Fluoroscopy is a common image guidance modality used in spine and orthopedic surgery. One benefit of this technology is that it provides real-time images without interrupting the procedure. A major challenge with fluoroscopy is that it provides projection images with no depth information, limiting surgical accuracy in complex procedures like for example in thoracic spine surgery1 . 3D technologies such as intraoperative Cone Beam CT and surgical navigation solve the surgical accuracy problem but increase cost and impair the surgical workflow, limiting its adoption2 . In an attempt to improve surgical accuracy, control costs and simplify the surgical workflow, a new approach to image guidance based on real-time 3D imaging is proposed3 . Fast fluoroscopic acquisitions taken in a circular tomosynthesis geometry are used to provide near real-time 3D updates of the imaged surgical scene. 3D updates are achieved via a model-based reconstruction that makes proficient use of prior information, and instrument tracking is achieved via image processing. This new imaging approach is named Cone Beam Tomosynthesis (CBT) fluoroscopy. A first prototype based on a modified C-arm and with a single rotating source is used to assess the surgical performance of CBT-fluoroscopy. Preliminary results show that CBT-fluoroscopy can achieve near-real-time imaging performance and provide comparable surgical accuracy to fluoroscopy in the use case of pedicle screw placement on phantoms; the limitations of the approach are analyzed and steps to address these limitations are discussed.
Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis
KEYWORDS: Image processing algorithms and systems, Breast, Tissues, Image segmentation, Digital filtering, Image filtering, Computed tomography, Spherical lenses, 3D image processing, Digital breast tomosynthesis
Recently, tomosynthesis (DBT) and CT (BCT) have been developed for breast imaging. Since each modality
produces a fundamentally different representation of the breast volume, our goal was to investigate whether
a 3D segmentation algorithm for breast masses could be applied to both DBT and breast BCT images. A
secondary goal of this study was to investigate a simplified method for comparing manual outlines to a computer
The seeded mass lesion segmentation algorithm is based on maximizing the radial gradient index (RGI) along
a constrained region contour. In DBT, the constraint function was a prolate spherical Gaussian, with a larger
FWHM along the depth direction where the resolution is low, while it was a spherical Gaussian for BCT. For
DBT, manual lesion outlines were obtained in the in-focus plane of the lesion, which was used to compute the
overlap ratio with the computer segmentation. For BCT, lesions were manually outlined in three orthogonal
planes, and the average overlap ratio from the three planes was computed.
In DBT, 81% of all lesions were segmented at an overlap ratio of 0.4 or higher, based on manual outlines in
one slice through the lesion center. In BCT, 93% of all segmentations achieved an average overlap ratio of 0.4,
based on the manual outlines in three orthogonal planes.
Our results indicate mass lesions in both BCT and DBT images can be segmented with the proposed 3D
segmentation algorithm, by selecting an appropriate set of parameters and after images have undergone specific
The limitations of current CCD-based microCT X-ray imaging systems arise from two important factors. First, readout
speeds are curtailed in order to minimize system read noise, which increases significantly with increasing readout rates.
Second, the afterglow associated with commercial scintillator films can introduce image lag, leading to substantial
artifacts in reconstructed images, especially when the detector is operated at several hundred frames/second (fps). For
high speed imaging systems, high-speed readout electronics and fast scintillator films are required. This paper presents
an approach to developing a high-speed CT detector based on a novel, back-thinned electron-multiplying CCD
(EMCCD) coupled to various bright, high resolution, low afterglow films. The EMCCD camera, when operated in its
binned mode, is capable of acquiring data at up to 300 fps with reduced imaging area. CsI:Tl,Eu and ZnSe:Te films,
recently fabricated at RMD, apart from being bright, showed very good afterglow properties, favorable for high-speed
imaging. Since ZnSe:Te films were brighter than CsI:Tl,Eu films, for preliminary experiments a ZnSe:Te film was
coupled to an EMCCD camera at UC Davis Medical Center. A high-throughput tungsten anode X-ray generator was
used, as the X-ray fluence from a mini- or micro-focus source would be insufficient to achieve high-speed imaging. A
euthanized mouse held in a glass tube was rotated 360 degrees in less than 3 seconds, while radiographic images were
recorded at various readout rates (up to 300 fps); images were reconstructed using a conventional Feldkamp cone-beam
reconstruction algorithm. We have found that this system allows volumetric CT imaging of small animals in
approximately two seconds at ~110 to 190 μm resolution, compared to several minutes at 160 μm resolution needed for
the best current systems.
