KEYWORDS: Visualization, Magnetic resonance imaging, Human-machine interfaces, In vivo imaging, MATLAB, Prostate, Reconstruction algorithms, Computer aided diagnosis and therapy, Prostate cancer, Medical imaging
We propose a Markov Random Field (MRF) formulation for the intensity-based N-view 2D-3D registration problem. The
transformation aligning the 3D volume to the 2D views is estimated by iterative updates obtained by discrete optimization
of the proposed MRF model. We employ a pairwise MRF model with a fully connected graph in which the nodes represent
the parameter updates and the edges encode the image similarity costs resulting from variations of the values of adjacent
nodes. A label space refinement strategy is employed to achieve sub-millimeter accuracy. The evaluation on real and
synthetic data and comparison to state-of-the-art method demonstrates the potential of our approach.
In trauma and orthopedic surgery, imaging through X-ray fluoroscopy with C-arms is ubiquitous. This leads to
an increase in ionizing radiation applied to patient and clinical staff. Placing these devices in the desired position
to visualize a region of interest is a challenging task, requiring both skill of the operator and numerous X-rays
for guidance. We propose an extension to C-arms for which position data is available that provides the surgeon
with so called artificial fluoroscopy. This is achieved by computing digitally reconstructed radiographs (DRRs)
from pre- or intraoperative CT data. The approach is based on C-arm motion estimation, for which we employ
a Camera Augmented Mobile C-arm (CAMC) system, and a rigid registration of the patient to the CT data.
Using this information we are able to generate DRRs and simulate fluoroscopic images. For positioning tasks,
this system appears almost exactly like conventional fluoroscopy, however simulating the images from the CT
data in realtime as the C-arm is moved without the application of ionizing radiation. Furthermore, preoperative
planning can be done on the CT data and then visualized during positioning, e.g. defining drilling axes for
pedicle approach techniques. Since our method does not require external tracking it is suitable for deployment
in clinical environments and day-to-day routine. An experiment with six drillings into a lumbar spine phantom
showed reproducible accuracy in positioning the C-arm, ranging from 1.1 mm to 4.1 mm deviation of marker
points on the phantom compared in real and virtual images.
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.
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.
In this paper we present a novel method for analyzing and visualizing dynamic peristaltic motion of the colon in 3D from two series of differently oriented 2D MRI images. To this end, we have defined an MRI examination protocol, and introduced methods for spatio-temporal alignment of the two MRI image series into a common reference. This represents the main contribution of this paper, which enables the 3D analysis of peristaltic motion. The objective is to provide a detailed insight into this complex motion, aiding in the diagnosis and characterization of colon motion disorders. We have applied the proposed spatio-temporal method on Cine MRI data sets of healthy volunteers. The results have been inspected and validated by an expert radiologist. Segmentation and cylindrical approximation of the colon results in a 4D visualization of the peristaltic motion.
In the current clinical workflow of minimally invasive aortic procedures navigation tasks are performed under 2D
or 3D angiographic imaging. Many solutions for navigation enhancement suggest an integration of the preoperatively
acquired computed tomography angiography (CTA) in order to provide the physician with more image
information and reduce contrast injection and radiation exposure. This requires exact registration algorithms
that align the CTA volume to the intraoperative 2D or 3D images. Additional to the real-time constraint, the registration
accuracy should be independent of image dissimilarities due to varying presence of medical instruments
and contrast agent. In this paper, we propose efficient solutions for image-based 2D-3D and 3D-3D registration
that reduce the dissimilarities by image preprocessing, e.g. implicit detection and segmentation, and adaptive
weights introduced into the registration procedure. Experiments and evaluations are conducted on real patient
data.
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