Modelling prostate motion for data fusion during image-guided interventions.

There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.

Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging.

Statistical shape modelling potentially provides a powerful tool for generating patient-specific, 3D representations of bony anatomy for computer-aided orthopaedic surgery (CAOS) without the need for a preoperative CT scan. Furthermore, freehand 3D ultrasound (US) provides a non-invasive method for digitising bone surfaces in the operating theatre that enables a much greater region to be sampled compared with conventional direct-contact (i.e., pointer-based) digitisation techniques. In this paper, we describe how these approaches can be combined to simultaneously generate and register a patient-specific model of the femur and pelvis to the patient during surgery. In our implementation, a statistical deformation model (SDM) was constructed for the femur and pelvis by performing a principal component analysis on the B-spline control points that parameterise the freeform deformations required to non-rigidly register a training set of CT scans to a carefully segmented template CT scan. The segmented template bone surface, represented by a triangulated surface mesh, is instantiated and registered to a cloud of US-derived surface points using an iterative scheme in which the weights corresponding to the first five principal modes of variation of the SDM are optimised in addition to the rigid-body parameters. The accuracy of the method was evaluated using clinically realistic data obtained on three intact human cadavers (three whole pelves and six femurs). For each bone, a high-resolution CT scan and rigid-body registration transformation, calculated using bone-implanted fiducial markers, served as the gold standard bone geometry and registration transformation, respectively. After aligning the final instantiated model and CT-derived surfaces using the iterative closest point (ICP) algorithm, the average root-mean-square distance between the surfaces was 3.5mm over the whole bone and 3.7mm in the region of surgical interest. The corresponding distances after aligning the surfaces using the marker-based registration transformation were 4.6 and 4.5mm, respectively. We conclude that despite limitations on the regions of bone accessible using US imaging, this technique has potential as a cost-effective and non-invasive method to enable surgical navigation during CAOS procedures, without the additional radiation dose associated with performing a preoperative CT scan or intraoperative fluoroscopic imaging. However, further development is required to investigate errors using error measures relevant to specific surgical procedures.

Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery.

Intraoperative freehand three-dimensional (3-D) ultrasound (3D-US) has been proposed as a noninvasive method for registering bones to a preoperative computed tomography image or computer-generated bone model during computer-aided orthopedic surgery (CAOS). In this technique, an US probe is tracked by a 3-D position sensor and acts as a percutaneous device for localizing the bone surface. However, variations in the acoustic properties of soft tissue, such as the average speed of sound, can introduce significant errors in the bone depth estimated from US images, which limits registration accuracy. We describe a new self-calibrating approach to US-based bone registration that addresses this problem, and demonstrate its application within a standard registration scheme. Using realistic US image data acquired from 6 femurs and 3 pelves of intact human cadavers, and accurate Gold Standard registration transformations calculated using bone-implanted fiducial markers, we show that self-calibrating registration is significantly more accurate than a standard method, yielding an average root mean squared target registration error of 1.6 mm. We conclude that self-calibrating registration results in significant improvements in registration accuracy for CAOS applications over conventional approaches where calibration parameters of the 3D-US system remain fixed to values determined using a preoperative phantom-based calibration.

Application of soft tissue modelling to image-guided surgery.

The deformation of soft tissue compromises the accuracy of image-guided surgery based on preoperative images, and restricts its applicability to surgery on or near bony structures. One way to overcome these limitations is to combine biomechanical models with sparse intraoperative data, in order to realistically warp the preoperative image to match the surgical situation. We detail the process of biomechanical modelling in the context of image-guided surgery. We focus in particular on the finite element method, which is shown to be a promising approach, and review the constitutive relationships which have been suggested for representing tissue during surgery. Appropriate intraoperative measurements are required to constrain the deformation, and we discuss the potential of the modalities which have been applied to this task. This technology is on the verge of transition into clinical practice, where it promises to increase the guidance accuracy and facilitate less invasive interventions. We describe here how soft tissue modelling techniques have been applied to image-guided surgery applications.

Cadaver validation of intensity-based ultrasound to CT registration.

A method is presented for the registration of tracked B-mode ultrasound images to a CT volume of a femur or pelvis. This registration can allow tracked surgical instruments to be aligned with the CT image or an associated preoperative plan. Our method requires no manual segmentation of either the ultrasound images or the CT volume. The CT and US images are processed to produce images where the image intensity represents the probability of the presence of a bone edge. These images are then registered together using normalised cross-correlation as a similarity measure. The parameter which represents 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 1.7 mm root-mean-square target registration error.

Self-calibrating ultrasound-to-CT bone registration.

We describe a new self-calibrating approach to rigid registration of 3D ultrasound images in which in vivo data acquired for registration are used to simultaneously perform a patient-specific update of the calibration parameters of the 3D ultrasound system. Using a self-calibrating implementation of a point-based registration algorithm, and points obtained from ultrasound images of the femurs and pelves of human cadavers, we show that the accuracy of registration to a CT scan is significantly improved compared with a standard algorithm. This new approach provides an effective means of compensating for errors introduced by the propagation of ultrasound through soft tissue, which currently limit the accuracy of conventional methods where the calibration parameters are fixed to values determined preoperatively using a phantom.

A novel piezo-optical styrene sensor incorporating polymer-supported tribromide ion.

Encapsulation of Amberlyst A-26 supported tribromide in a 10,000 MW polyethylene glycol matrix gives a robust colour-sensitive reagent matrix which can be deposited on indium-tin oxide coated polyvinylidene fluoride (PVDF) piezoelectric film for the detection of styrene (and other alkene) vapours.