Accuracy of implementing principles of fusion imaging in the follow up and surveillance of complex aneurysm repair.

Fusion imaging is standard for the endovascular treatment of complex aortic aneurysms, but its role in follow up has not been explored. A critical issue is renal function deterioration over time. Renal volume has been used as a marker of renal impairment; however, it is not reproducible and remains a complex and resource-intensive procedure. The aim of this study is to determine the accuracy of a fusion-based software to automatically calculate the renal volume changes during follow up. In this study, computerized tomography (CT) scans of 16 patients who underwent complex aortic endovascular repair were analysed. Preoperative, 1-month and 1-year follow-up CT scans have been analysed using a conventional approach of semi-automatic segmentation, and a second approach with automatic segmentation. For each kidney and at each time point the percentage of change in renal volume was calculated using both techniques. After review, volume assessment was feasible for all CT scans. For the left kidney, the intraclass correlation coefficient (ICC) was 0.794 and 0.877 at 1 month and 1 year, respectively. For the right side, the ICC was 0.817 at 1 month and 0.966 at 1 year. The automated technique reliably detected a decrease in renal volume for the eight patients with occluded renal arteries during follow up. This is the first report of a fusion-based algorithm to detect changes in renal volume during postoperative surveillance using an automated process. Using this technique, the standardized assessment of renal volume could be implemented with greater ease and reproducibility and serve as a warning of potential renal impairment.

Magnetic resonance imaging planning in children with complex congenital heart disease - A new approach.

OBJECTIVES: To compare a standard sequential 2D Planning Method (2D-PM) with a 3D offline Planning Method (3D-PM) based on 3D contrast-enhanced magnetic resonance angiography (CE-MRA) in children with congenital heart disease (CHD). DESIGN: In 14 children with complex CHD (mean: 2.6 years, range: 3 months to 7.6 years), axial and coronal cuts were obtained with single slice spin echo sequences to get the final double oblique longitudinal cut of the targeted anatomical structure (2D-PM, n = 31). On a separate workstation, similar maximal intensity projection (MIP) images were generated offline from a 3D CE-MRA. MIP images were localizers for repeated targeted imaging using the previous spin echo sequence (3D-PM). Finally, image coverage, spatial orientation and acquisition time were compared for 2D-PM and 3D-PM. MAIN OUTCOME MEASURES: 2D-PM and 3D-PM images were similar: both perfectly covered the selected anatomic regions and no spatial differences were found (p>0.05). The mean time for creation of the final imaging plane was 241 ± 31 s (2D-PM) compared to 71 ± 18 s (3D-PM) (p<0.05). CONCLUSIONS: 3D-PM shows similar results compared to 2D-PM, but allows faster and offline planning thereby reducing the scan time significantly. As newly developed high-resolution 3D datasets can also be used further improvement of this technology is expected.

Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric.

OBJECTIVE: 3-D +t echocardiography (3DtE) is widely employed for the assessment of left ventricular anatomy and function. However, the information derived from 3DtE images can be affected by the poor image quality and the limited field of view. Registration of multiview 3DtE sequences has been proposed to compound images from different acoustic windows, therefore improving both image quality and coverage. We propose a novel subspace error metric for an automatic and robust registration of multiview intrasubject 3DtE sequences. METHODS: The proposed metric employs linear dimensionality reduction to exploit the similarity in the temporal variation of multiview 3DtE sequences. The use of a low-dimensional subspace for the computation of the error metric reduces the influence of image artefacts and noise on the registration optimization, resulting in fast and robust registrations that do not require a starting estimate. RESULTS: The accuracy, robustness, and execution time of the proposed registration were thoroughly validated. Results on 48 pairwise multiview 3DtE registrations show the proposed error metric to outperform a state-of-the-art phase-based error metric, with improvements in median/75th percentile of the target registration error of 21%/31% and an improvement in mean execution time of 45%. CONCLUSION: The proposed subspace error metric outperforms sum-of-squared differences and phase-based error metrics for the registration of multiview 3DtE sequences in terms of accuracy, robustness, and execution time. SIGNIFICANCE: The use of the proposed subspace error metric has the potential to replace standard image error metrics for a robust and automatic registration of multiview 3DtE sequences.

Regional Differences in End-Diastolic Volumes between 3D Echo and CMR in HLHS Patients.

