Publications by authors named "Calvin Maurer"

32 Publications

Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography.

J Med Invest 2020 ;67(1.2):30-39

Department of Radiology, The University of Tokyo Hospital, Japan.

Statistical iterative reconstruction is expected to improve the image quality of computed tomography (CT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this review is to summarize a fast iterative reconstruction algorithm by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using in-house statistical iterative reconstruction algorithm. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512 × 512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. J. Med. Invest. 67 : 30-39, February, 2020.
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http://dx.doi.org/10.2152/jmi.67.30DOI Listing
June 2021

Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors.

Radiother Oncol 2018 May 8;127(2):332-338. Epub 2018 Mar 8.

Department of Radiation Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, Rotterdam 3075 EA, The Netherlands.

Purpose: To validate a novel deformable image registration (DIR) method for online adaptation of planning organ-at-risk (OAR) delineations to match daily anatomy during hypo-fractionated RT of abdominal tumors.

Materials And Methods: For 20 liver cancer patients, planning OAR delineations were adapted to daily anatomy using the DIR on corresponding repeat CTs. The DIR's accuracy was evaluated for the entire cohort by comparing adapted and expert-drawn OAR delineations using geometric (Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD) and Mean Surface Error (MSE)) and dosimetric (D and D) measures.

Results: For all OARs, DIR achieved average DSC, MHD and MSE of 86%, 2.1 mm, and 1.7 mm, respectively, within 20 s for each repeat CT. Compared to the baseline (translations), the average improvements ranged from 2% (in heart) to 24% (in spinal cord) in DSC, and 25% (in heart) to 44% (in right kidney) in MHD and MSE. Furthermore, differences in dose statistics (D, D and D) using delineations from an expert and the proposed DIR were found to be statistically insignificant (p > 0.01).

Conclusion: The validated DIR showed potential for online-adaptive radiotherapy of abdominal tumors as it achieved considerably high geometric and dosimetric correspondences with the expert-drawn OAR delineations, albeit in a fraction of time required by experts.
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http://dx.doi.org/10.1016/j.radonc.2018.02.014DOI Listing
May 2018

Feasibility of real-time motion management with helical tomotherapy.

Med Phys 2018 Apr 23;45(4):1329-1337. Epub 2018 Feb 23.

Accuray Incorporated, 1310 Chesapeake Terrace, Sunnyvale, CA, 94089, USA.

Purpose: This study investigates the potential application of image-based motion tracking and real-time motion correction to a helical tomotherapy system.

Methods: A kV x-ray imaging system was added to a helical tomotherapy system, mounted 90 degrees offset from the MV treatment beam, and an optical camera system was mounted above the foot of the couch. This experimental system tracks target motion by acquiring an x-ray image every few seconds during gantry rotation. For respiratory (periodic) motion, software correlates internal target positions visible in the x-ray images with marker positions detected continuously by the camera, and generates an internal-external correlation model to continuously determine the target position in three-dimensions (3D). Motion correction is performed by continuously updating jaw positions and MLC leaf patterns to reshape (effectively re-pointing) the treatment beam to follow the 3D target motion. For motion due to processes other than respiration (e.g., digestion), no correlation model is used - instead, target tracking is achieved with the periodically acquired x-ray images, without correlating with a continuous camera signal.

Results: The system's ability to correct for respiratory motion was demonstrated using a helical treatment plan delivered to a small (10 mm diameter) target. The phantom was moved following a breathing trace with an amplitude of 15 mm. Film measurements of delivered dose without motion, with motion, and with motion correction were acquired. Without motion correction, dose differences within the target of up to 30% were observed. With motion correction enabled, dose differences in the moving target were less than 2%. Nonrespiratory system performance was demonstrated using a helical treatment plan for a 55 mm diameter target following a prostate motion trace with up to 14 mm of motion. Without motion correction, dose differences up to 16% and shifts of greater than 5 mm were observed. Motion correction reduced these to less than a 6% dose difference and shifts of less than 2 mm.

Conclusions: Real-time motion tracking and correction is technically feasible on a helical tomotherapy system. In one experiment, dose differences due to respiratory motion were greatly reduced. Dose differences due to nonrespiratory motion were also reduced, although not as much as in the respiratory case due to less frequent tracking updates. In both cases, beam-on time was not increased by motion correction, since the system tracks and corrects for motion simultaneously with treatment delivery.
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http://dx.doi.org/10.1002/mp.12791DOI Listing
April 2018

Reconstruction of the treatment area by use of sinogram in helical tomotherapy.

Radiat Oncol 2014 Nov 28;9:252. Epub 2014 Nov 28.

Department of Radiology, University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.

Background: TomoTherapy (Accuray, USA) has an image-guided radiotherapy system with a megavoltage (MV) X-ray source and an on-board imaging device. This system allows one to acquire the delivery sinogram during the actual treatment, which partly includes information from the irradiated object. In this study, we try to develop image reconstruction during treatment with helical tomotherapy.

