High image noise in low-dose fluoroscopic x-ray often necessitates additional radiographic-dose exposures to patients to
include as part of the medical records. We present an image registration based approach for the generation of highquality
images from a sequence of low-dose x-ray fluoroscopy exposures. Image subregions in consecutively acquired
fluoroscopy frames are registered to subregions in a pre-selected reference frame using a two-dimensional
transformation model. Frames neighboring the reference image are resampled using a smooth deformation field
generated by interpolation of the individual subregion deformations. Motion-corrected neighboring frames are then
combined with the reference frame using a weighted, frequency-specific multi-resolution combination method. Using
this method, image noise (localized standard deviation) was reduced by 38% in phantom data and by 29% in clinical
barium swallow examinations. We demonstrate an effective method for generating a simulated radiographic-dose x-ray
image from a set of consecutively acquired low-dose fluoroscopy images. The significant improvement in image quality
indicates the potential of this approach to lower average patient dose by substantially reducing the need for additional
exposures for patient records.
The prostate is known to move between daily fractions during the course of radiation therapy using external beams. This
movement causes problem with 3D conformal or intensity-modulated radiation therapy, in which tight margins are used
for treatment planning. To minimize the adverse effect of this motion on dose delivery, daily localization of the prostate
with respect to the planning CT is necessary. Current ultrasound-based localization systems require manual alignment of
ultrasound images with the planning CT. The resulting localization is subjective and has high interobserver variability.
To reduce the alignment uncertainty and increase the setup efficiency, we proposed an automatic prostate alignment
method using a volume subdivision-based elastic image registration algorithm. The algorithm uses normalized mutual
information as the measure of image similarity between the daily 3D ultrasound images and the planning CT. The
prostate contours on the CT are mapped to the ultrasound space by applying the transformation fields from image
registration. The displacement of the center-of-mass of the mapped contours is calculated for automatic patient setup. For
validation purposes, six experts independently and manually aligned the archived CT and 3D ultrasound images using
the SonArray system and reported their readings as shifts along the three principal axes. The mean shift and standard
deviation of the readings along each axis were calculated. We regarded the automatic alignment as being acceptable if
the difference between the mean shift and the automatic shift is within two times the standard deviation. Three out of
five patients were successfully aligned with two failures.
KEYWORDS: Image segmentation, 3D image processing, 3D modeling, Image processing algorithms and systems, Echocardiography, Image registration, 3D acquisition, Algorithm development, Image restoration, Medical imaging
Purpose: We report a deformable model (DM)-based fully automatic segmentation of the left ventricular (LV) myocardium (endocardium + epicardium) in real-time three-dimensional (3D) echocardiography. Methods: Initialization of the DM is performed through automated mutual information-based registration of the image to be segmented with a 3D template (image + corresponding endo-epicardial wiremesh). The initialized endocardial and epicardial wiremesh templates are then simultaneously refined iteratively under the joint influence of mesh-derived internal forces, image-derived external (gradient vector flow-based) forces, and endo-epicardium mesh-interaction
forces. Incorporation of adaptive mesh-interaction forces into the DM refinement, a novelty of the current work, ensures appropriate relative endo-epicardial orientation during simultaneous refinement. Repeating for the entire cardiac sequence provides the segmented myocardium for all phases. Preliminary comparison is presented between automatic and expert-defined myocardial segmentation for five subjects imaged in clinical settings using a Philips
SONOS 7500 scanner. Results: Root mean square (rms) radial distance error between the algorithm-determined and expert-traced endocardial and epicardial contours in six predetermined planar views was 3.86 ± 0.72 mm and 4.0 ± 0.63 mm in end-diastole, 3.9 ± 0.51 mm and 4.04 ± 0.65 mm in systole, respectively. Mean absolute error between
average myocardial thickness calculated using automatic and expert-defined contours was 1.64 ± 0.56 mm (apical), 1.3 ± 0.58 mm (mid) and 1.46 ± 0.45 mm (basal). The absolute difference in ejection fraction calculated using our algorithm and by the expert using the TomTec software was 7.2 ± 0.84 %. Conclusion: We demonstrate successful segmentation of LV myocardium, which allows clinically important LV structure and function (e.g. wall thickness,
LV volume and ejection fraction) to be tracked over the entire cardiac cycle.
We report an algorithm for automatic elastic registration of whole body computed tomography (CT) and positron emission tomography (PET) images, which would allow accurate localization of viable tumors visible clearly in the perfusion PET image with respect to the anatomy that is better delineated in the CT image. The algorithm generates an elastic transformation field (TA) from a unique quaternion-based interpolation of multiple rigid-body registrations
(based on maximization of normalized mutual information), each obtained for hierarchically subdivided image blocks. Fifteen pairs of clinically acquired CT and PET scans were registered using our algorithm. All image pairs registered visually correct. For validation, a set of anatomic landmarks was identified independently by two clinical experts in both images of each CT-PET pair. Based on each expert's marked points, two thin-plate spline-based deformation fields (TE1, TE2) were determined for each image pair. Interobserver variability calculated as the mean difference between transformed locations obtained using each of 'TE1', 'TE2' and 'TA' (5.8 mm) was comparable to the interobserver variability calculated as the mean difference in transformed locations obtained using only 'TE1' and 'TE2' (4.5 mm),
suggesting that the accuracy of the registration algorithm is comparable to that of the experts.
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