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D J Hawkes

Publications and source records attributed to D J Hawkes.

At least 19 recordsLinked to original sources
PubMed2006

MRI-based measurements of respiratory motion variability and assessment of imaging strategies for radiotherapy planning.

Respiratory organ motion has a significant impact on the planning and delivery of radiotherapy (RT) treatment for lung cancer. Currently widespread techniques, such as 4D-computed tomography (4DCT), cannot be used to measure variability of this motion from one cycle to the next. In this paper, we describe the use of fast magnetic resonance imaging (MRI) techniques to investigate the intra- and inter-cycle reproducibility of respiratory motion and also to estimate the level of errors that may be introduced into treatment delivery by using various breath-hold imaging strategies during lung RT planning. A reference model of respiratory motion is formed to enable comparison of different breathing cycles at any arbitrary position in the respiratory cycle. This is constructed by using free-breathing images from the inhale phase of a single breathing cycle, then co-registering the images, and thereby tracking landmarks. This reference model is then compared to alternative models constructed from images acquired during the exhale phase of the same cycle and the inhale phase of a subsequent cycle, to assess intra- and inter-cycle variability ('hysteresis' and 'reproducibility') of organ motion. The reference model is also compared to a series of models formed from breath-hold data at exhale and inhale. Evaluation of these models is carried out on data from ten healthy volunteers and five lung cancer patients. Free-breathing models show good levels of intra- and inter-cycle reproducibility across the tidal breathing range. Mean intra-cycle errors in the position of organ surface landmarks of 1.5(1.4)-3.5(3.3) mm for volunteers and 2.8(1.8)-5.2(5.2) mm for patients. Equivalent measures of inter-cycle variability across this range are 1.7(1.0)-3.9(3.3) mm for volunteers and 2.8(1.8)-3.3(2.2) mm for patients. As expected, models based on breath-hold sequences do not represent normal tidal motion as well as those based on free-breathing data, with mean errors of 4.4(2.2)-7.7(3.9) mm for volunteers and 10.1(6.1)-12.5(6.3) mm for patients. Errors are generally larger still when using a single breath-hold image at either exhale or inhale to represent the lung. This indicates that account should be taken of intra- and inter-cycle respiratory motion variability and that breath-hold-based methods of obtaining data for RT planning may potentially introduce large errors. This approach to analysis of motion and variability has potential to inform decisions about treatment margins and optimize RT planning.

Humans↗
PubMed2006

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.

Algorithms↗
PubMed2005

Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging.

Registration using models of compressible viscous fluids has not found the general application of some other techniques (e.g., free-form-deformation (FFD)) despite its ability to model large diffeomorphic deformations. We report on a multi-resolution fluid registration algorithm which improves on previous work by (a) directly solving the Navier-Stokes equation at the resolution of the images, (b) accommodating image sampling anisotropy using semi-coarsening and implicit smoothing in a full multi-grid (FMG) solver and (c) exploiting the inherent multi-resolution nature of FMG to implement a multi-scale approach. Evaluation is on five magnetic resonance (MR) breast images subject to six biomechanical deformation fields over 11 multi-resolution schemes. Quantitative assessment is by tissue overlaps and target registration errors and by registering using the known correspondences rather than image features to validate the fluid model. Context is given by comparison with a validated FFD algorithm and by application to images of volunteers subjected to large applied deformation. The results show that fluid registration of 3D breast MR images to sub-voxel accuracy is possible in minutes on a 1.6 GHz Linux-based Athlon processor with coarse solutions obtainable in a few tens of seconds. Accuracy and computation time are comparable to FFD techniques validated for this application.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2004

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.

Algorithms↗
PubMed2003

Zen and the art of medical image registration: correspondence, homology, and quality.

Nonrigid registration (NRR) is routinely used in the study of neuroanatomy and function and is a standard component of analysis packages such as SPM. There remain many unresolved correspondence problems that arise from attempts to associate functional areas with specific neuroanatomy and to compare both function and anatomy across patient groups. Problems can result from ignorance of the underlying neurology which is then compounded by unjustified inferences drawn from the results of NRR. Usually the magnitude, distribution, and significance of errors in NRR are unknown so the errors in correspondences determined by NRR are also unknown and their effect on experimental results cannot easily be quantified. In this paper we review the principles by which the presumed correspondence and homology of structures is used to drive registration and identify the conceptual and algorithmic areas where current techniques are lacking. We suggest that for applications using NRR to be robust and achieve their potential, context-specific definitions of correspondence must be developed which properly characterise error. Prior knowledge of image content must be utilised to monitor and guide registration and gauge the degree of success. The use of NRR in voxel-based morphometry is examined from this context and found wanting. We conclude that a move away from increasingly sophisticated but context-free registration technology is required and that the veracity of studies that rely on NRR should be keenly questioned when the error distribution is unknown and the results are unsupported by other contextual information.