Findings: Sinogram data were acquired during helical tomotherapy delivery using an arc-shaped detector array that consists of 576 xenon-gas filled detector cells. In preprocessing, these were normalized with full air-scan data. A software program was developed that reconstructs 3D images during treatment with corrections as; (1) the regions outside the field were masked not to be added in the backprojection (a masking correction), and (2) each voxel of the reconstructed image was divided by the number of the beamlets passing through its voxel (a ray-passing correction). The masking correction produced a reconstructed image, however, it contained streak artifacts. The ray-passing correction reduced this artifact. Although the SNR (the ratio of mean to standard deviation in a homogeneous region) and the contrast of the reconstructed image were slightly improved with the ray-passing correction, use of only the masking correction was sufficient for the visualization purpose.

Conclusions: The visualization of the treatment area was feasible by using the sinogram in helical tomotherapy. This proposed method would be useful in the treatment verification.
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http://dx.doi.org/10.1186/s13014-014-0252-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255647PMC
November 2014

Selective image similarity measure for bronchoscope tracking based on image registration.

Med Image Anal 2009 Aug 9;13(4):621-33. Epub 2009 Jun 9.

Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi 464-8603, Japan.

We propose a selective method of measurement for computing image similarities based on characteristic structure extraction and demonstrate its application to flexible endoscope navigation, in particular to a bronchoscope navigation system. Camera motion tracking is a fundamental function required for image-guided treatment or therapy systems. In recent years, an ultra-tiny electromagnetic sensor commercially became available, and many image-guided treatment or therapy systems use this sensor for tracking the camera position and orientation. However, due to space limitations, it is difficult to equip the tip of a bronchoscope with such a position sensor, especially in the case of ultra-thin bronchoscopes. Therefore, continuous image registration between real and virtual bronchoscopic images becomes an efficient tool for tracking the bronchoscope. Usually, image registration is done by calculating the image similarity between real and virtual bronchoscopic images. Since global schemes to measure image similarity, such as mutual information, squared gray-level difference, or cross correlation, average differences in intensity values over an entire region, they fail at tracking of scenes where less characteristic structures can be observed. The proposed method divides an entire image into a set of small subblocks and only selects those in which characteristic shapes are observed. Then image similarity is calculated within the selected subblocks. Selection is done by calculating feature values within each subblock. We applied our proposed method to eight pairs of chest X-ray CT images and bronchoscopic video images. The experimental results revealed that bronchoscope tracking using the proposed method could track up to 1600 consecutive bronchoscopic images (about 50s) without external position sensors. Tracking performance was greatly improved in comparison with a standard method utilizing squared gray-level differences of the entire images.
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http://dx.doi.org/10.1016/j.media.2009.06.001DOI Listing
August 2009

Image-guided surgical navigation in implant-based auricular reconstruction.

J Oral Maxillofac Surg 2008 Jun;66(6):1302-6

AssDivision of Plastic and Reconstructive Surgery, Stanford University Medical Center, Stanford, CA 94305, USA.

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http://dx.doi.org/10.1016/j.joms.2007.06.636DOI Listing
June 2008

Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation.

Cell 2007 Mar;128(6):1187-203

Department of Biological Sciences, Stanford University, Stanford, CA 94305, USA.

In Drosophila, approximately 50 classes of olfactory receptor neurons (ORNs) send axons to 50 corresponding glomeruli in the antennal lobe. Uniglomerular projection neurons (PNs) relay olfactory information to the mushroom body (MB) and lateral horn (LH). Here, we combine single-cell labeling and image registration to create high-resolution, quantitative maps of the MB and LH for 35 input PN channels and several groups of LH neurons. We find (1) PN inputs to the MB are stereotyped as previously shown for the LH; (2) PN partners of ORNs from different sensillar groups are clustered in the LH; (3) fruit odors are represented mostly in the posterior-dorsal LH, whereas candidate pheromone-responsive PNs project to the anterior-ventral LH; (4) dendrites of single LH neurons each overlap with specific subsets of PN axons. Our results suggest that the LH is organized according to biological values of olfactory input.
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http://dx.doi.org/10.1016/j.cell.2007.01.040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1885945PMC
March 2007

Bronchoscope tracking based on image registration using multiple initial starting points estimated by motion prediction.

Med Image Comput Comput Assist Interv 2006 ;9(Pt 2):645-52

Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8603, Japan.

This paper presents a method for tracking a bronchoscope based on motion prediction and image registration from multiple initial starting points as a function of a bronchoscope navigation system. We try to improve performance of bronchoscope tracking based on image registration using multiple initial guesses estimated using motion prediction. This method basically tracks a bronchoscopic camera by image registration between real bronchoscopic images and virtual ones derived from CT images taken prior to the bronchoscopic examinations. As an initial guess for image registration, we use multiple starting points to avoid falling into local minima. These initial guesses are computed using the motion prediction results obtained from the Kalman filter's output. We applied the proposed method to nine pairs of X-ray CT images and real bronchoscopic video images. The experimental results showed significant performance in continuous tracking without using any positional sensors.
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http://dx.doi.org/10.1007/11866763_79DOI Listing
April 2007

A study of the accuracy of cyberknife spinal radiosurgery using skeletal structure tracking.

