@article{fichtinger2012j,

title = {Multi-modal registration of speckle-tracked freehand 3D ultrasound to CT in the lumbar spine},

author = {Andrew Lang and Parvin Mousavi and Sean Gill and Gabor Fichtinger and Purang Abolmaesumi},

url = {https://www.sciencedirect.com/science/article/pii/S1361841511001149},

year  = {2012},

date = {2012-01-01},

journal = {Medical image analysis},

volume = {16},

issue = {3},

pages = {675-686},

publisher = {Elsevier},

abstract = {A method for registration of speckle-tracked freehand 3D ultrasound (US) to preoperative CT volumes of the spine is proposed. We register the US volume to the CT volume by creating individual US “sub-volumes”, each consisting of a small section of the entire US volume. The registration proceeds incrementally from the beginning of the US volume to the end, registering every sub-volume, where each sub-volume contains overlapping images with the previous sub-volume. Each registration is performed by generating simulated US images from the CT volume. As a by-product of our registration, the significant drift error common in speckle-tracked US volumes is corrected for. Results are validated through a phantom study of plastic spine phantoms created from clinical patient CT data as well as an animal study using a lamb cadaver. Results demonstrate that we were able to successfully register a speckle-tracked …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2012b,

title = {Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine},

author = {Sean Gill and Purang Abolmaesumi and Gabor Fichtinger and Jonathan Boisvert and David Pichora and Dan Borshneck and Parvin Mousavi},

url = {https://www.sciencedirect.com/science/article/pii/S1361841510001003},

year  = {2012},

date = {2012-01-01},

journal = {Medical image analysis},

volume = {16},

issue = {3},

pages = {662-674},

publisher = {Elsevier},

abstract = {We present a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions. In addition, we introduce a comprehensive validation scheme that accounts for changes in the curvature of the spine between preoperative and intraoperative imaging. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. During each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric is calculated with the real US image. Validation studies are performed on CT and US images from a sheep cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2010g,

title = {Registration of a statistical shape model of the lumbar spine to 3D ultrasound images},

author = {Siavash Khallaghi and Parvin Mousavi and Ren Hui Gong and Sean Gill and Jonathan Boisvert and Gabor Fichtinger and David Pichora and Dan Borschneck and Purang Abolmaesumi},

url = {https://link.springer.com/chapter/10.1007/978-3-642-15745-5_9},

year  = {2010},

date = {2010-01-01},

pages = {68-75},

publisher = {Springer Berlin Heidelberg},

abstract = {Motivation: Spinal needle injections are technically demanding procedures. The use of ultrasound image guidance without prior CT and MR imagery promises to improve the efficacy and safety of these procedures in an affordable manner. Methodology: We propose to create a statistical shape model of the lumbar spine and warp this atlas to patient-specific ultrasound images during the needle placement procedure. From CT image volumes of 35 patients, statistical shape model of the L3 vertebra is built, including mean shape and main modes of variation. This shape model is registered to the ultrasound data by simultaneously optimizing the parameters of the model and its relative pose. Ground-truth data was established by printing 3D anatomical models of 3 patients using a rapid prototyping. CT and ultrasound data of these models were registered using fiducial markers. Results: Pairwise …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2010l,

title = {Ultrasound guided spine needle insertion},

author = {Elvis CS Chen and Parvin Mousavi and Sean Gill and Gabor Fichtinger and Purang Abolmaesumi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7625/762538/Ultrasound-guided-spine-needle-insertion/10.1117/12.843716.short},

year  = {2010},

date = {2010-01-01},

volume = {7625},

pages = {1028-1035},

publisher = {SPIE},

abstract = {An ultrasound (US) guided, CT augmented, spine needle insertion navigational system is introduced. The system consists of an electromagnetic (EM) sensor, an US machine, and a preoperative CT volume of the patient anatomy. Three-dimensional (3D) US volume is reconstructed intraoperatively from a set of two-dimensional (2D) freehand US slices, and is coregistered with the preoperative CT. This allows the preoperative CT volume to be used in the intraoperative clinical coordinate. The spatial relationship between the patient anatomy, surgical tools, and the US transducer are tracked using the EM sensor, and are displayed with respect to the CT volume. The pose of the US transducer is used to interpolate the CT volume, providing the physician with a 2D "x-ray vision" to guide the needle insertion. Many of the system software components are GPU-accelerated, allowing real-time performance of the guidance …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2010o,

title = {Target motion compensation in MRI-guided prostate biopsy with static images},

author = {Hadi Tadayyon and Andras Lasso and Sean Gill and Aradhana Kaushal and Peter Guion and Gabor Fichtinger},

url = {https://ieeexplore.ieee.org/abstract/document/5626497/},

year  = {2010},

date = {2010-01-01},

pages = {5416-5419},

publisher = {IEEE},

abstract = {Purpose 

MRI-guided prostate needle biopsy requires compensation for organ motion between target planning and needle placement. 

