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J Michael Fitzpatrick

Publications and source records attributed to J Michael Fitzpatrick.

7 recordsLinked to original sources
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↗
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↗