Neurosurgery 2007 Feb;60(2 Suppl 1):ONS147-56; discussion ONS156

Department of Radiation Oncology, Stanford University Medical Center, Stanford, California 94305-5304, USA.

Objective: New technology has enabled the increasing use of radiosurgery to ablate spinal lesions. The first generation of the CyberKnife (Accuray, Inc., Sunnyvale, CA) image-guided radiosurgery system required implanted radiopaque markers (fiducials) to localize spinal targets. A recently developed and now commercially available spine tracking technology called Xsight (Accuray, Inc.) tracks skeletal structures and eliminates the need for implanted fiducials. The Xsight system localizes spinal targets by direct reference to the adjacent vertebral elements. This study sought to measure the accuracy of Xsight spine tracking and provide a qualitative assessment of overall system performance.

Methods: Total system error, which is defined as the distance between the centroids of the planned and delivered dose distributions and represents all possible treatment planning and delivery errors, was measured using a realistic, anthropomorphic head-and-neck phantom. The Xsight tracking system error component of total system error was also computed by retrospectively analyzing image data obtained from eleven patients with a total of 44 implanted fiducials who underwent CyberKnife spinal radiosurgery.

Results: The total system error of the Xsight targeting technology was measured to be 0.61 mm. The tracking system error component was found to be 0.49 mm.

Conclusion: The Xsight spine tracking system is practically important because it is accurate and eliminates the use of implanted fiducials. Experience has shown this technology to be robust under a wide range of clinical circumstances.
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http://dx.doi.org/10.1227/01.NEU.0000249248.55923.ECDOI Listing
February 2007

Shape-based averaging.

IEEE Trans Image Process 2007 Jan;16(1):153-61

SRI International, Menlo Park, CA 94025-3493, USA.

A new method for averaging multidimensional images is presented, which is based on signed Euclidean distance maps computed for each of the pixel values. We refer to the algorithm as "shape-based averaging" (SBA) because of its similarity to Raya and Udupa's shape-based interpolation method. The new method does not introduce pixel intensities that were not present in the input data, which makes it suitable for averaging nonnumerical data such as label maps (segmentations). Using segmented human brain magnetic resonance images, SBA is compared to label voting for the purpose of averaging image segmentations in a multiclassifier fashion. SBA, on average, performed as well as label voting in terms of recognition rates of the averaged segmentations. SBA produced more regular and contiguous structures with less fragmentation than did label voting. SBA also was more robust for small numbers of atlases and for low atlas resolutions, in particular, when combined with shape-based interpolation. We conclude that SBA improves the contiguity and accuracy of averaged image segmentations.
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http://dx.doi.org/10.1109/tip.2006.884936DOI Listing
January 2007

A method for bronchoscope tracking by combining a position sensor and image registration.

Comput Aided Surg 2006 May;11(3):109-17

Graduate School of Information Science, Nagoya University, Nagoya, and Minami-sanjo Hospital, Sapporo, Japan.

This paper describes a method for tracking a bronchoscope by combining a position sensor and image registration. A bronchoscopy guidance system is a tool for providing real-time navigation information acquired from pre-operative CT images to a physician during a bronchoscopic examination. In this system, one of the fundamental functions is tracking a bronchoscope's camera motion. Recently, a very small electromagnetic position sensor has become available. It is possible to insert this sensor into a bronchoscope's working channel to obtain the bronchoscope's camera motion. However, the accuracy of its output is inadequate for bronchoscope tracking. The proposed combination of the sensor and image registration between real and virtual bronchoscopic images derived from CT images is quite useful for improving tracking accuracy. Furthermore, this combination has enabled us to achieve a real-time bronchoscope guidance system. We performed evaluation experiments for the proposed method using a rubber phantom model. The experimental results showed that the proposed system allowed the bronchoscope's camera motion to be tracked at 2.5 frames per second.
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http://dx.doi.org/10.3109/10929080600751159DOI Listing
May 2006

Virtual 3D planning and guidance of mandibular distraction osteogenesis.

Comput Aided Surg 2006 Mar;11(2):51-62

Division of Plastic Surgery, Stanford University, California 94305, USA.

We present a system for 3D planning and pre-operative rehearsal of mandibular distraction osteogenesis procedures. Two primary architectural components are described: a planning system that allows geometric bone manipulation to rapidly explore various modifications and configurations, and a visuohaptic simulator that allows both general-purpose training and preoperative, patient-specific procedure rehearsal. We provide relevant clinical background, then describe the underlying simulation algorithms and their application to craniofacial procedures.
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http://dx.doi.org/10.3109/10929080600629157DOI Listing
March 2006

Hybrid bronchoscope tracking using a magnetic tracking sensor and image registration.

Med Image Comput Comput Assist Interv 2005 ;8(Pt 2):543-50

Graduate School of Information Science, Nagoya University, Nagoya, Japan.