Methods 

We propose slice-to-volume registration algorithms for tracking the prostate motion. Three orthogonal intra-operative slices are acquired in the approximate center of the prostate and registered with a high-resolution target planning volume. Both rigid and deformable scenarios were implemented. MRI-guided robotic prostate biopsy cases were analyzed retrospectively. 

Results 

Average registration errors were 2.55mm for the rigid algorithm and 2.05mm for the deformable algorithm. 

Conclusion 

Slice-based tracking appears to be promising. Deformable registration does not seem warranted.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2010w,

title = {MRI-guided prostate motion tracking by means of multislice-to-volume registration},

author = {Hadi Tadayyon and Siddharth Vikal and Sean Gill and Andras Lasso and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7625/76252V/MRI-GUIDED-prostate-motion-tracking-by-means-of-multislice-to/10.1117/12.844454.short},

year  = {2010},

date = {2010-01-01},

volume = {7625},

pages = {904-911},

publisher = {SPIE},

abstract = {We developed an algorithm for tracking prostate motion during MRI-guided prostatic needle placement, with the primary application in prostate biopsy. Our algorithm has been tested on simulated patient and phantom data. The algorithm features a robust automatic restart and a 12-core biopsy error validation scheme. Simulation tests were performed on four patient MRI pre-operative volumes. Three orthogonal slices were extracted from the pre-operative volume to simulate the intra-operative volume and a volume of interest was defined to isolate the prostate. Phantom tests used six datasets, each representing the phantom at a known perturbed position. These volumes were registered to their corresponding reference volume (the phantom at its home position). Convergence tests on the phantom data showed that the algorithm demonstrated accurate results at 100% confidence level for initial misalignments of less …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2009g,

title = {Biomechanically constrained groupwise US to CT registration of the lumbar spine},

author = {Sean Gill and Parvin Mousavi and Gabor Fichtinger and Elvis Chen and Jonathan Boisvert and David Pichora and Purang Abolmaesumi},

url = {https://link.springer.com/chapter/10.1007/978-3-642-04268-3_99},

year  = {2009},

date = {2009-01-01},

pages = {803-810},

publisher = {Springer Berlin Heidelberg},

abstract = {Registration of intraoperative ultrasound (US) with preoperative computed tomography (CT) data for interventional guidance is a subject of immense interest, particularly for percutaneous spinal injections. We propose a biomechanically constrained group-wise registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm simulates an US image from the CT data at each iteration of the registration. This simulated US image is used to calculate an intensity based similarity metric with the real US image. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. Validation is performed on CT and US images from a phantom …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2009i,

title = {Group-wise registration of ultrasound to CT images of human vertebrae},

author = {Sean Gill and Parvin Mousavi and Gabor Fichtinger and David Pichora and Purang Abolmaesumi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7261/72611O/Group-wise-registration-of-ultrasound-to-CT-images-of-human/10.1117/12.813776.short},

year  = {2009},

date = {2009-01-01},

volume = {7261},

pages = {556-564},

publisher = {SPIE},

abstract = {Automatic registration of ultrasound (US) to computed tomography (CT) datasets is a challenge of considerable interest, particularly in orthopaedic and percutaneous interventions. We propose an algorithm for group-wise volume-to-volume registration of US to CT images of the lumbar spine. Each vertebra in CT is treated as a sub-volume and transformed individually. The sub-volumes are then reconstructed into a single volume. The algorithm dynamically combines simulated US reflections from the vertebrae surfaces and surrounding soft tissue in the reconstructed CT, with scaled CT data to simulate US images of the spine anatomy. The simulated US data is used to register preoperative CT data to intra-operative US images. Covariance Matrix Adaption - Evolution Strategy (CMA-ES) is utilized as the optimization strategy. The registration is tested using a phantom of the lumbar spine (L3-L5). Initial misalignments …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2008k,

title = {Intraoperative prostate tracking with slice-to-volume registration in MR},

author = {Sean Gill and Purang Abolmaesumi and Siddharth Vikal and Parvin Mousavi and Gabor Fichtinger},

url = {https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4ce3fd74a1ce4adc39da59cb4f53515b85b1ea95},

year  = {2008},

date = {2008-01-01},

journal = {International conference of the society for medical innovation and technology},

pages = {154-158},

abstract = {A slice-to-volume registration algorithm, to be used for prostate tracking, is presented. The technique is designed to provide patient tracking information for prostate biopsy performed under MR guidance. Interventional MR slices are acquired from a static physical plane just prior to needle insertion. These slices are registered to a high resolution volume of the patient. Registration is performed at two resolutions, with the result of the low resolution registration used as the initial alignment of the high resolution step. Two versions of the algorithm are implemented: registration with a transverse slice and registration with intersecting transverse and sagittal slices. Validation is performed on five high resolution patient data sets. After segmentation of the prostate, an interventional MR slice is simulated and subsequently translated and rotated from its original position. Registration performed to recover the original position resulted in average target registration errors below 1 mm, for both the single transverse slice and the combination of a transverse and a sagittal slice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}