Ultrasound is commonly thought to underestimate ventricular volumes compared to magnetic resonance imaging (MRI), although the reason for this and the spatial distribution of the volume difference is not well understood. In this paper, we use landmark-based image registration to spatially align MRI and ultrasound images from patients with hypoplastic left heart syndrome and carry out a qualitative and quantitative spatial comparison of manual segmentations of the ventricular volume obtained from the respective modalities. In our experiments, we have found a trend showing volumes estimated from ultrasound to be smaller than those obtained from MRI (by approximately up to 20 ml), and that important contributors to this difference are the presence of artifacts such as shadows in the echo images and the different criteria to include or exclude image features as part of the ventricular volume.

Fully automated 2D-3D registration and verification.

Clinical application of 2D-3D registration technology often requires a significant amount of human interaction during initialisation and result verification. This is one of the main barriers to more widespread clinical use of this technology. We propose novel techniques for automated initial pose estimation of the 3D data and verification of the registration result, and show how these techniques can be combined to enable fully automated 2D-3D registration, particularly in the case of a vertebra based system. The initialisation method is based on preoperative computation of 2D templates over a wide range of 3D poses. These templates are used to apply the Generalised Hough Transform to the intraoperative 2D image and the sought 3D pose is selected with the combined use of the generated accumulator arrays and a Gradient Difference Similarity Measure. On the verification side, two algorithms are proposed: one using normalised features based on the similarity value and the other based on the pose agreement between multiple vertebra based registrations. The proposed methods are employed here for CT to fluoroscopy registration and are trained and tested with data from 31 clinical procedures with 417 low dose, i.e. low quality, high noise interventional fluoroscopy images. When similarity value based verification is used, the fully automated system achieves a 95.73% correct registration rate, whereas a no registration result is produced for the remaining 4.27% of cases (i.e. incorrect registration rate is 0%). The system also automatically detects input images outside its operating range.

4D Blood Flow Reconstruction Over the Entire Ventricle From Wall Motion and Blood Velocity Derived From Ultrasound Data.

We demonstrate a new method to recover 4D blood flow over the entire ventricle from partial blood velocity measurements using multiple 3D+t colour Doppler images and ventricular wall motion estimated using 3D+t BMode images. We apply our approach to realistic simulated data to ascertain the ability of the method to deal with incomplete data, as typically happens in clinical practice. Experiments using synthetic data show that the use of wall motion improves velocity reconstruction, shows more accurate flow patterns and improves mean accuracy particularly when coverage of the ventricle is poor. The method was applied to patient data from 6 congenital cases, producing results consistent with the simulations. The use of wall motion produced more plausible flow patterns and reduced the reconstruction error in all patients.

Interventional digital tomosynthesis from a standard fluoroscopy system using 2D-3D registration.

Interventional fluoroscopy provides guidance in a variety of minimally invasive procedures. However, three-dimensional (3D) clinically relevant information is projected onto a two-dimensional (2D) image which can make image interpretation difficult. Moreover, vasculature visualisation requires the use of iodinated contrast media which is nephrotoxic and is the primary cause of renal complications. In this article, we demonstrate how digital tomosynthesis slices can be produced on standard fluoroscopy equipment by registering the preoperative CT volume and the intraoperative fluoroscopy images using 2D-3D image registration. The proposed method automatically reconstructs patient-anatomy-specific slices and removes clutter resulting from bony anatomy. Such slices could provide additional intraoperative information which cannot be provided by the preoperative CT volume alone, such as the deformed aorta position offering improved guidance precision. Image acquisition would fit with interventional clinical work-flow and would not require a high X-ray dose. Experiments are carried out using one phantom and four clinical datasets. Phantom results showed a 3351% contrast-to-noise improvement compared to standard fluoroscopy. Patient results showed our method enabled visualization of clinically relevant features: outline of the aorta, the aortic bifurcation and some aortic calcifications.

Personalising population-based respiratory motion models of the heart using neighbourhood approximation based on learnt anatomical features.

Respiratory motion models have been proposed for the estimation and compensation of respiratory motion during image acquisition and image-guided interventions on organs in the chest and abdomen. However, such techniques are not commonly used in the clinic. Subject-specific motion models require a dynamic calibration scan that interrupts the clinical workflow and is often impractical to acquire, while population-based motion models are not as accurate as subject-specific motion models. To address this lack of accuracy, we propose a novel personalisation framework for population-based respiratory motion models and demonstrate its application to respiratory motion of the heart. The proposed method selects a subset of the population sample which is more likely to represent the cardiac respiratory motion of an unseen subject, thus providing a more accurate motion model. The selection is based only on anatomical features of the heart extracted from a static image. The features used are learnt using a neighbourhood approximation technique from a set of training datasets for which respiratory motion estimates are available. Results on a population sample of 28 adult healthy volunteers show average improvements in estimation accuracy of 20% compared to a standard population-based motion model, with an average value for the 50th and 95th quantiles of the estimation error of 1.6mm and 4.7 mm respectively. Furthermore, the anatomical features of the heart most strongly correlated to respiratory motion are investigated for the first time, showing the features on the apex in proximity to the diaphragm and the rib cage, on the left ventricle and interventricular septum to be good predictors of the similarity in cardiac respiratory motion.