In this paper, we propose a hybrid method for tracking a bronchoscope that uses a combination of magnetic sensor tracking and image registration. The position of a magnetic sensor placed in the working channel of the bronchoscope is provided by a magnetic tracking system. Because of respiratory motion, the magnetic sensor provides only the approximate position and orientation of the bronchoscope in the coordinate system of a CT image acquired before the examination. The sensor position and orientation is used as the starting point for an intensity-based registration between real bronchoscopic video images and virtual bronchoscopic images generated from the CT image. The output transformation of the image registration process is the position and orientation of the bronchoscope in the CT image. We tested the proposed method using a bronchial phantom model. Virtual breathing motion was generated to simulate respiratory motion. The proposed hybrid method successfully tracked the bronchoscope at a rate of approximately 1 Hz.
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http://dx.doi.org/10.1007/11566489_67DOI Listing
June 2006

Markerless real-time 3-D target region tracking by motion backprojection from projection images.

IEEE Trans Med Imaging 2005 Nov;24(11):1455-68

Neuroscience Program at SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025-3493, USA.

Accurate and fast localization of a predefined target region inside the patient is an important component of many image-guided therapy procedures. This problem is commonly solved by registration of intraoperative 2-D projection images to 3-D preoperative images. If the patient is not fixed during the intervention, the 2-D image acquisition is repeated several times during the procedure, and the registration problem can be cast instead as a 3-D tracking problem. To solve the 3-D problem, we propose in this paper to apply 2-D region tracking to first recover the components of the transformation that are in-plane to the projections. The 2-D motion estimates of all projections are backprojected into 3-D space, where they are then combined into a consistent estimate of the 3-D motion. We compare this method to intensity-based 2-D to 3-D registration and a combination of 2-D motion backprojection followed by a 2-D to 3-D registration stage. Using clinical data with a fiducial marker-based gold-standard transformation, we show that our method is capable of accurately tracking vertebral targets in 3-D from 2-D motion measured in X-ray projection images. Using a standard tracking algorithm (hyperplane tracking), tracking is achieved at video frame rates but fails relatively often (32% of all frames tracked with target registration error (TRE) better than 1.2 mm, 82% of all frames tracked with TRE better than 2.4 mm). With intensity-based 2-D to 2-D image registration using normalized mutual information (NMI) and pattern intensity (PI), accuracy and robustness are substantially improved. NMI tracked 82% of all frames in our data with TRE better than 1.2 mm and 96% of all frames with TRE better than 2.4 mm. This comes at the cost of a reduced frame rate, 1.7 s average processing time per frame and projection device. Results using PI were slightly more accurate, but required on average 5.4 s time per frame. These results are still substantially faster than 2-D to 3-D registration. We conclude that motion backprojection from 2-D motion tracking is an accurate and efficient method for tracking 3-D target motion, but tracking 2-D motion accurately and robustly remains a challenge.
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http://dx.doi.org/10.1109/TMI.2005.857651DOI Listing
November 2005

Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration.

IEEE Trans Med Imaging 2005 Nov;24(11):1441-54

Department of Computer Science, Stanford University, Stanford, CA 94305 USA.

Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.
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http://dx.doi.org/10.1109/TMI.2005.856749DOI Listing
November 2005

Progressive attenuation fields: fast 2D-3D image registration without precomputation.

Med Phys 2005 Sep;32(9):2870-80

Neuroscience Program, SRI International, Menlo Park, California 94025-3493, USA.

Computation of digitally reconstructed radiograph (DRR) images is the rate-limiting step in most current intensity-based algorithms for the registration of three-dimensional (3D) images to two-dimensional (2D) projection images. This paper introduces and evaluates the progressive attenuation field (PAF), which is a new method to speed up DRR computation. A PAF is closely related to an attenuation field (AF). A major difference is that a PAF is constructed on the fly as the registration proceeds; it does not require any precomputation time, nor does it make any prior assumptions of the patient pose or limit the permissible range of patient motion. A PAF effectively acts as a cache memory for projection values once they are computed, rather than as a lookup table for precomputed projections like standard AFs. We use a cylindrical attenuation field parametrization, which is better suited for many medical applications of 2D-3D registration than the usual two-plane parametrization. The computed attenuation values are stored in a hash table for time-efficient storage and access. Using clinical gold-standard spine image data sets from five patients, we demonstrate consistent speedups of intensity-based 2D-3D image registration using PAF DRRs by a factor of 10 over conventional ray casting DRRs with no decrease of registration accuracy or robustness.
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http://dx.doi.org/10.1118/1.1997367DOI Listing
September 2005

Design and application of an assessment protocol for electromagnetic tracking systems.

Med Phys 2005 Jul;32(7):2371-9

Center of Biomedical Engineering and Physics, Medical University of Vienna, Vienna, Austria.