Algorithms↗
PubMed2003

Measurement and analysis of brain deformation during neurosurgery.

Recent studies have shown that the surface of the brain is deformed by up to 20 mm after the skull is opened during neurosurgery, which could lead to substantial error in commercial image-guided surgery systems. We quantitatively analyze the intraoperative brain deformation of 24 subjects to investigate whether simple rules can describe or predict the deformation. Interventional magnetic resonance images acquired at the start and end of the procedure are registered nonrigidly to obtain deformation values throughout the brain. Deformation patterns are investigated quantitatively with respect to the location and magnitude of deformation, and to the distribution and principal direction of the displacements. We also measure the volume change of the lateral ventricles by manual segmentation. Our study indicates that brain shift occurs predominantly in the hemisphere ipsi-lateral to the craniotomy, and that there is more brain deformation during resection procedures than during biopsy or functional procedures. However, the brain deformation patterns are extremely complex in this group of subjects. This paper quantitatively demonstrates that brain deformation occurs not only at the surface, but also in deeper brain structure, and that the principal direction of displacement does not always correspond with the direction of gravity. Therefore, simple computational algorithms that utilize limited intraoperative information (e.g., brain surface shift) will not always accurately predict brain deformation at the lesion.

Adult↗
PubMed2002

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.

Algorithms↗
PubMed2002

Comparison between radiological and artificial neural network diagnosis in clinical screening.

The imaging protocol of the UK multicentre magnetic resonance imaging study for screening in women at genetic risk of breast cancer aims to assist in detecting and diagnosing malignant breast lesions. In this paper, we evaluate a three-layer, feed-forward, backpropagation neural network as an artificial radiological classifier using receiver operating characteristic (ROC) curve analysis and compare the results with those obtained using a proposed radiological scoring system for the study which currently supplements the radiologist's clinical opinion, in comparison with histological diagnosis. Based on the 76 symptomatic cases evaluated, descriptive features scored by radiologists showed considerable overlap between benign and malignant, although some features such as irregular contours and heterogeneous enhancement were more often associated with malignant pathology. In this preliminary evaluation, ROC analysis showed that the proposed scoring scheme did not perform well, indicating further refinement is required. When all 23 features were used in the neural network, its performance was poorer than that of the scoring scheme. When only ten features were used, limited to descriptors of enhancement characteristics, the neural network performed similar to the scoring scheme. This comparison shows that the neural network approach to clinical diagnosis has considerable potential and warrants further development.

Breast Neoplasms↗
PubMed2002

Measures of folding applied to the development of the human fetal brain.

Previous work has suggested the existence of differences between the cerebral cortex of normal individuals, and those of patients with diseases such as epilepsy and schizophrenia. These shape abnormalities may be of developmental origin. Improved shape measures could provide useful tools for neuroscience research and patient diagnosis. We consider the theoretically desirable properties of measures of brain shape. We have implemented seven measures, three from the neuroscience literature, and four new to this field. Three of the measures are zero-order and four are second-order with respect to the surface. We validate the measures using simple geometrical shapes, and a collection of magnetic resonance scans of ten histologically normal ex vivo fetal brains with gestational ages from 19-42 weeks. We then apply the measures to MR scans from two histologically abnormal ex vivo brains. We demonstrate that our implementation of the measures is sensitive to anatomical variability rather than to the discreteness of the image data. All the measures were sensitive to changes in shape during fetal development. Several of the measures could distinguish between the normal and abnormal fetal brains. We propose a multivariate approach to studying the shape of the cerebral cortex, in which both zero-order and second-order measures are used to quantify folding.

Aging↗
PubMed2001

Medical image registration.

Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.

Algorithms↗
PubMed2001

Pre-processed image reconstruction applied to breast and brain MR imaging.