Retrospective Rigid Motion Correction in k-Space for Segmented Radial MRI.

Motion occurring during magnetic resonance imaging acquisition is a major factor of image quality degradation. Self-navigation can help reduce artefacts by estimating motion from the acquired data to enable motion correction. Popular self-navigation techniques rely on the availability of a fully-sampled motion-free reference to register the motion corrupted data with. In the proposed technique, rigid motion parameters are derived using the inherent correlation between radial segments in k-space. The registration is performed exclusively in k-space using the Phase Correlation Method, a popular registration technique in computer vision. Robust and accurate registration has been carried out from radial segments composed of as few as 32 profiles. Successful self-navigation has been performed on 2-D dynamic brain scans corrupted with continuous motion for six volunteers. Retrospective motion correction using the derived self-navigation parameters resulted in significant improvement of image quality compared to the conventional sliding window. This work also demonstrates the benefits of using a bit-reversed ordering scheme to limit undesirable effects specific to retrospective motion correction on radial trajectories. This method provides a fast and efficient mean of measuring rigid motion directly in k-space from dynamic radial data under continuous motion.

A sensitivity analysis on 3D velocity reconstruction from multiple registered echo Doppler views.

We present a new method for reconstructing a 3D+t velocity field from multiple 3D+t colour Doppler images. Our technique reconstructs 3D velocity vectors from registered multiple standard 3D colour Doppler views, each of which contains a 1D projection of the blood velocity. Reconstruction is based on a scalable patch-wise Least Mean Squares approach, and a continuous velocity field is achieved by using a B-spline grid. We carry out a sensitivity analysis of clinically relevant parameters which affect the accuracy of the reconstruction, including the impact of noise, view angles and registration errors, using simulated data. A realistic simulation framework is achieved by a novel noise model to represent variations in colour Doppler images based on multiscale additive Gaussian noise. Simulations show that, if the Target Registration Error <2.5mm, view angles are >20° and the standard deviation of noise in the input data is <10 cm/s, the reconstructed velocity field presents visually plausible flow patterns and mean error in flow rate is approximately 10% compared to 2D+t Flow MRI. These results are verified by reconstructing 3D velocity on three healthy volunteers. The technique is applied to reconstruct 3D flow on three paediatric patients showing promising results for clinical application.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method.

Multiview diffeomorphic registration: application to motion and strain estimation from 3D echocardiography.

This paper presents a new registration framework for quantifying myocardial motion and strain from the combination of multiple 3D ultrasound (US) sequences. The originality of our approach lies in the estimation of the transformation directly from the input multiple views rather than from a single view or a reconstructed compounded sequence. This allows us to exploit all spatiotemporal information available in the input views avoiding occlusions and image fusion errors that could lead to some inconsistencies in the motion quantification result. We propose a multiview diffeomorphic registration strategy that enforces smoothness and consistency in the spatiotemporal domain by modeling the 4D velocity field continuously in space and time. This 4D continuous representation considers 3D US sequences as a whole, therefore allowing to robustly cope with variations in heart rate resulting in different number of images acquired per cardiac cycle for different views. This contributes to the robustness gained by solving for a single transformation from all input sequences. The similarity metric takes into account the physics of US images and uses a weighting scheme to balance the contribution of the different views. It includes a comparison both between consecutive images and between a reference and each of the following images. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement fields. Registration and strain accuracy were evaluated on synthetic 3D US sequences with known ground truth. Experiments were also conducted on multiview 3D datasets of 8 volunteers and 1 patient treated by cardiac resynchronization therapy. Strain curves obtained from our multiview approach were compared to the single-view case, as well as with other multiview approaches. For healthy cases, the inclusion of several views improved the consistency of the strain curves and reduced the number of segments where a non-physiological strain pattern was observed. For the patient, the improvement (pacing ON vs. OFF) in synchrony of regional strain correlated with clinician blind assessment and could be seen more clearly when using the multiview approach.

Design and evaluation of an image-guidance system for robot-assisted radical prostatectomy.

