Timothy J. Carter

Characterizing Clinically Significant Prostate Cancer Using Template Prostate Mapping Biopsy

PURPOSE Definitions of prostate cancer risk are limited since accurate attribution of the cancer grade and burden is not possible due to the random and systematic errors associated with transrectal ultrasound guided biopsy. Transperineal prostate mapping biopsy may have a role in accurate risk stratification. We defined the transperineal prostate mapping biopsy characteristics of clinically significant disease. MATERIALS AND METHODS A 3-dimensional model of each gland and individual cancer was reconstructed using 107 radical whole mount specimens. We performed 500 transperineal prostate mapping simulations per case by varying needle targeting errors to calculate sensitivity, specificity, and negative and positive predictive value to detect lesions 0.2 ml or greater, or 0.5 ml or greater. Definitions of clinically significant cancer based on a combination of Gleason grade and cancer burden (cancer core length) were derived. RESULTS Mean±SD patient age was 61±6.4 years (range 44 to 74) and mean prostate specific antigen was 9.7±5.9 ng/ml (range 0.8 to 36.2). We reconstructed 665 foci. The total cancer core length from all positive biopsies for a particular lesion that detected more than 95% of lesions 0.5 ml or greater and 0.2 ml or greater was 10 mm or greater and 6 mm or greater, respectively. The maximum cancer core length that detected more than 95% of lesions 0.5 ml or greater and 0.2 ml or greater was 6 mm or greater and 4 mm or greater, respectively. We combined these cancer burden thresholds with dominant and nondominant Gleason pattern 4 to derive 2 definitions of clinically significant disease. CONCLUSIONS Transperineal prostate mapping may provide an effective method to risk stratify men with localized prostate cancer. The definitions that we present require prospective validation.

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.

A biopsy simulation study to assess the accuracy of several transrectal ultrasonography (TRUS)‐biopsy strategies compared with template prostate mapping biopsies in patients who have undergone radical prostatectomy

Study Type – Diagnostic (validating cohort)

MR Navigated Breast Surgery: Method and Initial Clinical Experience

3D dynamic contrast enhanced magnetic resonance (MR) images may help to reduce the high re-excision rate associated with breast conserving surgery. However these images are acquired prone, whilst surgery is performed supine which results in a large deformation that limits their usefulness. We describe here a registration technique based on a biomechanical model to account for soft tissue deformation between prone MR imaging and surgery. The accuracies of the individual registration steps are assessed off-line. We then report our first clinical experience with an image-guided surgery system which incorporates these algorithms. The systems accuracy is assessed against tracked ultrasound images, and is determined to be around 5mm for this case.

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.

Cadaver Validation of the Use of Ultrasound for 3D Model Instantiation of Bony Anatomy in Image Guided Orthopaedic Surgery

We present cadaver validation of a method that uses tracked ultrasound to instantiate and register 3D statistical shape models (SSMs) of 3 femurs and 2 pelves. The SSMs were generated directly from the deformation fields obtained from non-rigid registration of CT images to a single target CT image. Ultrasound images were obtained from three intact cadavers. These were used to instantiate the model by iteratively minimising the RMS distance between the model surface and the ultrasound-derived bone surface points. The RMS distance between the instantiated model surface and the CT-derived bone surface was less than 3.72mm in the region of the femoral head and acetabulum. We conclude that SSMs of the femur and pelvis may be instantiated and registered to surgical space to within a clinically acceptable accuracy using intraoperative ultrasound. This potentially could reduce the invasiveness of orthopaedic procedures, and remove the requirement for a preoperative CT scan.

A hybrid fem-based method for aligning prone and supine images for image guided breast surgery

In breast conserving surgery clinicians may benefit from information from preoperative images (e.g. high quality dynamic contrast enhanced magnetic resonant image obtained in the prone position), by registering them to the supine patient on the operating table in the theatre. Due to large deformation involved between prone and supine, either non-rigid intensity-based mage registration methods or biomechanical model based methods alone have limited success. In this paper, we proposed a hybrid finite element method (FEM) based image registration method by combining patient-specific biomechanical models with nonrigid intensity-based image registration methods. FEM-based biomechanical models were used to estimate the major deformation of breasts while non-rigid intensity based image registration methods were used to recover the difference between experimental acquisitions and FE predictions due to the simplifications and approximations of biomechanical models. The proposed method shows a good performance for image registration, demonstrated by the experimental example of prone and supine MR breast image registration.

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 framework for image-guided breast surgery

Breast-conserving surgery for the treatment of cancer frequently requires a repeat operation due to the initial excision of the tumour being incomplete. Improved image guidance, using preoperative MR images, might help to reduce this high re-excision rate. Since the diagnostic MR images are acquired prone, but surgery is performed supine, significant deformation of the soft tissue of the breast occurs. We have developed an approach to account for this deformation based on a patient-specific biomechanical model. The model is constructed from a supine MR image, and it is used to initialize a non-rigid intensity-based registration of the diagnostic prone MR image with the supine image. In the operating theatre the surface of the breast is acquired with a stereo camera, and the model is deformed to match this surface in order to predict the position of the lesion. We illustrate our framework with initial results for one patient case, in which we estimate our target registration error to be 4mm.