This paper defines a simple protocol for competitive and quantified evaluation of electromagnetic tracking systems such as the NDI Aurora (A) and Ascension microBIRD with dipole transmitter (B). It establishes new methods and a new phantom design which assesses the reproducibility and allows comparability with different tracking systems in a consistent environment. A machined base plate was designed and manufactured in which a 50 mm grid of holes was precisely drilled for position measurements. In the center a circle of 32 equispaced holes enables the accurate measurement of rotation. The sensors can be clamped in a small mount which fits into pairs of grid holes on the base plate. Relative positional/orientational errors are found by subtracting the known distances/ rotations between the machined locations from the differences of the mean observed positions/ rotation. To measure the influence of metallic objects we inserted rods made of steel (SST 303, SST 416), aluminum, and bronze into the sensitive volume between sensor and emitter. We calculated the fiducial registration error and fiducial location error with a standard stylus calibration for both tracking systems and assessed two different methods of stylus calibration. The positional jitter amounted to 0.14 mm(A) and 0.08 mm(B). A relative positional error of 0.96 mm +/- 0.68 mm, range -0.06 mm; 2.23 mm(A) and 1.14 mm +/- 0.78 mm, range -3.72 mm; 1.57 mm(B) for a given distance of 50 mm was found. The relative rotation error was found to be 0.51 degrees (A)/0.04 degrees (B). The most relevant distortion caused by metallic objects results from SST 416. The maximum error 4.2 mm(A)/ > or = 100 mm(B) occurs when the rod is close to the sensor(20 mm). While (B) is more sensitive with respect to metallic objects, (A) is less accurate concerning orientation measurements. (B) showed a systematic error when distances are calculated.
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http://dx.doi.org/10.1118/1.1944327DOI Listing
July 2005

Design and application of an assessment protocol for electromagnetic tracking systems.

Med Phys 2005 Jul;32(7Part1):2371-2379

Image Guidance Laboratories, Stanford University School of Medicine, Stanford, California.

This paper defines a simple protocol for competitive and quantified evaluation of electromagnetic tracking systems such as the NDI Aurora (A) and Ascension microBIRD with dipole transmitter (B). It establishes new methods and a new phantom design which assesses the reproducibility and allows comparability with different tracking systems in a consistent environment. A machined base plate was designed and manufactured in which a 50 mm grid of holes was precisely drilled for position measurements. In the center a circle of 32 equispaced holes enables the accurate measurement of rotation. The sensors can be clamped in a small mount which fits into pairs of grid holes on the base plate. Relative positional/orientational errors are found by subtracting the known distances/rotations between the machined locations from the differences of the mean observed positions/rotation. To measure the influence of metallic objects we inserted rods made of steel (SST 303, SST 416), aluminum, and bronze into the sensitive volume between sensor and emitter. We calculated the fiducial registration error and fiducial location error with a standard stylus calibration for both tracking systems and assessed two different methods of stylus calibration. The positional jitter amounted to 0.14 mm(A) and 0.08 mm(B). A relative positional error of 0.96mm±0.68mm, range -0.06 mm; 2.23 mm(A) and 1.14mm±0.78mm, range -3.72 mm; 1.57 mm(B) for a given distance of 50 mm was found. The relative rotation error was found to be 0.51° (A)/0.04° (B). The most relevant distortion caused by metallic objects results from SST 416. The maximum error 4.2mm(A)∕⩾100mm(B) occurs when the rod is close to the sensor(20 mm). While (B) is more sensitive with respect to metallic objects, (A) is less accurate concerning orientation measurements. (B) showed a systematic error when distances are calculated.
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http://dx.doi.org/10.1118/1.1944327DOI Listing
July 2005

Unwarping confocal microscopy images of bee brains by nonrigid registration to a magnetic resonance microscopy image.

J Biomed Opt 2005 Mar-Apr;10(2):024018

SRI International, Neuroscience Program, 333 Ravenswood Avenue, Menlo Park, California 94025, USA.

Confocal microscopy (CM) is a powerful image acquisition technique that is well established in many biological applications. It provides 3-D acquisition with high spatial resolution and can acquire several different channels of complementary image information. Due to the specimen extraction and preparation process, however, the shapes of imaged objects may differ considerably from their in vivo appearance. Magnetic resonance microscopy (MRM) is an evolving variant of magnetic resonance imaging, which achieves microscopic resolutions using a high magnetic field and strong magnetic gradients. Compared to CM imaging, MRM allows for in situ imaging and is virtually free of geometrical distortions. We propose to combine the advantages of both methods by unwarping CM images using a MRM reference image. Our method incorporates a sequence of image processing operators applied to the MRM image, followed by a two-stage intensity-based registration to compute a nonrigid coordinate transformation between the CM images and the MRM image. We present results obtained using CM images from the brains of 20 honey bees and a MRM image of an in situ bee brain.
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http://dx.doi.org/10.1117/1.1896025DOI Listing
September 2005

Intensity-based 2D-3D spine image registration incorporating a single fiducial marker.

Acad Radiol 2005 Jan;12(1):37-50

Department of Computer Science, Stanford University, 300 Pasteur Drive, Stanford, CA 94305-5327, USA.

