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Biomedical subjects

J Michael Fitzpatrick

Publications and source records attributed to J Michael Fitzpatrick.

13 recordsLinked to original sources
PubMed2005

Accuracy of customized miniature stereotactic platforms.

In this study, a new system was evaluated for implanting deep-brain stimulators based on a one-piece platform for each trajectory customized from a preoperative planning image. During surgery, the platform is attached to skull-implanted posts that extend through the scalp. The platform acts as a miniature stereotactic frame to provide guidance for parallel cannulas as they are advanced through a burr hole to the target. Accuracy is determined from a postoperative CT. For each implantation, the distance between the position observed in the postoperative image and the position calculated relative to the platform from the preoperative image is our measure of error. Because this measure incorporates the surgical error of electrode anchoring, brain shift between preoperative and postoperative scanning, and error in the measurement of the position of the electrode in CT, it will tend to overestimate the true error. The mean error was 2.8 mm for 20 implantations. These data reflect favorably the accuracy of this system when compared with others.

Artifacts↗
PubMed2005

In vitro assessment of image-guided otologic surgery: submillimeter accuracy within the region of the temporal bone.

OBJECTIVES: Application of image-guided surgery to otology has been limited by the need for submillimeter accuracy via a fiducial system that is easily usable (noninvasive and nonobstructive). METHODS: A dental bite-block was fitted with a rigid frame with 7 fiducial markers surrounding each external ear. The temporal bones of 3 cadaveric skulls were removed and replaced with surgical targets arranged in a bull's-eye pattern about the centroid of each temporal bone. The surgical targets were identified both within CT scans and in physical space using an infrared optical tracking system. The difference between positions in CT space versus physical space was calculated as target registration error. RESULTS: A total of 234 independent target registration errors were calculated. Mean +/- standard deviation = 0.73 mm +/- 0.25 mm. CONCLUSIONS: These findings show that image-guided otologic surgery with submillimeter accuracy is achievable with a minimally invasive fiducial frame. Significance In vivo validation of the system is ongoing. With such validation, this system may facilitate clinically applicable image-guided otologic surgery. EBM RATING: A.

Humans↗
PubMed2005

Image-guided surgery: what is the accuracy?

PURPOSE OF REVIEW: Use of image-guided surgery (IGS) systems in otolaryngology, particularly rhinology, has grown exponentially in recent years. Central to their use is the understanding of the accuracy of each system. The purpose of this review is to discuss the error inherent in all IGS systems. A standardized technique (currently used in the engineering literature) for understanding and reporting error in IGS systems is reviewed. Using this technique, the error of commercially available IGS systems is reviewed. RECENT FINDINGS: The most commonly used IGS systems depend on the conformation of the skin, as opposed to relying on bone-implanted devices. For these systems, mean accuracies 2 mm or less are routinely reported. This finding is independent of fiducial markers (eg, proprietary headsets, skin-affixed markers, or laser scanning of skin surfaces). Techniques of fiducial localization and registration of CT scans to intraoperative anatomy are proprietary to each company. As such, there is great variability in reporting system specifications-particularly error of IGS systems. This lack of standardization makes comparison of one system to another difficult if not impossible. SUMMARY: Image-guided surgery systems commonly used in rhinology report mean accuracies of 2 mm or less. Surgeons must be aware that this value represents a mean of a distribution of errors. As such, 95% of the time error can be expected to be less than approximately 1.7 times its mean value. However, outliers (errors much larger and much smaller than the mean) may exist for each IGS intervention. As noted, IGS systems function to complement-not replace-knowledge of surgical anatomy.

Humans↗
PubMed2005

Minimally invasive, image-guided, facial-recess approach to the middle ear: demonstration of the concept of percutaneous cochlear access in vitro.

