@article {Lee2008a, title = {Prostate brachytherapy seed localization with Gaussian blurring and camera self-calibration}, journal = {Medical image computing and computer-assisted intervention (MICCAI)}, volume = {11}, number = {Pt 2}, year = {2008}, pages = {636{\textendash}643}, publisher = {Department of Electrical, Computer Engineering, Johns Hopkins University, Baltimore, MD, USA}, abstract = {

A tomosynthesis-based prostate brachytherapy seed localization method is described Gaussian-blurred images are computed from a limited number of X-ray images, and a 3-D volume is reconstructed by backprojection Candidate seed locations are extracted from the reconstructed volume, false positive seeds are removed by optimizing a local cost function In case where the estimated pose error is large, a self-calibration process corrects the estimation error of the intrinsic camera parameters, the translation of the pose in order to improve the reconstruction Simulation, phantom experiment results imply that the implanted seed locations can be estimated from four or five images depending on the number of seeds The algorithm was also validated using patient data, successfully localizing the implanted seeds

}, keywords = {Artifacts, Brachytherapy, Calibration, Computer-Assisted, Humans, instrumentation/methods/standards, Male, methods, methods/standards, Normal Distribution, Prostatic Neoplasms, Radiographic Image Interpretation, radiography/radiotherapy, Radiotherapy, Tomography, X-Ray Computed}, author = {Junghoon Lee and Xiaofeng Liu and Jerry L. Prince and Gabor Fichtinger} } @article {Blumenfeld2007, title = {Transperineal prostate biopsy under magnetic resonance image guidance: a needle placement accuracy study}, journal = {Journal of magnetic resonance imaging}, volume = {26}, number = {3}, year = {2007}, month = {Sep}, pages = {688{\textendash}694}, publisher = {Department of Radiology, Brigham, Women{\textquoteright}s Hospital, Harvard Medical School, Boston, Massachusetts, USA}, abstract = {

PURPOSE: To quantify needle placement accuracy of magnetic resonance image (MRI)-guided core needle biopsy of the prostate MATERIALS AND METHODS: A total of 10 biopsies were performed with 18-gauge (G) core biopsy needle via a percutaneous transperineal approach Needle placement error was assessed by comparing the coordinates of preplanned targets with the needle tip measured from the intraprocedural coherent gradient echo images The source of these errors was subsequently investigated by measuring displacement caused by needle deflection, needle susceptibility artifact shift in controlled phantom studies Needle placement error due to misalignment of the needle template guide was also evaluated RESULTS: The mean, standard deviation (SD) of errors in targeted biopsies was 6 5 +/- 3 5 mm Phantom experiments showed significant placement error due to needle deflection with a needle with an asymmetrically beveled tip (3 2-8 7 mm depending on tissue type) but significantly smaller error with a symmetrical bevel (0 6-1 1 mm) Needle susceptibility artifacts observed a shift of 1 6 +/- 0 4 mm from the true needle axis Misalignment of the needle template guide contributed an error of 1 5 +/- 0 3 mm CONCLUSION: Needle placement error was clinically significant in MRI-guided biopsy for diagnosis of prostate cancer Needle placement error due to needle deflection was the most significant cause of error, especially for needles with an asymmetrical bevel

}, keywords = {Aged, Artifacts, Biopsy, Contrast Media, diagnosis/pathology, Humans, Imaging, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male, methods, Middle Aged, Needle, Needles, Phantoms, pharmacology, Prostatectomy, Prostatic Neoplasms, Reproducibility of Results, Retrospective Studies}, doi = {10 1002/jmri 21067}, url = {http://dx doi org/10 1002/jmri 21067}, author = {Philip Blumenfeld and Nobuhiko Hata and Simon DiMaio and Kelly Zou and Steven Haker and Gabor Fichtinger and Clare M Tempany} } @article {Xu2006a, title = {Lung deformation estimation and four-dimensional CT lung reconstruction}, journal = {Journal of Academic Radiology}, volume = {13}, number = {9}, year = {2006}, month = {Sep}, pages = {1082{\textendash}1092}, publisher = {Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA sheng xu@philips com}, abstract = {

RATIONALE AND OBJECTIVES: Four-dimensional (4D) computed tomography (CT) can be used in radiation treatment planning to account for respiratory motion Current 4D CT techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of the CT scan to the patient{\textquoteright}s respiratory phase We propose a 4D CT method for lung applications to overcome these problems MATERIALS AND METHODS: A set of axial scans are taken at multiple table positions to obtain a series of two-dimensional images while the patient is breathing freely Each two-dimensional image is registered to a reference CT volume The deformation of the image with respect to the volume is used to synchronize the image with the respiratory cycle assuming that there is no phase variation along the craniocaudal direction The reconstructed 4D dataset is a series of deformable transformations of the reference volume RESULTS: A synthetic 4D dataset showed that the registration error is less than 5\% of the image deformation A swine study showed that the algorithm can generate better image quality than the image sorting method A respiratory-gated 4D dataset showed that the algorithm{\textquoteright}s result is consistent with the ground truth CONCLUSION: The algorithm can reconstruct good quality 4D images without external surrogates even if the CT scans are acquired under irregular respiratory motion The algorithm may allow for reduced radiation dose to the patient with a limited loss of image quality Although the phase variation exists along the craniocaudal direction, the 4D reconstruction is reasonably accurate

}, keywords = {Algorithms, Artifacts, Computer-Assisted, Humans, Imaging, Information Storage, Lung, methods, Movement, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, Reproducibility of Results, Respiratory Mechanics, Retrieval, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed}, doi = {10 1016/j acra 2006 05 004}, url = {http://dx doi org/10 1016/j acra 2006 05 004}, author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary} } @article {DiMaio2006, title = {Needle artifact localization in 3T MR images}, journal = {Studies in health technology and informatics}, volume = {119}, year = {2006}, pages = {120{\textendash}125}, publisher = {Brigham, Women{\textquoteright}s Hospital, Harvard Medical School, Boston 02115, USA simond@bwh harvard edu}, abstract = {

This work explores an image-based approach for localizing needles during MRI-guided interventions, for the purpose of tracking, navigation Susceptibility artifacts for several needles of varying thickness were imaged, in phantoms, using a 3 tesla MRI system, under a variety of conditions The relationship between the true needle positions, the locations of artifacts within the images, determined both by manual, automatic segmentation methods, have been quantified, are presented here

}, keywords = {Artifacts, Magnetic Resonance Imaging, Needles, United States}, author = {Simon DiMaio and Daniel Kacher and Randy Ellis and Gabor Fichtinger and Nobuhiko Hata and Gary Zientara and Lawrence Panych and Ron Kikinis and Ferenc Jolesz} }