Rationale And Objectives: The two-dimensional (2D)-three dimensional (3D) registration of a computed tomography image to one or more x-ray projection images has a number of image-guided therapy applications. In general, fiducial marker-based methods are fast, accurate, and robust, but marker implantation is not always possible, often is considered too invasive to be clinically acceptable, and entails risk. There also is the unresolved issue of whether it is acceptable to leave markers permanently implanted. Intensity-based registration methods do not require the use of markers and can be automated because such geometric features as points and surfaces do not need to be segmented from the images. However, for spine images, intensity-based methods are susceptible to local optima in the cost function and thus need initial transformations that are close to the correct transformation.

Materials And Methods: In this report, we propose a hybrid similarity measure for 2D-3D registration that is a weighted combination of an intensity-based similarity measure (mutual information) and a point-based measure using one fiducial marker. We evaluate its registration accuracy and robustness by using gold-standard clinical spine image data from four patients.

Results: Mean registration errors for successful registrations for the four patients were 1.3 and 1.1 mm for the intensity-based and hybrid similarity measures, respectively. Whereas the percentage of successful intensity-based registrations (registration error < 2.5 mm) decreased rapidly as the initial transformation got further from the correct transformation, the incorporation of a single marker produced successful registrations more than 99% of the time independent of the initial transformation.

Conclusion: The use of one fiducial marker reduces 2D-3D spine image registration error slightly and improves robustness substantially. The findings are potentially relevant for image-guided therapy. If one marker is sufficient to obtain clinically acceptable registration accuracy and robustness, as the preliminary results using the proposed hybrid similarity measure suggest, the marker can be placed on a spinous process, which could be accomplished without penetrating muscle or using fluoroscopic guidance, and such a marker could be removed relatively easily.
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http://dx.doi.org/10.1016/j.acra.2004.09.013DOI Listing
January 2005

The impact of fiducial distribution on headset-based registration in image-guided sinus surgery.

Otolaryngol Head Neck Surg 2004 Nov;131(5):666-72

Head and Neck Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA.

Objective: The objective of this study was to assess registration error due to fiducial configuration for the ENT headsets for the CBYON Suite (CBYON, Mountain View, CA) and InstaTrak (GEMS Navigation and Visualization, Waukesha, WI).

Study Design: Axial CT scans (1-mm slice thickness) were obtained of for 24 cadaveric heads using the CBYON headset and for 23 cadaveric heads using the GEMS headset. The CBYON and GEMS NAV software were used to calculate the fiducial registration error (FRE). Fiducial localization error (FLE) was estimated from FRE. Theoretical target registration error (TRE) was calculated at 11 targets.

Results: The FRE for CBYON and GEMS NAV was 0.69 mm and 0.27 mm, respectively. The theoretical TRE for CBYON and GEMS NAV was 0.41 mm and 0.30 mm, respectively. The theoretical TRE was greater at targets posterior in the sinus cavities.

Conclusion: Theoretical TRE values for both ENT headsets are less than clinically observed TRE. Clinically observed TRE is likely due to repositioning accuracy.

Ebm Rating: B-2.
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http://dx.doi.org/10.1016/j.otohns.2004.03.045DOI Listing
November 2004

Expectation maximization strategies for multi-atlas multi-label segmentation.

Inf Process Med Imaging 2003 Jul;18:210-21

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA, USA.

It is well-known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher that the accuracy of the individual classifiers. In order to combine multiple segmentations we introduce two extensions to an expectation maximization (EM) algorithm for ground truth estimation based on multiple experts (Warfield et al., MICCAI 2002). The first method repeatedly applies the Warfield algorithm with a subsequent integration step. The second method is a multi-label extension of the Warfield algorithm. Both extensions integrate multiple segmentations into one that is closer to the unknown ground truth than the individual segmentations. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of classifier combination methods in atlas-based segmentation. By applying random deformations, a given ground truth atlas is transformed into multiple segmentations that could result from imperfect registrations of an image to multiple atlas images. We demonstrate that a segmentation produced by combining multiple individual registration-based segmentations is more accurate for the two EM methods we propose than for simple label averaging.
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http://dx.doi.org/10.1007/978-3-540-45087-0_18DOI Listing
July 2003

Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation.

IEEE Trans Med Imaging 2004 Aug;23(8):983-94

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA 94305-5327, USA.

