated a visualization method using the fuzzy c-means algorithm. This method automatically
defines a transfer function for volume visualization of vessels without the need of a segmentation mask. However,
insufficient visualization results of the displayed vessels caused by low data quality can be improved using local
vessel segmentation in the vicinity of the lesion. We also provide an interactive segmentation technique of liver
tumors for the volumetric measurement and for the visualization of pathological tissue combined with anatomical
structures. In order to support coagulation estimation with respect to the heat-sink effect of the cooling blood
flow which decreases thermal ablation, a numerical simulation of the heat distribution is provided.
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