Biomedical subjects

C Studholme

Publications and source records attributed to C Studholme.

26 records · Page 2Linked to original sources

PubMed2000

Does performing image registration and subtraction in ictal brain SPECT help localize neocortical seizures?

UNLABELLED: Ictal brain SPECT (IS) findings in neocortical epilepsy (patients without mesiotemporal sclerosis) can be subtle. This study is aimed at assessing how the seizure focus identification was improved by the inclusion of individual IS and interictal brain SPECT (ITS)-MRI image registration as well as performing IS - ITS image subtraction. METHODS: The study involved the posthoc analysis of 64 IS scans using 99mTc-ethyl cysteinate dimer that were obtained in 38 patients without mesiotemporal sclerosis but with or without other abnormalities on MRI. Radiotracer injection occurred during video-electroencephalographic (EEG) monitoring. Patients were injected 2-80 s (median time, 13 s) after clinical or EEG seizure onset. All patients had sufficient follow-up to correlate findings with the SPECT results. All patients had ITS and MRI, including a coronal volume sequence used for registration. Image registration (IS and ITS to MRI) was performed using automated software. After normalization, IS - ITS subtraction was performed. The IS, ITS, and subtraction studies were read by 2 experienced observers who were unaware of the clinical data and who assessed the presence and localization of an identifiable seizure focus before and after image registration and subtraction. Correlation was made with video-EEG (surface and invasive) and clinical and surgical follow-up. RESULTS: Probable or definite foci were identified in 38 (59%) studies in 33 (87%) patients. In 52% of the studies, the image registration aided localization, and in 58% the subtraction images contributed additional information. In 9%, the subtraction images confused the interpretation. In follow-up after surgery, intracranial EEG or video-EEG monitoring (or both) has confirmed close or reasonable localization in 28 (74%) patients. In 6 (16%) patients, SPECT indicated false seizure localization. CONCLUSION: Image registration and image subtraction improve the localization of neocortical seizure foci using IS, but close correlation with the original images is required. False localizations occur in a minority of patients.

Adult↗

PubMed1998

Visual assessment of the accuracy of retrospective registration of MR and CT images of the brain.

In a previous study we demonstrated that automatic retrospective registration algorithms can frequently register magnetic resonance (MR) and computed tomography (CT) images of the brain with an accuracy of better than 2 mm, but in that same study we found that such algorithms sometimes fail, leading to errors of 6 mm or more. Before these algorithms can be used routinely in the clinic, methods must be provided for distinguishing between registration solutions that are clinically satisfactory and those that are not. One approach is to rely on a human observer to inspect the registration results and reject images that have been registered with insufficient accuracy. In this paper, we present a methodology for evaluating the efficacy of the visual assessment of registration accuracy. Since the clinical requirements for level of registration accuracy are likely to be application dependent, we have evaluated the accuracy of the observer's estimate relative to six thresholds: 1-6 mm. The performance of the observers was evaluated relative to the registration solution obtained using external fiducial markers that are screwed into the patient's skull and that are visible in both MR and CT images. This fiducial marker system provides the gold standard for our study. Its accuracy is shown to be approximately 0.5 mm. Two experienced, blinded observers viewed five pairs of clinical MR and CT brain images, each of which had each been misregistered with respect to the gold standard solution. Fourteen misregistrations were assessed for each image pair with misregistration errors distributed between 0 and 10 mm with approximate uniformity. For each misregistered image pair each observer estimated the registration error (in millimeters) at each of five locations distributed around the head using each of three assessment methods. These estimated errors were compared with the errors as measured by the gold standard to determine agreement relative to each of the six thresholds, where agreement means that the two errors lie on the same side of the threshold. The effect of error in the gold standard itself is taken into account in the analysis of the assessment methods. The results were analyzed by means of the Kappa statistic, the agreement rate, and the area of receiver-operating-characteristic (ROC) curves. No assessment performed well at 1 mm, but all methods performed well at 2 mm and higher. For these five thresholds, two methods agreed with the standard at least 80% of the time and exhibited mean ROC areas greater than 0.84. One of these same methods exhibited Kappa statistics that indicated good agreement relative to chance (Kappa > 0.6) between the pooled observers and the standard for these same five thresholds. Further analysis demonstrates that the results depend strongly on the choice of the distribution of misregistration errors presented to the observers.

Algorithms↗

PubMed1997

Evaluation of the limits of visual detection of image misregistration in a brain fluorine-18 fluorodeoxyglucose PET-MRI study.

In routine clinical work, registration accuracy is assessed by visual inspection. However, the accuracy of visual assessment of registration has not been evaluated. This study establishes the limits of visual detection of misregistration in a registered brain fluorine-18 fluorodeoxyglucose positron emission tomography to magnetic resonance image volume. The "best" registered image volume was obtained by automatic registration using mutual information optimization. Translational movements by 1 mm, 2 mm, 3 mm and 4 mm, and rotational movements by 1 degrees , 2 degrees , 3 degrees and 4 degrees in the positive and negative directions in the x- (lateral), y- (anterior-posterior) and z- (axial) axes were introduced to this standard. These 48 images plus six "best" registered images were presented in random sequence to five observers for visual categorization of registration accuracy. No observer detected a definite misregistration in the "best" registered image. Evaluation for inter-observer variation using observer pairings showed a high percentage of agreement in assigned categories for both translational and rotational misregistrations. Assessment of the limits of detection of misregistration showed that a 2-mm translational misregistration was detectable by all observers in the x- and y-axes and 3-mm translational misregistration in the z-axis. With rotational misregistrations, rotation around the z-axis was detectable by all at 2 degrees rotation whereas rotation around the y-axis was detected at 3-4 degrees . Rotation around the x-axis was not symmetric with a positive rotation being identified at 2 degrees whereas negative rotation was detected by all only at 4 degrees. Therefore, visual analysis appears to be a sensitive and practical means to assess image misregistration accuracy. The awareness of the limits of visual detection of misregistration will lead to increase care when evaluating registration quality in both research and clinical settings.

