Mouse Atlas Registration with Non-tomographic Imaging Modalities—a Pilot Study Based on Simulation

Wang, Hongkai; Stout, David; Chatziioannou, Arion F.

doi:10.1007/s11307-011-0519-x

Cited by 11 publications

(19 citation statements)

References 46 publications

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“…Figure 15 illustrates the capabilities of the PETbox4/MARS combination system. In this static 20 minute 64 Cu study, a reconstructed three-dimensional volume PET image was registered with the MARS data set to provide an anatomical reference (Wang et al, 2011; Wang et al, 2012). The activity in the entire mouse was 0.14 MBq (3.7 µCi) at scan time.…”

Section: Resultsmentioning

confidence: 99%

“…The PETbox4 is integrated with a Mouse Atlas Registration System (MARS) which provides a novel anatomical reference approach via a combination of x-ray projection, optical photographic images and a digital mouse atlas (Wang et al, 2011; Wang et al, 2012). The MARS consists of a miniature x-ray tube (MAGNUM ® 40 kV x-ray source, Moxtek Inc., UT, USA), an x-ray detector (RedEye™200 Remote Senor, Rad-icon Imaging Corp. CA, USA) and a webcam (Firefly ® MV, Point Grey Research Inc., BC, Canada).…”

Section: Methodsmentioning

confidence: 99%

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NEMA NU-4 performance evaluation of PETbox4, a high sensitivity dedicated PET preclinical tomograph

Taschereau

et al. 2013

Phys. Med. Biol.

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PETbox4 is a new, fully tomographic bench top PET scanner dedicated to high sensitivity and high resolution imaging of mice. This manuscript characterizes the performance of the prototype system using the National Electrical Manufacturers Association (NEMA) NU 4–2008 standards, including studies of sensitivity, spatial resolution, energy resolution, scatter fraction, count-rate performance and image quality. The PETbox4 performance is also compared with the performance of PETbox, a previous generation limited angle tomography system. PETbox4 consists of four opposing flat-panel type detectors arranged in a box like geometry. Each panel is made by a 24 × 50 pixelated array of 1.82 × 1.82 × 7mm bismuth germanate (BGO) scintillation crystals with a crystal pitch of 1.90mm. Each of these scintillation arrays is coupled to two Hamamatsu H8500 photomultiplier tubes via a glass light guide. Volumetric images for a 45 × 45 × 95 mm field of view (FOV) are reconstructed with a maximum likelihood expectation maximization (ML-EM) algorithm incorporating a system model based on a parameterized detector response. With an energy window of 150–650 keV, the peak absolute sensitivity is approximately 18% at the center of FOV. The measured crystal energy resolution ranges from 13.5% to 48.3% full width at half maximum (FWHM), with a mean of 18.0%. The intrinsic detector spatial resolution is 1.5mm FWHM in both transverse and axial directions. The reconstructed image spatial resolution for different locations in the FOV ranges from 1.32mm to 1.93 mm, with an average of 1.46 mm. The peak noise equivalent count rate for the mouse-sized phantom is 35 kcps for a total activity of 1.5 MBq (40 µCi) and the scatter fraction is 28%. The standard deviation in the uniform region of the image quality phantom is 5.7%. The recovery coefficients range from 0.10 to 0.93. In comparison to the first generation two panel PETbox system, PETbox4 achieves substantial improvements on sensitivity and spatial resolution. The overall performance demonstrates that the PETbox4 scanner is suitable for producing high quality images for molecular imaging based biomedical research.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

NEMA NU-4 performance evaluation of PETbox4, a high sensitivity dedicated PET preclinical tomograph

Taschereau

et al. 2013

Phys. Med. Biol.

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“…The limited mouse area visible in the side-view photo reduces the completeness of the acquired data, making direct application of the atlas registration method described in our previous simulation study (Wang et al , 2011) unfeasible. To solve this problem, we constructed a statistical mouse atlas based on multiple training subjects, and fit the atlas to the incomplete data using the techniques of statistical shape model (SSM) and conditional Gaussian model (CGM).…”

Section: Methodsmentioning

confidence: 99%

“…In a previous study (Wang et al , 2011), we simulated 11 different combinations of three non-tomographic imaging devices (i.e. optical camera, 3D surface scanner and planar x-ray projector) by positioning the devices at different view-angles to the subject.…”

Section: Introductionmentioning

confidence: 99%

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera

et al. 2012

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This paper introduces a mouse atlas registration system (MARS), composed of a stationary top-view x-ray projector and a side-view optical camera, coupled to a mouse atlas registration algorithm. This system is using the x-ray and optical images to guide a fully automatic co-registration of a mouse atlas with each subject, in order to provide anatomical reference for small animal molecular imaging systems such as Positron Emission Tomography (PET). To facilitate the registration, a statistical atlas that accounts for inter-subject anatomical variations was constructed based on 83 organ-labeled mouse micro-CT images. The statistical shape model and conditional Gaussian model techniques were used to register the atlas with the x-ray image and optical photo. The accuracy of the atlas registration was evaluated by comparing the registered atlas with the organ-labeled micro-CT images of the test subjects. The results showed excellent registration accuracy of the whole-body region, and good accuracy for the brain, liver, heart, lungs and kidneys. In its implementation, the MARS system was integrated with a preclinical PET scanner to deliver combined PET/MARS imaging, and to facilitate atlas-assisted analysis of the preclinical PET images.

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“…Conventional small animal in vivo tracking methods include direct voxel/pixel to voxel/pixel tracking based on intensity and gradient information or atlas matching, which relies on an a-priori digital model (Joshi et al , 2010; Wang et al , 2012a; Wang et al , 2012b). The atlas matching method relies on iteratively deforming a digital mouse model to match simulated x-ray projections with measured x-ray projections.…”

Section: Introductionmentioning

confidence: 99%

Canny edge-based deformable image registration

et al. 2017

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This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and Distinctive Features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

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Mouse Atlas Registration with Non-tomographic Imaging Modalities—a Pilot Study Based on Simulation

Cited by 11 publications

References 46 publications

NEMA NU-4 performance evaluation of PETbox4, a high sensitivity dedicated PET preclinical tomograph

NEMA NU-4 performance evaluation of PETbox4, a high sensitivity dedicated PET preclinical tomograph

MARS: a mouse atlas registration system based on a planar x-ray projector and an optical camera

Canny edge-based deformable image registration

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