A novel methodology for personalized simulations of ventricular hemodynamics from noninvasive imaging data

Current state-of-the-art imaging techniques can provide quantitative information to characterize ventricular function within the limits of the spatiotemporal resolution achievable in a realistic acquisition time. These imaging data can be used to personalize computer models, which in turn can help treatment planning by quantifying biomarkers that cannot be directly imaged, such as flow energy, shear stress and pressure gradients. To date, computer models have typically relied on invasive pressure measurements to be made patient-specific. When these data are not available, the scope and validity of the models are limited. To address this problem, we propose a new methodology for modeling patient-specific hemodynamics based exclusively on noninvasive velocity and anatomical data from 3D+t echocardiography or Magnetic Resonance Imaging (MRI). Numerical simulations of the cardiac cycle are driven by the image-derived velocities prescribed at the model boundaries using a penalty method that recovers a physical solution by minimizing the energy imparted to the system. This numerical approach circumvents the mathematical challenges due to the poor conditioning that arises from the imposition of boundary conditions on velocity only. We demonstrate that through this technique we are able to reconstruct given flow fields using Dirichlet only conditions. We also perform a sensitivity analysis to investigate the accuracy of this approach for different images with varying spatiotemporal resolution. Finally, we examine the influence of noise on the computed result, showing robustness to unbiased noise with an average error in the simulated velocity approximately 7% for a typical voxel size of 2mm(3) and temporal resolution of 30ms. The methodology is eventually applied to a patient case to highlight the potential for a direct clinical translation.

Towards a fast and efficient approach for modelling the patient-specific ventricular haemodynamics

Computer modelling of the heart has emerged over the past decade as a powerful technique to explore the cardiovascular pathophysiology and inform clinical diagnosis. The current state-of-the-art in biophysical modelling requires a wealth of, potentially invasive, clinical data for the parametrisation and validation of the models, a process that is still too long and complex to be compatible with the clinical decision-making time. Therefore, there remains a need for models that can be quickly customised to reconstruct physical processes difficult to measure directly in patients. In this paper, we propose a less resource-intensive approach to modelling, whereby computational fluid-dynamics (CFD) models are constrained exclusively by boundary motion derived from imaging data through a validated wall tracking algorithm. These models are generated and parametrised based solely on ultrasound data, whose acquisition is fast, inexpensive and routine in all patients. To maximise the time and computational efficiency, a semi-automated pipeline is embedded in an image processing workflow to personalise the models. Applying this approach to two patient cases, we demonstrate this tool can be directly used in the clinic to interpret and complement the available clinical data by providing a quantitative indication of clinical markers that cannot be easily derived from imaging, such as pressure gradients and the flow energy.

Automated registration of optical coherence tomography and dermoscopy in the assessment of sub-clinical spread in basal cell carcinoma

Optical coherence tomography (OCT) has been shown to be of clinical value in imaging basal cell carcinoma (BCC). A novel dual OCT-video imaging system, providing automated registration of OCT and dermoscopy, has been developed to assess the potential of OCT in measuring the degree of sub-clinical spread of BCC. Seventeen patients selected for Mohs micrographic surgery (MMS) for BCC were recruited to the study. The extent of BCC infiltration beyond a segment of the clinically assessed pre-surgical border was evaluated using OCT. Sufficiently accurate (<0.5 mm) registration of OCT and dermoscopy images was achieved in 9 patients. The location of the OCT-assessed BCC border was also compared with that of the final surgical defect. Infiltration of BCC across the clinical border ranged from 0 mm to >2.5 mm. In addition, the OCT border lay between 0.5 mm and 2.0 mm inside the final MMS defect in those cases where this could be assessed. In one case, where the final MMS defect was over 17 mm from the clinical border, OCT showed >2.5 mm infiltration across the clinical border at the FOV limit. These results provide evidence that OCT allows more accurate assessment of sub-clinical spread of BCC than clinical observation alone. Such a capability may have clinical value in reducing the number of surgical stages in MMS for BCC. There may also be a role for OCT in aiding the selection of patients most suitable for MMS.