Magnetic resonance imaging (MRI) has emerged as a powerful tool in medical diagnosis and research. Although high spatial resolution images are essential in medical diagnosis and image analysis, high temporal resolution is equally important in applications of dynamic contrast-enhanced MRI or functional brain MRI. In particular, in breast MRI the ability to differentiate between benign and malignant lesions depends, in part, on the temporal resolution of the dynamic image acquisition. New applications of MRI such as multi-feature analysis of image time series data and full 3D functional MRI or event-related functional MRI require high spatial and high temporal resolution for accurate image analysis on a voxel-by-voxel basis. Currently available partial Fourier reconstruction techniques. which effectively improve the time resolution, suffer from a reduced signal to noise ratio in the reconstructed image, a decrease in spatial resolution or reconstruction artefacts, making numerical image analysis difficult. In this work we present an image reconstruction algorithm based on image recovery theory which effectively doubles the temporal resolution and results in an image quality sufficient for further numerical analysis. The developed algorithm requires a full Fourier space acquisition of a pre-contrast or baseline image prior to the reconstruction procedure of the time series partial Fourier data.

Algorithms↗
PubMed2001

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.

Algorithms↗
PubMed2000

Voxel similarity measures for 3-D serial MR brain image registration.

We have evaluated eight different similarity measures used for rigid body registration of serial magnetic resonance (MR) brain scans. To assess their accuracy we used 33 clinical three-dimensional (3-D) serial MR images, with deformable extradural tissue excluded by manual segmentation and simulated 3-D MR images with added intensity distortion. For each measure we determined the consistency of registration transformations for both sets of segmented and unsegmented data. We have shown that of the eight measures tested, the ones based on joint entropy produced the best consistency. In particular, these measures seemed to be least sensitive to the presence of extradural tissue. For these data the difference in accuracy of these joint entropy measures, with or without brain segmentation, was within the threshold of visually detectable change in the difference images.

Adult↗
PubMed2000

Design and evaluation of a system for microscope-assisted guided interventions (MAGI).

The problem of providing surgical navigation using image overlays on the operative scene can be split into four main tasks--calibration of the optical system; registration of preoperative images to the patient; system and patient tracking, and display using a suitable visualization scheme. To achieve a convincing result in the magnified microscope view a very high alignment accuracy is required. We have simulated an entire image overlay system to establish the most significant sources of error and improved each of the stages involved. The microscope calibration process has been automated. We have introduced bone-implanted markers for registration and incorporated a locking acrylic dental stent (LADS) for patient tracking. The LADS can also provide a less-invasive registration device with mean target error of 0.7 mm in volunteer experiments. These improvements have significantly increased the alignment accuracy of our overlays. Phantom accuracy is 0.3-0.5 mm and clinical overlay errors were 0.5-1.0 mm on the bone fiducials and 0.5-4 mm on target structures. We have improved the graphical representation of the stereo overlays. The resulting system provides three-dimensional surgical navigation for microscope-assisted guided interventions (MAGI).

Computer Simulation↗
PubMed2000

Sources of error in comparing functional magnetic resonance imaging and invasive electrophysiological recordings.

OBJECT: Several authors have recently reported studies in which they aim to validate functional magnetic resonance (fMR) imaging against the accepted gold standard of invasive electrophysiological monitoring. The authors have conducted a similar study, and in this paper they identify and quantify two characteristics of these data that can make such a comparison problematic. METHODS: Eight patients in whom surgery for epilepsy was performed and five healthy volunteers underwent fMR imaging to localize the part of the sensorimotor cortex responsible for hand movement. In the patient group subdural electrode mats were subsequently implanted to identify eloquent regions of the brain and the epileptogenic zone. The fMR imaging data were processed to correct for motion during the study and then registered with a postimplantation computerized tomography (CT) scan on which the electrodes were visible. The motion during imaging in the two groups studied, and the deformation of the brain between the preoperative images and postoperative scans were measured. The patients who underwent epilepsy surgery moved significantly more during fMR imaging experiments than healthy volunteers performing the same motor task. This motion had a particularly increased out-of-plane component and was significantly more correlated with the stimulus than in the volunteers. This motion was especially increased when the patients were performing a task on the side affected by the lesion. The additional motion is hard to correct and substantially degrades the quality of the resulting fMR images, making it a much less reliable technique for use in these patients than in others. Also, the authors found that after electrode implantation, the brain surface can shift more than 10 mm relative to the skull compared with its preoperative location, substantially degrading the accuracy of the comparison of electrophysiological measurements made in the deformed brain and fMR studies obtained preoperatively. CONCLUSIONS: These two findings indicate that studies of this sort are currently of limited use for validating fMR imaging and should be interpreted with care. Additional image analysis research is necessary to solve the problems caused by patients' motion and brain deformation.

Cerebral Cortex↗