In this paper we present a novel geometric calibration procedure for cone-beam computed tomography (CBCT)
devices with arbitrary geometry using a calibration phantom containing steel beads. In contrast to typical
calibration procedures the position of the beads does not have to be known precisely as it is also recovered
during calibration. In addition, the arrangement of the beads inside the phantom is very flexible and does not
have to follow hard constraints. The bead centers are extracted with subpixel precision from the projection
images while taking the absorption properties of the calibration phantom into account. Based on the recovered
center positions and phantom geometry, the projection geometry is computed for every projection image. This
geometry can be arbitrary and does not have to lie on a specific path, e.g. a circle. This allows to calibrate devices
with reproducible mechanical errors in the gantry movement. We present an evaluation of the point extraction
and the calibration procedure on ground-truth data and show reconstruction results on a device calibrated using
the proposed calibration method.
The volumetric reconstruction of a freehand ultrasound sweep, also called compounding, introduces additional
diagnostic value to the ultrasound acquisition by allowing 3D visualization and fast generation of arbitrary
MPR(Multi-Planar-Reformatting) slices. Furthermore reconstructing a sweep adds to the general availability
of the ultrasound data since volumes are more common to a variety of clinical applications/systems like PACS.
Generally there are two reconstruction approaches, namely forward and backward with their respective advantages
and disadvantages. In this paper we present a hybrid reconstruction method partially implemented
on the GPU that combines the forward and backward approaches to efficiently reconstruct a continuous freehand
ultrasound sweep, while ensuring at the same time a high reconstruction quality. The main goal of this
work was to significantly decrease the waiting time from sweep acquisition to volume reconstruction in order
to make an ultrasound examination more convenient for both the patient and the sonographer. Testing our
algorithm demonstrated a significant performance gain by an average factor of 197 for simple interpolation
and 84 for advanced interpolation schemes, reconstructing a 2563 volume in 0.35 seconds and 0.82 seconds
respectively.
Simulation of ultrasound (US) images from volumetric medical image data has been shown to be an important
tool in medical image analysis. However, there is a trade off between the accuracy of the simulation and its real-time
performance. In this paper, we present a framework for acceleration of ultrasound simulation on the graphics
processing unit (GPU) of commodity computer hardware. Our framework can accommodate ultrasound modeling
with varying degrees of complexity. To demonstrate the flexibility of our proposed method, we have implemented
several models of acoustic propagation through 3D volumes. We conducted multiple experiments to evaluate
the performance of our method for its application in multi-modal image registration and training. The results
demonstrate the high performance of the GPU accelerated simulation outperforming CPU implementations by
up to two orders of magnitude and encourage the investigation of even more realistic acoustic models.
KEYWORDS: Calibration, Ultrasonography, Technetium, Image sensors, 3D image reconstruction, In vivo imaging, Transducers, 3D acquisition, Liver, Reconstruction algorithms
For freehand ultrasound systems, a calibration method is necessary to locate the position and orientation of a
2D B-mode ultrasound image plane with respect to a position sensor attached to the transducer. In addition,
the acquisition time discrepancy between the position measurements and the image frames has the be computed.
We developed a new method that adresses both of these problems, based on the fact that a freehand ultrasound
system establishes consistent 3D data of an arbitrary object. Two angulated sweeps of any object containing
well visible structures are recorded, each at a different orientation. A non-linear optimization strategy maximizes
the similarity of 2D ultrasound images from one sweep to reconstructions computed from the other sweep. No
designated phantom is required for this calibration. The process can be performed in vivo on the patient. We
evaluated our method using freehand acquisitions on both a phantom and the human liver. The accuracy of the
approach was validated using a 3D ultrasound probe as a known reference geometry.
We present a set of new methods for efficient and precise registration of any X-Ray modality (fluoroscopy, portal imaging or regular X-Ray imaging) to a CT data set. These methods require neither feature extraction nor 2D or 3D segmentation. Our main contribution is to directly perform the computations on the gradient vector volume of the CT data, which has several advantages. It can increase the precision of the registration as it assesses mainly the alignment of intensity edges in both CT and X-Ray images. By using only significant areas of the gradient vector volume, the amount of information needed in each registration step can be reduced up to a factor of 10. This both speeds up the registration process and allows for using the CT data with full precision, e.g. 5123 voxels. We introduce a Volume Gradient Rendering (VGR) as well as a Volume Gradient Correlation (VGC) method, where the latter one can be used directly for computing the image similarity without DBR generation.
Conference Committee Involvement (12)
Image Processing
17 February 2025 | San Diego, California, United States
Image Processing
19 February 2024 | San Diego, California, United States
Image Processing
20 February 2023 | San Diego, California, United States
Image Processing
20 February 2022 | San Diego, California, United States
Image Processing
15 February 2021 | Online Only, California, United States
Image Processing
17 February 2020 | Houston, Texas, United States
Image Processing
19 February 2019 | San Diego, California, United States
Image Processing
11 February 2018 | Houston, Texas, United States
Image Processing Posters
12 February 2017 | Orlando, FL, United States
Image Processing
12 February 2017 | Orlando, Florida, United States
Image Processing
1 March 2016 | San Diego, California, United States
Image Processing
24 February 2015 | Orlando, Florida, United States
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