Accurate needle placement in deep-seated liver tumours can be difficult. In this work, we disclose two new manually controlled steerable needles for 17G radio-frequency ablation probe placement. The needles contain stylets with embedded compliant joints for active tip articulations, and concentric tubes for (curved-path) guidance. Needle steering was evaluated sequentially by intended users and in intended-use tissue types. Six interventional radiologists evaluated the needle in repeated ultrasound-guided steering tasks in liver-mimicking phantoms. Targets were located at a 100 mm depth and 20 mm lateral offset from the initial insertion line. The resulting mean absolute tip placement error was 1.0 ± 1.0 mm. Subsequently, steering-induced tissue damage was evaluated in fresh cirrhotic human liver explants. The surface area of puncture holes was estimated in scanned histology slides, using a connected-components analysis. The mean surface area was 0.26 ± 0.16 mm² after steering with a median radius of curvature of 0.7 × 10³ mm, versus 0.35 ± 0.15 mm² after straight-path insertions with the steerable needle and 0.15 ± 0.09 mm² after straight-path RFA probe insertions. The steering mechanisms proposed enable clinically relevant path corrections for 17G needles. Radiologists were quickly adept in curved-path RFA probe placement and the evaluation of histological tissue damage demonstrated a potentially safe use during liver interventions.

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Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images. Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles. Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers. Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches.

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This literature review was conducted to evaluate liver biopsy adequacy, including total core length (TCL), number of portal tracts (PT), fragmentation, and complication rates, as a function of needle type and gauge. A systematic electronic search was performed in the Web of Science and Google Scholar databases, according to the PRISMA statement. Eligible data, describing in vivo percutaneous ultrasound-guided human liver biopsy quality outcomes, were compared to adequacy criteria of the American Association for the Study of Liver Diseases (AASLD, TCL ≥ 20 mm, PT ≥ 11). An adequate mean number of PTs was found in 83% of biopsy needles assessed between 2012 and 2019, compared to 0% between 1998 and 2004. For TCL, this was 44% and 33%, respectively. Increasing the needle diameter enhanced TCL (result in 50% of included studies) and PT count (100%), and reduced fragmentation rates (75%), whereas no effect on pain or complications was found (83%). In total, five needle types achieved adequate PT counts, using 16 G (3×), 17 G (1×), or 18 G (1×) needles. Adequacy was reached using either a core needle biopsy (CNB, 3×) approach with one pass, or a fine needle aspiration (FNA, 2×) approach with two passes. The recommendations for biopsy adequacy can be met using 16/17 G FNA or 16/18 G CNB needles. Currently, many publications still present substandard liver biopsy quality outcomes. Although minimizing biopsy invasiveness is desirable, a decreased diameter or number of passes is ill-judged when reliability of biopsy outcomes is at stake.

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We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher image quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. In the optimization, we utilize transformed points to effectively compute a distance between rigid transformations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.

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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.

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Purpose: The purpose is to design and validate an anthropomorphic polyvinyl alcohol (PVA) liver phantom with respiratory motion to simulate needle-based interventions. Such a system can, for example, be used as a validation tool for novel needles. Methods: Image segmentations of CT scans of four patients during inspiration and expiration were used to measure liver and rib displacement. An anthropomorphic liver mold based on a CT scan was 3D printed and filled with 5% w/w PVA-to-water, undergoing two freeze–thaw cycles, in addition to a 3D-printed compliant rib cage. They were both held in place by a PVA abdominal phantom. A sinusoidal motion vector, based on the measured liver displacement, was applied to the liver phantom by means of a motion stage. Liver, rib cage and needle deflection were tracked by placing electromagnetic sensors on the phantom. Liver and rib cage phantom motion was validated by comparison with the CT images of the patients, whereas needle deflection was compared with the literature. Results: CT analysis showed that from the state of expiration to inspiration, the livers moved predominantly toward the right (mean: 2 mm, range: − 11 to 11 mm), anterior (mean: 15 mm, range: 9–21 mm) and caudal (mean: 16 mm, range: 6–24 mm) direction. The mechatronic design of the liver phantom gives the freedom to set direction and amplitude of the motion and was able to mimic the direction of liver motion of one patient. Needle deflection inside the phantom increased from 1.6 to 3.8 mm from the initial expiration state to inspiration. Conclusions: The developed liver phantom allows for applying different motion patterns and shapes/sizes and thus allows for patient-specific simulation of needle-based interventions. Moreover, it is able to mimic appropriate respiratory motion and needle deflection as observed in patients.

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Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.

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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. @en

Purpose: Accurate and precise needle placement is of utmost importance in interventional radiology. However, targeting can be challenging due to, eg, tissue motion and deformation. Steerable needles are a possible solution to overcome these challenges. The present work studied the clinical need for steerable needles. We aimed to answer three subquestions: 1) What are the current challenges in needle placement? 2) What are allowable needle placement errors? and 3) Do current needles need improvement and would steerable needles add clinical value?
Methods: A questionnaire was administered at the Annual Meeting of ­Cardiovascular and Interventional Radiology Society of Europe in 2016. In total, 153 respondents volunteered to fill out the survey, among them 125 (interventional) radiologists with experience in needle placement.
Results: 1) Current challenges in needle placement include patient-specific and technical factors. Movement of the target due to breathing makes it most difficult to place a needle (90%). 2) The mean maximal allowable needle placement error in targeted lesions is 2.7 mm. A majority of the respondents (85%) encounter unwanted needle bending upon insertion. The mean maximal encountered unwanted needle bending is 5.3 mm. 3) Needles in interventional radiology need improvement, eg, improved needle visibility and manipulability, according to 95% of the respondents. Added value for steerable needles in current interventions is seen by 93% of the respondents.
Conclusion: Steerable needles have the potential to add clinical value to radiologic interventions. The current data can be used as input for defining clinical design requirements for technical tools, such as steerable needles and navigation models, with the aim to improve needle placement in interventional radiology.@en

Multimodal image fusion for image guidance in minimally invasive liver interventions generally requires the registration of pre-operatively acquired images with interventional images of the patient. Whereas rigid registration approaches are fast and can be used in an interventional setting, the actual liver deformation may be nonrigid. The purpose of this paper is to assess the magnitude of nonrigid deformation of the liver between pre-operative and interventional CT images in the case of tumor ablations, over the full liver and over parts of the liver that match the volumes typically imaged by a 3D ultrasound transducer. We acquired 3D abdominal CT scans of 38 patients that had undergone the radiofrequency ablation of liver tumors, pre-operative CT images as well as intraoperative CT images. To determine the magnitude of liver deformation due to pose changes and respiration, we nonrigidly registered the pre-operative CT scan with the intraoperative CT scan. By fitting the deformation to a rigid transformation in the region of interest and computing the residual displacements, the nonrigid deformation part can be quantified. We performed quantifications over the complete liver, as well as for two volumes of interest representative of sub-xiphoidal and intercostal 3D ultrasound acquisitions. The results showed that a substantial amount of nonrigid deformation was found, and rotation of the patient's pose and deep inhalation caused significant liver deformation. Hence we concluded that nonrigid motion correction in the interventions should be taken into account.

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In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.

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Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.

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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.

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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.

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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.

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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.

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