HYPOTHESIS: Image-guided surgery will permit accurate access to the middle ear via the facial recess using a single drill hole from the lateral aspect of the mastoid cortex. BACKGROUND: The widespread use of image-guided methods in otologic surgery has been limited by the need for a system that achieves the necessary level of accuracy with an easy-to-use, noninvasive fiducial marker system. We have developed and recently reported such a system (accuracy within the temporal bone = 0.76 +/- 0.23 mm; n = 234 measurements). With this system, image-guided otologic surgery is feasible. METHODS: Skulls (n = 2) were fitted with a dental bite-block affixed fiducial frame and scanned by computed tomography using standard temporal-bone algorithms. The frame was removed and replaced with an infrared emitter used to track the skull during dissection. Tracking was accomplished using an infrared tracker and commercially available software. Using this system in conjunction with a tracked otologic drill, the middle ear was approached via the facial recess using a single drill hole from the lateral aspect of the mastoid cortex. The path of the drill was verified by subsequently performing a traditional temporal bone dissection, preserving the tunnel of bone through which the drill pass had been made. RESULTS: An accurate approach to the middle ear via the facial recess was achieved without violating the canal of the facial nerve, the horizontal semicircular canal, or the external auditory canal. CONCLUSIONS: Image-guided otologic surgery provides access to the cochlea via the facial recess in a minimally invasive, percutaneous fashion. While the present study was confined to in vitro demonstration, these exciting results warrant in vivo testing, which may lead to clinically applicable access.

Cochlea↗
PubMed2004

Registration of medical images using an interpolated closest point transform: method and validation.

Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information-based registration methods (see for instance [IEEE Trans. Med. Imag. 22 (8) (2003) 986] for a review on these methods) have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the Iterative Closest Point algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. Pre-computing the closest point map speeds up the process because at each iteration point correspondence can be established by table lookup. We also show that because the closest point map is defined on a regular grid it introduces a registration error and we propose an interpolation scheme that addresses this issue. The method has been tested both on synthetic and real images, and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.

Algorithms↗
PubMed2004

MRI simulator with object-specific field map calculations.

A new MRI simulator has been developed that generates images of realistic objects for arbitrary pulse sequences executed in the presence of static field inhomogeneities, including those due to magnetic susceptibility, variations in the applied field, and chemical shift. In contrast to previous simulators, this system generates object-specific inhomogeneity patterns from first principles and propagates the consequent frequency offsets and intravoxel dephasing through the acquisition protocols to produce images with realistic artifacts. The simulator consists of two parts. The input to part 1 is a set of "susceptibility voxels" that describe the magnetic properties of the object being imaged. It calculates a frequency offset for each voxel by computing the size of the static field offset at each voxel in the image based on the magnetic susceptibility of each tissue type within all voxels. The method of calculation is a three-dimensional convolution of the susceptibility-voxels with a kernel derived from a previously published method and takes advantage of the superposition principle to include voxels with mixtures of substances of differing susceptibilities. Part 2 produces both a signal and a reconstructed image. Its inputs include a voxel-based description of the object, frequency offsets computed by part 1, applied static field errors, chemical shift values, and a description of the imaging protocol. Intravoxel variations in both static field and time-dependent phase are calculated for each voxel. Validations of part 1 are presented for a known analytic solution and for experimental data from two phantoms. Part 2 was validated with comparisons to an independent simulation provided by the Montreal Neurological Institute and experimental data from a phantom.

Algorithms↗
PubMed2004

Characterizing changes in MR images with color-coded Jacobians.

Image registration is the process of establishing spatial correspondence between two images or between two image volumes. Registration can be achieved by rigid, elastic, or a combination of rigid and elastic transforms that attempt to bring the two images into coincidence. A rigid transform accounts for differences in positioning and an elastic transform describes deformations due to differences in tissue properties, temporal changes due to growth or atrophy, or differences between individuals. Deformation-based morphometry uses the resulting deformation fields from these transforms to evaluate differences between the images being registered. Three methods of registration were evaluated: rigid (affine) transformation, elastic optical flow transformation, and elastic spline transformation. All three methods produce vector deformation fields that map each point in one image to a point in the other image. A 12-color map of the transformation Jacobian was used to represent local volume changes. Using the three registration methods, color-mapped Jacobians were determined using a simulated three-dimensional block with known translation, rotation, expansion, contraction, and intensity modulations. Color-coded Jacobians were also generated for experimentally measured magnetic resonance image volumes of water-filled balloons and 7-year-old twin boys. Color-coded Jacobians overlaid on anatomical images provide a convenient method to identify regional tissue expansion and contraction.

Algorithms↗
PubMed2004

Phantom validation of coregistration of PET and CT for image-guided radiotherapy.