Registering preprocedure volumetric images with intraprocedure 3-D ultrasound using an ultrasound imaging model

For many image-guided interventions there exists a need to compute the registration between preprocedure image(s) and the physical space of the intervention. Real-time intraprocedure imaging such as ultrasound (US) can be used to image the region of interest directly and provide valuable anatomical information for computing this registration. Unfortunately, real-time US images often have poor signal-to-noise ratio and suffer from imaging artefacts. Therefore, registration using US images can be challenging and significant preprocessing is often required to make the registrations robust. In this paper we present a novel technique for computing the image-to-physical registration for minimally invasive cardiac interventions using 3-D US. Our technique uses knowledge of the physics of the US imaging process to reduce the amount of preprocessing required on the 3-D US images. To account for the fact that clinical US images normally undergo significant image processing before being exported from the US machine our optimization scheme allows the parameters of the US imaging model to vary. We validated our technique by computing rigid registrations for 12 cardiac US/magnetic resonance imaging (MRI) datasets acquired from six volunteers and two patients. The technique had mean registration errors of 2.1-4.4 mm, and 75% capture ranges of 5-30 mm. We also demonstrate how the same approach can be used for respiratory motion correction: on 15 datasets acquired from five volunteers the registration errors due to respiratory motion were reduced by 45%-92%.

Cadaver validation of intensity-based ultrasound to CT registration

A method is presented for the rigid registration of tracked B-mode ultrasound images to a CT volume of a femur and pelvis. This registration can allow tracked surgical instruments to be aligned with the CT image or an associated preoperative plan. Our method is fully automatic and requires no manual segmentation of either the ultrasound images or the CT volume. The parameter which is directly related to the speed of sound through tissue has also been included in the registration optimisation process. Experiments have been carried out on six cadaveric femurs and three cadaveric pelves. Registration results were compared with a "gold standard" registration acquired using bone implanted fiducial markers. Results show the registration method to be accurate, on average, to 1.6 mm root-mean-square target registration error.

Tissue deformation and shape models in image-guided interventions: a discussion paper

This paper promotes the concept of active models in image-guided interventions. We outline the limitations of the rigid body assumption in image-guided interventions and describe how intraoperative imaging provides a rich source of information on spatial location of anatomical structures and therapy devices, allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Established methods using free-form deformations have a very large number of degrees of freedom. Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions. We provide preliminary results from applications for each type of model.

Registration-based interpolation

A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the registration algorithm. The registration-based method is compared to both standard linear interpolation and shape-based interpolation in 20 tomographic data sets. Results show that the proposed method statistically significantly outperforms both linear and shape-based interpolation.

Registration of freehand 3D ultrasound and magnetic resonance liver images

We present a method to register a preoperative MR volume to a sparse set of intraoperative ultrasound slices. Our aim is to allow the transfer of information from preoperative modalities to intraoperative ultrasound images to aid needle placement during thermal ablation of liver metastases. The spatial relationship between ultrasound slices is obtained by tracking the probe using a Polaris optical tracking system. Images are acquired at maximum exhalation and we assume the validity of the rigid body transformation. An initial registration is carried out by picking a single corresponding point in both modalities. Our strategy is to interpret both sets of images in an automated pre-processing step to produce evidence or probabilities of corresponding structure as a pixel or voxel map. The registration algorithm converts the intensity values of the MR and ultrasound images into vessel probability values. The registration is then carried out between the vessel probability images. Results are compared to a "bronze standard" registration which is calculated using a manual point/line picking algorithm and verified using visual inspection. Results show that our starting estimate is within a root mean square target registration error (calculated over the whole liver) of 15.4 mm to the "bronze standard" and this is improved to 3.6 mm after running the intensity-based algorithm.