It is well known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. We have previously shown this to be true for atlas-based segmentation of biomedical images. The conventional method for combining individual classifiers weights each classifier equally (vote or sum rule fusion). In this paper, we propose two methods that estimate the performances of the individual classifiers and combine the individual classifiers by weighting them according to their estimated performance. The two methods are multiclass extensions of an expectation-maximization (EM) algorithm for ground truth estimation of binary classification based on decisions of multiple experts (Warfield et al., 2004). The first method performs parameter estimation independently for each class with a subsequent integration step. The second method considers all classes simultaneously. We demonstrate the efficacy of these performance-based fusion methods by applying them to atlas-based segmentations of three-dimensional confocal microscopy images of bee brains. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of classifier combination methods in atlas-based segmentation. By applying random deformations, a given ground truth atlas is transformed into multiple segmentations that could result from imperfect registrations of an image to multiple atlas images. In a second evaluation study, multiple actual atlas-based segmentations are combined and their accuracies computed by comparing them to a manual segmentation. We demonstrate in both evaluation studies that segmentations produced by combining multiple individual registration-based segmentations are more accurate for the two classifier fusion methods we propose, which weight the individual classifiers according to their EM-based performance estimates, than for simple sum rule fusion, which weights each classifier equally.
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http://dx.doi.org/10.1109/TMI.2004.830803DOI Listing
August 2004

Method for measurement of the blood-brain barrier permeability in the perfused mouse brain: application to amyloid-beta peptide in wild type and Alzheimer's Tg2576 mice.

J Neurosci Methods 2004 Sep;138(1-2):233-42

Department of Neurosurgery, Frank P. Smith Neurosurgical Research Laboratory, Center of Aging & Developmental Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 670, Rochester, NY 14642, USA.

The role of transport exchanges of neuroactive solutes across the blood-brain barrier (BBB) is increasingly recognized. To take full advantage of genetically altered mouse models of neurodegenerative disorders for BBB transport studies, we adapted a brain perfusion technique to the mouse. During a carotid brain perfusion with a medium containing sheep red blood cells and mock plasma, the physiological parameters in the arterial inflow, regional cerebral blood flow (14C-iodoantipyrine autoradiography), ultrastructural integrity of the tissue, barrier to lanthanum, brain water content, energy metabolites and lactate levels remain unchanged. Amyloid-beta peptides (Abeta) were iodinated by lactoperoxidase method. Non-oxidized mono-iodinated Abeta monomers were separated by HPLC (as confirmed by MALDI-TOF spectrometry) and used in transport measurements. Transport of intact 125I-Abeta40 across the BBB was time- and concentration-dependent in contrast to negligible 14C-inulin uptake. In 5-6 months old Alzheimer's Tg2576 mice, Abeta40 BBB transport was increased by >eight-fold compared to age-matched littermate controls, and was mediated via the receptor for advanced glycation endproducts. We conclude the present arterial brain perfusion method provides strictly controlled environment in cerebral microcirculation suitable for examining transport of rapidly and slowly penetrating molecules across the BBB in normal and transgenic mice.
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http://dx.doi.org/10.1016/j.jneumeth.2004.04.026DOI Listing
September 2004

Quantitative computer-aided computed tomography analysis of sphenoid sinus anatomical relationships.

Am J Rhinol 2004 May-Jun;18(3):173-8

Head & Neck Institute, Section of Nasal and Sinus Disorders, Cleveland Clinic Foundation, Cleveland, Ohio, USA.

Background: This study describes a novel computer-generated anatomic symmetry plane as a framework for the quantitative description of sphenoid sinus anatomy. The aim of this study was to (1) determine relationships and distances between a midline sphenoid reference point (called the central sphenoid point [CSP]) and lateral sphenoid wall structures and (2) assess the incidence of anterior clinoid process (ACP) pneumatization and pterygoid recess (PR) pneumatization.

Methods: Axial computed tomography (CT) scans (1-mm slice thickness) were obtained on a VolumeZoom CT scanner (Siemens Medical, Erlangen, Germany). Mathematically derived anatomic symmetry planes were created using custom postprocessing software. A standardized review of each CT scan using surgical planning software (CBYON Suite version 2.6; CBYON, Mountain View, CA) was performed. The CSP was defined as a reference point in the midline sagittal plane at the intersection of the vertical sellar face and the horizontal sellar floor.

Results: A total of 128 sides in 64 cadaveric specimens were available for review. The incidences of ACP pneumatization and PR pneumatization were 23.4 and 37.5%. The mean distances from the CSP to the left optic canal midpoint, the left ACP entrance point, and the left PR lateral wall were 17.2, 15.6, and 27.6 mm, respectively. The corresponding distances from the CSP on the right side were 17.3, 15.8, and 28.0 mm, respectively. Measurements from the maxillary spine to the optic canal midpoint, ACP entrance point, and PR lateral wall on each side were performed also.

Conclusion: This approach provides both quantitative and qualitative understanding of sphenoid osteology and may be coupled with intraoperative surgical navigation to reduce the risks of sphenoid surgery. Both PR and ACP pneumatization are surprisingly common. Because the CSP-derived relationships may be referenced during endoscopic surgical navigation, they may provide greater clinical utility than traditional alternatives. This paradigm may facilitate a greater understanding of sphenoid anatomy and enhance surgical safety and precision.
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September 2004

Designing optically tracked instruments for image-guided surgery.

IEEE Trans Med Imaging 2004 May;23(5):533-45

Accuray, Inc., 1310 Chesapeake Terrace, Sunnyvale, CA 94089, USA.