UNLABELLED: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Systems for image guidance during laparoscopic surgery can be broadly defined as systems that enable the surgeon to refer to preoperatively gathered information during the procedure. For a laparoscopic system the preoperative information can be overlaid onto the laparoscopic video screen. Examples of surgical image-guidance systems and the results of early testing are not uncommon but the technical methodologies used vary widely as do the visualisation methods. This study reports our experience of using an image-guidance system on 13 patients. Furthermore, we use previously proposed methodology to form a development and evaluation framework specific to image-guided laparoscopic radical prostatectomy. Finally, we propose that if the system development process is properly designed, it should be possible to correlate system technical parameters with clinical outcomes. We present a possible plot for the key technical parameter of accuracy. Better understanding of this correlation should enable robust development and evaluation of surgical image-guidance systems to optimise patient outcomes. OBJECTIVE: To implement and test the feasibility of an image-guidance system for robot-assisted radical prostatectomy (RARP). Laparoscopic surgical outcomes may be improved through image guidance. However, to demonstrate improved outcomes rigorous evaluation techniques are required. Therefore we also present our work in establishing robust evaluation techniques. PATIENTS AND METHODS: Development work used three cadavers and an anatomy phantom. The system has been used on 13 patients. During surgery the surgeon can refer to the patient's magnetic resonance imaging (collected before the operation) overlaid on the endoscopic video image. The result of the overlay process was measured qualitatively by the surgeon with reference to the desired clinical outcomes. RESULTS: The use of the overlay system has not resulted in any measurable change in clinical outcomes. The surgeons found the system to be a useful tool for reference during surgery. A more rigorous evaluation method is proposed that will enable on-going development. CONCLUSION: Image guidance during RARP is feasible. We propose a series of measures that will improve further development and evaluation.

Increasing the automation of a 2D-3D registration system.

Routine clinical use of 2D-3D registration algorithms for Image Guided Surgery remains limited. A key aspect for routine clinical use of this technology is its degree of automation, i.e., the amount of necessary knowledgeable interaction between the clinicians and the registration system. Current image-based registration approaches usually require knowledgeable manual interaction during two stages: for initial pose estimation and for verification of produced results. We propose four novel techniques, particularly suited to vertebra-based registration systems, which can significantly automate both of the above stages. Two of these techniques are based upon the intraoperative "insertion" of a virtual fiducial marker into the preoperative data. The remaining two techniques use the final registration similarity value between multiple CT vertebrae and a single fluoroscopy vertebra. The proposed methods were evaluated with data from 31 operations (31 CT scans, 419 fluoroscopy images). Results show these methods can remove the need for manual vertebra identification during initial pose estimation, and were also very effective for result verification, producing a combined true positive rate of 100% and false positive rate equal to zero. This large decrease in required knowledgeable interaction is an important contribution aiming to enable more widespread use of 2D-3D registration technology.

Interventional digital tomosynthesis from a standard fluoroscopy system using 2D-3D registration.

Fluoroscopy is the mainstay of interventional radiology. However, the images are 2D and visualisation of vasculature requires nephrotoxic contrast. Cone-beam computed tomography is often available, but involves large radiation dose and interruption to clinical workflow. We propose the use of 2D-3D image registration to allow digital tomosynthesis (DTS) slices to be produced using standard fluoroscopy equipment. Our method automatically produces patient-anatomy-specific slices and removes clutter resulting from bones. Such slices could provide additional intraoperative information, offering improved guidance precision. Image acquisition would fit with interventional clinical workflow and would not require a high x-ray dose. Phantom results showed a 1133% contrast-to-noise improvement compared to standard fluoroscopy. Patient results showed our method enabled visualisation of clinically relevant features: outline of the aorta, the aortic bifurcation and some aortic calcifications.

Non-rigid 2D-3D registration using anisotropic error ellipsoids to account for projection uncertainties during aortic surgery.

Overlay of preoperative images is increasingly being used to aid complex endovascular aortic repair and is obtained by rigid 2D-3D registration of 3D preoperative (CT) and 2D intraoperative (X-ray) data. However, for tortuous aortas large non-rigid deformations occur, thus a non-rigid registration must be performed to enable an accurate overlay. This article proposes the use of Thin-Plate Splines (TPS) to perform non-rigid 2D-3D registration. Intraoperative X-ray data contain no spatial information along the X-ray projection direction. Our approach accounts for this lack of spatial information by the use of an approximating TPS with non-isotropic error ellipsoids, where the major ellipsoid axis is aligned with the X-ray projection direction. Experiments are carried out using 1D-2D and 2D-3D simulated data and 2D-3D interventional data. Simulated results show that our proposed method is 1.5 times more accurate than interpolating TPS based registration. Interventional data results show how large rigid registration errors of 9mm can be reduced to 4mm using our proposed method.

3D intraventricular flow mapping from colour Doppler images and wall motion.