Cone-beam systems designed for breast cancer detection bear a unique radiation dose limitation and are vulnerable to the
additive noise from the detector. Additive noise is the signal fluctuation from detector elements and is independent of the
incident exposure level. In this study, two different approaches (single pixel based and region of interest based) to
measure the additive noise were explored using continuously acquired air images at different exposure levels, with both
raw images and flat-field corrected images. The influence from two major factors, inter-pixel variance and image lag,
were studied. The pixel variance measured from dark images was used as the gold standard (for the entire detector
15.12±1.3 ADU2) for comparison. Image noise propagation through reconstruction procedures was also investigated and
a mathematically derived quadratic relationship between the image noise and the inverse of the radiation dose was
confirmed with experiment data. The additive noise level was proved to affect the CT image noise as the second order
coefficient and thus determines the lower limit of the scan radiation dose, above which the scanner operates at quantum
limited region and utilizes the x-ray photon most efficiently.
Current dedicated, cone-beam breast CT scanners generally use a circular
scanning configuration largely because it is relatively easy to implement
mechanically. It is also well-known, however, that a circular scanning
configuration produces insufficient cone-beam data for reconstrucing
accurate 3D breast images. Approximate algorithms, such as FDK has
been widely applied to reconstruct images from circular cone-beam
data. In the FDK reconstruction, it is possible to observe artifacts such as
intensity decay for locations that are not within the plane containing
the circular source trajectory. Such artifacts may potentially lead
to false positive and/or false negative diagnosis of breast cancer.
Non-circular imaging configurations may provide data sufficient for accurate image reconstruction.
In this work, we implement, investigate innovative, non-circular scanning
configurations such as helical and saddle configurations for data
acquisition on a dedicated, cone-beam breast CT scanner, and develop
novel algorithms to reconstruct accurate 3D images from these data.
A dedicated, cone-beam breast CT scanner capable of performing non-circular
scanning configurations was used in this research. We have investigated
different scanning configurations, including helical and saddle configurations.
A Defrise disk phantom and a dead mouse were scanned by use of these
configurations. For each configuration, cone-beam data were acquired
at 501 views over each turn. We have reconstructed images using our
BPF algorithm from data acquired with the helical scanning
Cone beam breast CT (CBBCT) has potential as an alternative to mammography for screening breast cancer while limiting the radiation dose to that of a two-view mammogram. A clinical trial of CBBCT has been underway and volumetric breast images have been obtained. Although these images clearly show the 3D structure of the breast, they are limited by quantum noise due to dose limitations. Noise from these images adds to the challenges of glandular/adipose tissue segmentation. In response to this, an automated method for reducing noise and segmenting glandular tissue in CBBCT images was developed.
A histogram based 2-means clustering algorithm was used in conjunction with a seven-point 3D median filter to reduce quantum noise. Following this, a 2D parabolic correction was applied to flatten the adipose tissue in each slice to reduce system inhomogeneities. Finally, a median smoothing algorithm was applied to further reduce noise for optimal segmentation.
The algorithm was tested on actual breast scan volume data sets for subjective analysis and on a 3D mathematical phantom to test the algorithm.
Subjective comparison of the actual breast scans with the denoised and segmented volumes showed good segmentation with little to no noticeable degradation. The mathematical phantom, after denoising and segmentation, was found to accurately measure the percent glandularity within 0.03% of the actual value for the phantom containing larger spherical shapes, but was only able to preserve small micro-calcification sized spheres of 0.8 and 1.0 mm, and small fibers with diameters of 1.2 and 1.4 mm.