Brain↗

PubMed1997

Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures.

Approaches using measures of voxel intensity similarity are showing promise in fully automating magnetic resonance (MR) and positron emission tomography (PET) image registration in the head, without requiring extraction and identification of corresponding structures. In this paper a method of multiresolution optimization of these measures is described and five alternative measures are compared: cross correlation, minimization of corresponding PET intensity variation, moments of the distribution of values in the intensity feature space, entropy of the intensity feature space and mutual information. Their ability to recover registration is examined for ten clinically acquired image pairs with respect to the size of initial misregistration, the precision of the final result, and the accuracy assessed by visual inspection. The mutual information measure proved the most robust to initial starting estimate, successfully registering 98.8% of 900 trial misregistrations. Success is defined as providing a visually acceptable solution to a trained observer. A high resolution search (1/16 mm step size) of 30 trial misregistrations showed that optimization using the mutual information measure provided solutions with 0.13 mm, 0.11 mm and 0.17 mm standard deviations in the three Cartesian axes of the translation vector and 0.2 degree, 0.3 degree and 0.2 degree standard deviations for rotations about the three axes. The algorithm takes between 4 and 8 minutes to run on a typical workstation, including visual inspection of the result.

Algorithms↗

PubMed1996

Automated 3-D registration of MR and CT images of the head.

This paper discusses the application of voxel similarity measures in the automated registration of clinically acquired MR and CT data of the head. We describe a novel single-start multi-resolution approach to the optimization of these measures, and the issues involved in applying this to data having a range of different fields of view and sampling resolution. We compare four proposed measures of voxel similarity using the same optimization scheme when presented with 10 pairs of images with a range of initial misregistrations. The registration estimates are compared with those provided by manual point-based registration and evaluated by visual inspection to give an assessment of the robustness and accuracy of the different measures. One full-volume CT image set is used to investigate the performance of each measure when used to align truncated images from different regions in the head. The soft tissue correlation and mutual information measures were found to provide the most robust measures of misregistration, providing results comparable to or better than those from manual point-based registration for all but the most truncated image volumes.

Brain↗

PubMed1994

Combined and three-dimensional rendered multimodal data for planning cranial base surgery: a prospective evaluation.

Magnetic resonance (MR), X-ray computed tomography (CT), and angiographic images best depict soft tissue, bone, and blood vessels respectively. No one on its own is sufficient in the preoperative assessment of cranial base lesions. We have developed and evaluated a computational technique for the three-dimensional (3D) combination and display of multimodality images for planning cranial base surgery. This evaluation was prospective and performed in such a way that the results could be quantified. Eight patients (three acoustic neuromas, four subfrontal and suprasellar meningiomas, and one petrous apex meningioma) underwent MR, CT, and MR angiographic investigations. These images were registered with anatomical landmarks rather than an external frame. Two techniques were used to display the resulting combined images: multiple slices in which bone from CT was overlaid on soft tissue from registered MR and pseudo-3D-rendered movie sequences showing bone from CT, lesions and optic nerves from MR, and blood vessels from MR angiography. The advantages of the combined displays compared with those of conventional methods of viewing were assessed prospectively by the operating surgeon and by an independent surgeon, and the results were compared with operative findings. The preoperative assessment showed a significant improvement (P < 0.05, sign test) in the depiction of both individual structures (lesion and bone from overlaid slices and lesion and vasculature from 3D-rendered displays) and structural relationships (tumor-bone relationships from overlaid slices and of tumor-vasculature relationships from 3D-rendered displays). The operative findings indicated that a more accurate interpretation of this information was possible from the combined images.

Adult↗

PubMedDate not supplied

Comparison and evaluation of retrospective intermodality brain image registration techniques.

PURPOSE: The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. To ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold standard results until after their results had been submitted. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification. METHOD: Image volumes of three modalities (CT, MR, and PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/ or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOIs), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c". RESULTS: This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration. CONCLUSION: Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Brain↗

PubMedDate not supplied

Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm.

PURPOSE: Clinical imaging systems, especially MR scanners, frequently have errors of a few percent in their voxel dimensions. We evaluate a nine degree of freedom registration algorithm that maximizes mutual information for determining scaling errors. We evaluate it by registering MR and CT images for each of five patients (patient scaling) and by registering MR images of a phantom to a computer model of the phantom (phantom scaling). METHOD: Each scaling method was validated using bone-implanted markers localized in the patient images and also intraoperatively. The root mean square residual in the alignment of the fiducial markers [fiducial registration error (FRE)] was determined without scale correction, with patient scaling, and with phantom scaling. RESULTS: Each scaling method significantly reduced the average FRE (p < 0.05) for MR to CT registration and for MR to physical space registration, indicating that voxel scaling errors were reduced. The greater reduction in scaling errors was achieved using the phantom scaling method. CONCLUSION: We have demonstrated that a nine degree of freedom registration algorithm that maximizes mutual information can significantly reduce scaling errors in MR.

Algorithms↗