Assessment of a technique for 2D–3D registration of cerebral intra-arterial angiography

This study assesses the ability of a computer algorithm to perform automated 2D-3D registrations of digitally subtracted cerebral angiograms. The technique was tested on clinical studies of five patients with intracranial aneurysms. The automated procedure was compared against a gold standard manual registration, and achieved a mean registration accuracy of 1.3 mm (SD 0.6 mm). Two registration strategies were tested using coarse (128 x 128 pixel) or fine (256 x 256 pixel) images. The mean registration errors proved similar but registration of the lower resolution images was 3 times quicker (mean registration times 33 s, SD 13 s for low and 150 s SD 48 s for high resolution images). The automated techniques were considerably faster than manual registrations but achieved similar accuracy. The technique has several potential uses but is particularly applicable to endovascular treatment techniques.

Deforming a preoperative volume to represent the intraoperative scene

Soft-tissue deformation can be a problem if a preoperative modality is used to help guide a surgical or interventional procedure. We present a method that can warp a preoperative CT image to represent the intraoperative scene shown by an interventional fluoroscopy image. The method is a novel combination of a 2D-3D image registration algorithm and a deformation algorithm that allows rigid bodies to be incorporated into a nonlinear deformation based on radial basis functions. The 2D-3D registration algorithm is used to obtain information on relative vertebral movements between preoperative and intraoperative images. The deformation algorithm uses this information to warp the preoperative image to represent the intraoperative scene more accurately. Images from an aortic stenting procedure were used. The observed deformation in our experiment was 5 degrees flexion and 5 mm lengthening of the lumbar spine over a distance of four vertebrae. The vertebral positions in the warped CT volume represent the intraoperative scene more accurately than in the preoperative CT volume. Although we had no gold standard with which to assess the registration accuracy of soft-tissue structures, the position of such structures within the warped CT volume appeared visually realistic.

Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images

We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.

The EASI project--improving the effectiveness and quality of image-guided surgery

In recent years, advances in computer technology and a significant increase in the accuracy of medical imaging have made it possible to develop systems that can assist the clinician in diagnosis, planning, and treatment. This paper deals with an area that is generally referred to as computer-assisted surgery, image-directed surgery, or image-guided surgery. We report the research, development, and clinical validation performed since January 1996 in the European Applications in Surgical Interventions (EASI) project, which is funded by the European Commission in their "4th Framework Telematics Applications for Health" program. The goal of this project is the improvement of the effectiveness and quality of image-guided neurosurgery of the brain and image-guided vascular surgery of abdominal aortic aneurysms, while at the same time reducing patient risks and overall cost. We have developed advanced prototype systems for preoperative surgical planning and intraoperative surgical navigation, and we have extensively clinically validated these systems. The prototype systems and the clinical validation results are described in this paper.

A comparison of similarity measures for use in 2-D-3-D medical image registration

A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.54 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.

Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery

Registration of intraoperative fluoroscopy images with preoperative three-dimensional (3-D) CT images can be used for several purposes in image-guided surgery. On the one hand, it can be used to display the position of surgical instruments, which are being tracked by a localizer, in the preoperative CT scan. On the other hand, the registration result can be used to project preoperative planning information or important anatomical structures visible in the CT image onto the fluoroscopy image. For this registration task, a novel voxel-based method in combination with a new similarity measure (pattern intensity) has been developed. The basic concept of the method is explained at the example of two-dimensional (2-D)/3-D registration of a vertebra in an X-ray fluoroscopy image with a 3-D CT image. The registration method is described, and the results for a spine phantom are presented and discussed. Registration has been carried out repeatedly with different starting estimates to study the capture range. Information about registration accuracy has been obtained by comparing the registration results with a highly accurate "ground-truth" registration, which has been derived from fiducial markers attached to the phantom prior to imaging. In addition, registration results for different vertebrae have been compared. The results show that the rotation parameters and the shifts parallel to the projection plane can accurately be determined from a single projection. Because of the projection geometry, the accuracy of the height above the projection plane is significantly lower.