Radiotherapy treatment planning integrating positron emission tomography (PET) and computerized tomography (CT) is rapidly gaining acceptance in the clinical setting. Although hybrid systems are available, often the planning CT is acquired on a dedicated system separate from the PET scanner. A limiting factor to using PET data becomes the accuracy of the CT/PET registration. In this work, we use phantom and patient validation to demonstrate a general method for assessing the accuracy of CT/PET image registration and apply it to two multi-modality image registration programs. An IAEA (International Atomic Energy Association) brain phantom and an anthropomorphic head phantom were used. Internal volumes and externally mounted fiducial markers were filled with CT contrast and 18F-fluorodeoxyglucose (FDG). CT, PET emission, and PET transmission images were acquired and registered using two different image registration algorithms. CT/PET Fusion (GE Medical Systems, Milwaukee, WI) is commercially available and uses a semi-automated initial step followed by manual adjustment. Automatic Mutual Information-based Registration (AMIR), developed at our institution, is fully automated and exhibits no variation between repeated registrations. Registration was performed using distinct phantom structures; assessment of accuracy was determined from registration of the calculated centroids of a set of fiducial markers. By comparing structure-based registration with fiducial-based registration, target registration error (TRE) was computed at each point in a three-dimensional (3D) grid that spans the image volume. Identical methods were also applied to patient data to assess CT/PET registration accuracy. Accuracy was calculated as the mean with standard deviation of the TRE for every point in the 3D grid. Overall TRE values for the IAEA brain phantom are: CT/PET Fusion = 1.71 +/- 0.62 mm, AMIR = 1.13 +/- 0.53 mm; overall TRE values for the anthropomorphic head phantom are: CT/PET Fusion = 1.66 +/- 0.53 mm, AMIR = 1.15 +/- 0.48 mm. Precision (repeatability by a single user) measured for CT/PET Fusion: IAEA phantom = 1.59 +/- 0.67 mm and anthropomorphic head phantom = 1.63 +/- 0.52 mm. (AMIR has exact precision and so no measurements are necessary.) One sample patient demonstrated the following accuracy results: CT/PET Fusion = 3.89 +/- 1.61 mm, AMIR = 2.86 +/- 0.60 mm. Semi-automatic and automatic image registration methods may be used to facilitate incorporation of PET data into radiotherapy treatment planning in relatively rigid anatomic sites, such as head and neck. The overall accuracies in phantom and patient images are < 2 mm and < 4 mm, respectively, using either registration algorithm. Registration accuracy may decrease, however, as distance from the initial registration points (CT/PET fusion) or center of the image (AMIR) increases. Additional information provided by PET may improve dose coverage to active tumor subregions and hence tumor control. This study shows that the accuracy obtained by image registration with these two methods is well suited for image-guided radiotherapy.

Algorithms↗
PubMed2004

Submillimetric target-registration error using a novel, non-invasive fiducial system for image-guided otologic surgery.

OBJECTIVE: Otologic surgery is undertaken to treat ailments of the ear, including persistent infections, hearing loss, vertigo, and cancer. Typically performed on otherwise-healthy patients in outpatient facilities, the application of image-guided surgery (IGS) has been limited because accurate (<1 mm), non-invasive fiducial systems for otologic surgery have not been available. We now present such a fiducial system. METHODS: A dental bite-block was fitted with a custom-designed rigid frame with 7 fiducial markers surrounding each external ear. The bones containing the ear (i.e., the temporal bones) of 3 cadaveric skulls were removed and replaced with discs containing 13 surgical targets arranged in a cross-hair pattern about the centroid of each ear. The surgical targets (26/skull) and fiducial markers (14/skull) were identified both within CT scans using a published algorithm and in physical space using an infrared optical tracking system. Fiducial registration error (FRE), fiducial localization error (FLE), and target registration error (TRE) were calculated. RESULTS: For all trials, root mean square FRE = 0.66, FLE = 0.72, and TRE = 0.77 mm. The mean TRE for n = 234 independent targets was 0.73 with a standard deviation of 0.25 mm. CONCLUSIONS: Using a novel, non-invasive fiducial system (the EarMark), submillimetric accuracy was repeatably achieved. This system will facilitate image-guided otologic surgery.

Artifacts↗
PubMed2002

Green chemistry: science and politics of change.

The chemical industry plays a key role in sustaining the world economy and underpinning future technologies, yet is under unprecedented pressure from the effects of globalization and change in many of its traditional markets. Against this background, what will be needed for the industry to embrace efforts to make it "greener"? We explore some of the issues raised by the development of "green chemistry" techniques and identify potential barriers to their implementation by industry.

Carbon Dioxide↗