Most image-guided surgery (IGS) systems track the positions of surgical instruments in the physical space occupied by the patient. This task is commonly performed using an optical tracking system that determines the positions of fiducial markers such as infrared-emitting diodes or retroreflective spheres that are attached to the instrument. Instrument tracking error is an important component of the overall IGS system error. This paper is concerned with the effect of fiducial marker configuration (number and spatial distribution) on tip position tracking error. Statistically expected tip position tracking error is calculated by applying results from the point-based registration error theory developed by Fitzpatrick et al. Tracking error depends not only on the error in localizing the fiducials, which is the error value generally provided by manufacturers of optical tracking systems, but also on the number and spatial distribution of the tracking fiducials and the position of the instrument tip relative to the fiducials. The theory is extended in two ways. First, a formula is derived for the special case in which the fiducials and the tip are collinear. Second, the theory is extended for the case in which there is a composition of transformations, as is the situation for tracking an instrument relative to a coordinate reference frame (i.e., a set of fiducials attached to the patient). The derivation reveals that the previous theory may be applied independently to the two transformations; the resulting independent components of tracking error add in quadrature to give the overall tracking error. The theoretical results are verified with numerical simulations and experimental measurements. The results in this paper may be useful for the design of optically tracked instruments for image-guided surgery; this is illustrated with several examples.
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http://dx.doi.org/10.1109/tmi.2004.825614DOI Listing
May 2004

Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images.

Med Phys 2004 Mar;31(3):427-32

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, California 94305-5327, USA.

We present a technique for modeling liver motion during the respiratory cycle using intensity-based nonrigid registration of gated magnetic resonance (MR) images. Three-dimensional MR images of the abdomens of four volunteers were acquired at end-inspiration, end-expiration, and eight time points in between using respiratory gating. The deformation fields between the images were computed using intensity-based rigid and nonrigid registration algorithms. Global motion is modeled by a rigid transformation while local motion is modeled by a free-form deformation based on B-splines. Much of the liver motion was cranial-caudal translation, which was captured by the rigid transformation. However, there was still substantial residual deformation (approximately 10 mm averaged over the entire liver in four volunteers, and 34 mm at one place in the liver of one volunteer). The computed organ motion model can potentially be used to determine an appropriate respiratory-gated radiotherapy window during which the position of the target is known within a specified excursion.
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http://dx.doi.org/10.1118/1.1644513DOI Listing
March 2004

Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains.

Neuroimage 2004 Apr;21(4):1428-42

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA 94305-5327, USA.

This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based nonrigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average-shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by the similarity index (SI) between the automatic segmentation result and a manually generated gold standard. The best segmentation accuracy was achieved using the MUL paradigm, which resulted in a mean similarity index value between manual and automatic segmentation of 0.86 (AVG, 0.84; SIM, 0.82; IND, 0.81). The superiority of the MUL strategy over the other three methods is statistically significant (two-sided paired t test, P < 0.001). Both the MUL and AVG strategies performed better than the best possible SIM and IND strategies with optimal a posteriori atlas selection (mean similarity index for optimal SIM, 0.83; for optimal IND, 0.81). Our findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.
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http://dx.doi.org/10.1016/j.neuroimage.2003.11.010DOI Listing
April 2004

Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint.

IEEE Trans Med Imaging 2003 Jun;22(6):730-41

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA 94305-5327, USA.

In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and post-contrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%-78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts better than or equal to the smoothness constraint in 13 out of 17 cases (better in 9, equal in 4, worse in 4). The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
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http://dx.doi.org/10.1109/TMI.2003.814791DOI Listing
June 2003

Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees.

IEEE Trans Inf Technol Biomed 2003 Mar;7(1):16-25

Image Guidance Laboratories, Department of Neurosurgery, Stanford University, Stanford, CA 94305-5327, USA.

One major problem with nonrigid image registration techniques is their high computational cost. Because of this, these methods have found limited application to clinical situations where fast execution is required, e.g., intraoperative imaging. This paper presents a parallel implementation of a nonrigid image registration algorithm. It takes advantage of shared-memory multiprocessor computer architectures using multithreaded programming by partitioning of data and partitioning of tasks, depending on the computational subproblem. For three different biomedical applications (intraoperative brain deformation, contrast-enhanced MR mammography, intersubject brain registration), the scaling behavior of the algorithm is quantitatively analyzed. The method is demonstrated to perform the computation of intra-operative brain deformation in less than a minute using 64 CPUs on a 128-CPU shared-memory supercomputer (SGI Origin 3800). It is shown that its serial component is no more than 2% of the total computation time, allowing a speedup of at least a factor of 50. In most cases, the theoretical limit of the speedup is substantially higher (up to 132-fold in the application examples presented in this paper). The parallel implementation of our algorithm is, therefore, capable of solving nonrigid registration problems with short execution time requirements and may be considered an important step in the application of such techniques to clinically important problems such as the computation of brain deformation during cranial image-guided surgery.
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http://dx.doi.org/10.1109/titb.2003.808506DOI Listing
March 2003
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