We propose a new method to recover 3D time-resolved velocity vectors within the left ventricle (LV) using a combination of multiple registered 3D colour Doppler images and LV wall motion. Incorporation of wall motion, calculated from 3D B-Mode images, and the use of a multi-scale reconstruction framework allow recovery of 3D velocity over the entire ventricle, even in regions where there is little or no Doppler data. Our method is tested on the LV of a paediatric patient and is compared to 2D and 3D flow Magnetic Resonance Imaging (MRI). Use of wall motion information increased stroke volume accuracy by 14%, and enabled full 3D velocity mapping within the ventricle. Velocity distribution showed good agreement with respect to MRI, and vortex formation during diastole was successfully reconstructed.

Improved modelling of tool tracking errors by modelling dependent marker errors.

Accurate understanding of equipment tracking error is essential for decision making in image guided surgery. For tools tracked using markers attached to a rigid body, existing error estimation methods use the assumption that the individual marker errors are independent random variables. This assumption is not valid for all tracking systems. This paper presents a method to estimate a more accurate tracking error function, consisting of a systematic and random component. The proposed method does not require detailed knowledge of the tracking system physics. Results from a pointer calibration are used to demonstrate that the proposed method provides a better match to observed results than the existing state of the art. A simulation of the pointer calibration process is then used to show that existing methods can underestimate the pointer calibration error by a factor of two. A further simulation of laparoscopic camera tracking is used to show that existing methods cannot model important variations in system performance due to the angular arrangement of the tracking markers. By arranging the markers such that the systematic errors are nearly identical for all markers, the rotational component of the tracking error can be reduced, resulting in a significant reduction in target tracking errors.

Reconstruction of a 3D surface from video that is robust to missing data and outliers: application to minimally invasive surgery using stereo and mono endoscopes.

Minimally invasive surgery (MIS) offers great benefits to patients compared with open surgery. Nevertheless during MIS surgeons often need to contend with a narrow field-of-view of the endoscope and obstruction from other surgical instruments. He/she may also need to relate the surgical scene to information derived from previously acquired 3D medical imaging. We thus present a new framework to reconstruct the 3D surface of an internal organ from endoscopic images which is robust to measurement noise, missing data and outliers. This can provide 3D surface with a wide field-of-view for surgeons, and it can also be used for 3D-3D registration of the anatomy to pre-operative CT/MRI data for use in image guided interventions. Our proposed method first removes most of the outliers using an outlier removal method that is based on the trilinear constraints over three images. Then data that are missing from one or more of the video images (missing data) and 3D structure are recovered using the structure from motion (SFM) technique. Evolutionary agents are applied to improve both the efficiency of data recovery and robustness to outliers. Furthermore, an incremental bundle adjustment strategy is used to refine the camera parameters and 3D structure and produce a more accurate 3D surface. Experimental results with synthetic data show that the method is able to reconstruct surfaces in the presence of feature tracking errors (up to 5 pixel standard deviation) and a large amount of missing data (up to 50%). Experiments on a realistic phantom model and in vivo data further demonstrate the good performance of the proposed approach in terms of accuracy (1.7 mm residual phantom surface error) and robustness (50% missing data rate, and 20% outliers in in vivo experiments).

Registration of 3D trans-esophageal echocardiography to X-ray fluoroscopy using image-based probe tracking.

Two-dimensional (2D) X-ray imaging is the dominant imaging modality for cardiac interventions. However, the use of X-ray fluoroscopy alone is inadequate for the guidance of procedures that require soft-tissue information, for example, the treatment of structural heart disease. The recent availability of three-dimensional (3D) trans-esophageal echocardiography (TEE) provides cardiologists with real-time 3D imaging of cardiac anatomy. Increasingly X-ray imaging is now supported by using intra-procedure 3D TEE imaging. We hypothesize that the real-time co-registration and visualization of 3D TEE and X-ray fluoroscopy data will provide a powerful guidance tool for cardiologists. In this paper, we propose a novel, robust and efficient method for performing this registration. The major advantage of our method is that it does not rely on any additional tracking hardware and therefore can be deployed straightforwardly into any interventional laboratory. Our method consists of an image-based TEE probe localization algorithm and a calibration procedure. While the calibration needs to be done only once, the GPU-accelerated registration takes approximately from 2 to 15s to complete depending on the number of X-ray images used in the registration and the image resolution. The accuracy of our method was assessed using a realistic heart phantom. The target registration error (TRE) for the heart phantom was less than 2mm. In addition, we assess the accuracy and the clinical feasibility of our method using five patient datasets, two of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures. The registration results showed our technique had mean registration errors of 1.5-4.2mm and 95% capture range of 8.7-11.4mm in terms of TRE.