In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients. (C) 2019 Elsevier B.V. All rights reserved.

PURPOSE:Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle. METHODS:We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins. RESULTS:Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%. CONCLUSIONS:Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

Introduction: Accurate needle placement is crucial in image-guided needle interventions. A targeting error may be introduced due to undesired needle deflection upon insertion through tissue, caused by e.g. patient breathing, tissue heterogeneity, or asymmetric needle tip geometries. This paper aims to quantify needle deflection in thermal ablation procedures of liver tumors by means of a CT image analysis. Methods: Needle selection was done by using all clinical CT data that were made during thermal ablation procedures of the liver, ranging from 2008-2016, in the Erasmus MC, the Netherlands. The 3D needle shape was reconstructed for all selected insertions using manual segmentation. Subsequently, a straight line was computed between the entry point of the needle into the body and the needle tip. The maximal perpendicular distance between this straight line and the actual needle was used to calculate needle deflection. Results: In total, 365 needles were included in the analysis ranging from 14G to 17G in diameter. Average needle insertion depth was 95mm (range: 32 mm – 182 mm). Needle deflection was on average 1.3 mm (range: 0.0 mm – 6.5 mm). 54% of the needles (n=196) had a needle deflection of more than one millimeter, whereas 7% of the needles (n=25) showed a large needle deflection of more than three millimeters. Conclusions: Needle deflection in interventional radiology occurs in more than half of the needle insertions. Therefore, deflection should be taken into account when performing procedures and when defining design requirements for novel needles. Further, needle insertion models need to be developed that account for needle deflection.

CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distance between corresponding landmark of 6.4 mm vs 10.2 mm). The registration of the preoperative data with the needle image in 16 cases yielded a DICE of 90.1% and a mean surface distance of 5.2 mm. The remaining three cases with DICE smaller than 80% were classified as unsuccessful registration. The results show that this is promising tool for liver image registration in interventional radiology.

PURPOSE:In image-guided radio frequency ablation for liver cancer treatment, pre- and post-interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post-interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre-interventional image to the post-interventional image.METHODS:The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre-interventional and post-interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed.RESULTS:The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non-rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors' CT images.CONCLUSIONS:This method is thus a promising tool to assess the success of RFA liver cancer treatment.

The previous manuscript1 has the authors grouped by institution in the final paper, yielding an incorrect order of the authors (Luu, Niessen, van Walsum, Klink, Moelker). The correct order is Ha Manh Luu, Camiel Klink, Wiro Niessen, Adriaan Moelker, Theo van Walsum.

Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p < 0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

Poster: "ECR 2015 / C-2024 / Real-time three-dimensional ultrasound user interface for TIPS planning" by: "C. F. Cuijpers1, C. Klink2, E. Varga2, P. J. Stappers1, Y. Song1; 1Delft/NL, 2Rotterdam/NL"

In this work we describe a 4D registration method for on the fly stabilization of ultrasound volumes for improving image guidance for transjugular intrahepatic portosystemic shunt (TIPS) interventions. The purpose of the method is to enable a continuous visualization of the relevant anatomical planes (determined in a planning stage) in a free breathing patient during the intervention. This requires registration of the planning information to the interventional images, which is achieved in two steps. In the first step tracking is performed across the streaming input. An approximate transformation between the reference image and the incoming image is estimated by composing the intermediate transformations obtained from the tracking. In the second step a subsequent registration is performed between the reference image and the approximately transformed incoming image to account for the accumulation of error. The two step approach helps in reducing the search range and is robust under rotation. We additionally present an approach to initialize and verify the registration. Verification is required when the reference image (containing planning information) is acquired in the past and is not part of the (interventional) 4D ultrasound sequence. The verification score will help in invalidating the registration outcome, for instance, in the case of insufficient overlap or information between the registering images due to probe motion or loss of contact, respectively. We evaluate the method over thirteen 4D US sequences acquired from eight subjects. A graphics processing unit implementation runs the 4D tracking at 9 Hz with a mean registration error of 1.7 mm.

In this paper we present a rigid registration approach for 4D ultrasound (US) datasets, where images are registered over time. The 3D registration approach preceding the 4D registration consists of two main steps -block-matching and outlier rejection. The outlier rejection step removes the spurious matchings' from the block-matching module and ensures inverse consistency. For 4D registration, we perform registration of consecutive US volumes over the time series. Transformation between any two frames is estimated by taking the product of all the intermediate transforms. To avoid accumulation of error over the series of transformations, a long range feedback mechanism is proposed. A mean total registration error of 1 mm is achieved across six 4D ultrasound sequences of human liver with an execution speed of 10 Hz.

Real-time 3D US has potential for image guidance in minimally invasive liver interventions. However, motion caused by patient breathing makes it hard to visualize a localized area, and to maintain alignment with pre-operative information. In this work we develop a fast affine registration framework to compensate in real-time for liver motion/displacement due to breathing. The affine registration of two consecutive ultrasound volumes in time is performed using block-matching. For a set of evenly distributed points in one volume and their correspondences in the other volume, we propose a robust outlier rejection method to reject false matches. The inliers are then used to determine the affine transformation. The approach is evaluated on 13 4D ultrasound sequences acquired from 8 subjects. For 91 pairs of 3D ultrasound volumes selected from these sequences, a mean registration error of 1.8mm is achieved. A graphics processing unit (GPU) implementation runs the 3D US registration at 8 Hz.

In interventional radiology, inserting a needle from the skin to the target lesion is a challenging task, because the physician has to estimate the spatial orientation of the needle based on a sequence of two-dimensional radiology images. We wanted to know what kind of visual aids could support the physician in better estimating the orientation of the needle and reduce task load. Therefore, an experiment was conducted in which user interfaces with different visual aids were tested with physicians. Results show that the appropriate combination of visual aids helps to estimate the orientation of the needle more accurately as well as to reduce task load as compared to current clinical practice.

One of the most technically challenging procedure in interventional radiology is a transjugular intrahepatic portosystemic shunt (TIPS) placement. The main problem is the limited image guidance while navigating. The physician basically punctures blind through the liver into the target portal vein, leading to many unsuccessful punctures and unnecessary risks before access to the portal vein is gained. To be able to improve guidance, developers have to be sure to use the most promising image modality available. This paper compares the available modalities for TIPS to various criteria to find which modality is the most suitable to improve the success of the puncture. Results showed that, even though many user interface improvements are required, real-time three-dimensional ultrasound has the most potential to improve the puncture in the future. The study emphasizes the importance of thorough technology analysis before developing medical devices.

We present an evaluation of five diffusion filters for liver vessel enhancement in 3D CTA datasets of the liver. 3D CTA liver images are generally noisy, with limited contrast between vessels and parenchyma, especially for the small vessels. We investigate the performance of five (an)isotropic diffusions filters: Regular Perona-Malik, Coherence-Enhancing Diffusion, Edge-Enhancing Diffusion, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion on a set of 14 abdominal CTA clinical datasets. The evaluation is based on signal to noise improvement. A parameter optimization is performed on 7 training images, after which the optimal versions of the filters are compared on 7 test images. The results demonstrate that all the diffusion filters improve SNR of the images and Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion gives the largest increase in SNR.