Electromagnetic (EM) tracking systems are often used for real time navigation of medical tools in an Image
Guided Therapy (IGT) system. They are specifically advantageous when the medical device requires tracking
within the body of a patient where line of sight constraints prevent the use of conventional optical tracking. EM
tracking systems are however very sensitive to electromagnetic field distortions. These distortions, arising from
changes in the electromagnetic environment due to the presence of conductive ferromagnetic surgical tools or
other medical equipment, limit the accuracy of EM tracking, in some cases potentially rendering tracking data
unusable. We present a mapping method for the operating region over which EM tracking sensors are used,
allowing for characterization of measurement errors, in turn providing physicians with visual feedback about
measurement confidence or reliability of localization estimates.
In this instance, we employ a calibration phantom to assess distortion within the operating field of the
EM tracker and to display in real time the distribution of measurement errors, as well as the location and
extent of the field associated with minimal spatial distortion. The accuracy is assessed relative to successive
measurements. Error is computed for a reference point and consecutive measurement errors are displayed relative
to the reference in order to characterize the accuracy in near-real-time. In an initial set-up phase, the phantom
geometry is calibrated by registering the data from a multitude of EM sensors in a non-ferromagnetic ("clean")
EM environment. The registration results in the locations of sensors with respect to each other and defines
the geometry of the sensors in the phantom. In a measurement phase, the position and orientation data from
all sensors are compared with the known geometry of the sensor spacing, and localization errors (displacement
and orientation) are computed. Based on error thresholds provided by the operator, the spatial distribution of
localization errors are clustered and dynamically displayed as separate confidence zones within the operating
region of the EM tracker space.
Static X-ray computed tomography (CT) volumes are often used as anatomic roadmaps during catheter-based cardiac
interventions performed under X-ray fluoroscopy guidance. These CT volumes provide a high-resolution depiction of
soft-tissue structures, but at only a single point within the cardiac and respiratory cycles. Augmenting these static CT
roadmaps with segmented myocardial borders extracted from live ultrasound (US) provides intra-operative access to
real-time dynamic information about the cardiac anatomy. In this work, using a customized segmentation method based
on a 3D active mesh, endocardial borders of the left ventricle were extracted from US image streams (4D data sets) at a
frame rate of approximately 5 frames per second. The coordinate systems for CT and US modalities were registered
using rigid body registration based on manually selected landmarks, and the segmented endocardial surfaces were
overlaid onto the CT volume. The root-mean squared fiducial registration error was 3.80 mm. The accuracy of the
segmentation was quantitatively evaluated in phantom and human volunteer studies via comparison with manual
tracings on 9 randomly selected frames using a finite-element model (the US image resolutions of the phantom and
volunteer data were 1.3 x 1.1 x 1.3 mm and 0.70 x 0.82 x 0.77 mm, respectively). This comparison yielded 3.70±2.5
mm (approximately 3 pixels) root-mean squared error (RMSE) in a phantom study and 2.58±1.58 mm (approximately 3
pixels) RMSE in a clinical study. The combination of static anatomical roadmap volumes and dynamic intra-operative
anatomic information will enable better guidance and feedback for image-guided minimally invasive cardiac
interventions.
This work presents an integrated system for multimodality image guidance of minimally invasive medical procedures.
This software and hardware system offers real-time integration and registration of multiple image streams with
localization data from navigation systems. All system components communicate over a local area Ethernet network,
enabling rapid and flexible deployment configurations. As a representative configuration, we use X-ray fluoroscopy
(XF) and ultrasound (US) imaging. The XF imaging system serves as the world coordinate system, with gantry geometry
derived from the imaging system, and patient table position tracked with a custom-built measurement device using linear
encoders. An electromagnetic (EM) tracking system is registered to the XF space using a custom imaging phantom that
is also tracked by the EM system. The RMS fiducial registration error for the EM to X-ray registration was 2.19 mm,
and the RMS target registration error measured with an EM-tracked catheter was 8.81 mm. The US image stream is
subsequently registered to the XF coordinate system using EM tracking of the probe, following a calibration of the US
image within the EM coordinate system. We present qualitative results of the system in operation, demonstrating the
integration of live ultrasound imaging spatially registered to X-ray fluoroscopy with catheter localization using
electromagnetic tracking.
KEYWORDS: Magnetic resonance imaging, Heart, Image segmentation, Image registration, Image fusion, 3D image processing, X-rays, Tissues, In vivo imaging, 3D acquisition
The utility of X-ray fused with MRI (XFM) using external fiducial markers to perform targeted endomyocardial injections in infarcted hearts of swine was tested. Endomyocardial injections of feridex-labeled mesenchymal stromal cells (Fe-MSC) were performed in the previously infarcted hearts of 12 Yucatan miniswine (33-67 kg). Animals had pre-injection cardiac MRI, XFM-guided endomyocardial injection of Fe-MSC suspension spiked with tissue dye, and post-injection MRI. 24 hours later, after euthanasia, the hearts were excised, sliced and stained with TTC. During the injection procedure, operators were provided with 3D surfaces of endocardium, epicardium, myocardial wall thickness and infarct registered with live XF images to facilitate device navigation and choice of injection location. 130 injections were performed in hearts where diastolic wall thickness ranged from 2.6 to 17.7 mm. Visual inspection of the pattern of dye staining on TTC stained heart slices correlated (r=0.98) with XFM-derived injection locations mapped onto delayed hyperenhancement MRI and the susceptibility artifacts seen on the post-injection T2*-weighted gradient echo MRI. The in vivo target registration error was 3.17±2.61 mm (n=64) and 75% of injections were within 4 mm of the predicted location. 3D to 2D registration of XF and MR images using external fiducial markers enables accurate targeted endomyocardial injection in a swine model of myocardial infarction. The present data suggest that the safety and efficacy of this approach for performing targeted endomyocardial delivery should be evaluated further clinically.
We present our co-registration results of two complementary imaging modalities, MRI and X-ray angiography (XA), using dual modality fiducial markers. Validation experiments were conducted using a vascular phantom with eight fiducial markers around its periphery. Gradient-distortion-corrected 3D MRI was used to image the phantom and determine the 3D locations of the markers. XA imaging was performed at various C-arm orientations. These images were corrected for geometric distortion, and projection parameters were optimized using a calibration phantom. Closed-form 3D-to-3D rigid-body registration was performed between the MR markers and a 3D reconstruction of the markers from multiple XA images. 3D-to-2D registration was performed using a single XA image by projecting the MR markers onto the XA image and iteratively minimizing the 2D errors between the projected markers and their observed locations in the image. The RMS registration error was 0.77 mm for the 3D-to-3D registration, and 1.53 pixels for the 3D-to-2D registration. We also showed that registration can be performed at a large IS where many markers are visible, then the image can be zoomed in maintaining the registration. This requires calibration of imperfections in the zoom operation of the image intensifier. When we applied the registration used for an IS of 330 mm to an image acquired with an IS of 130 mm, the error was 42.16 pixels before zoom correction and 3.37 pixels after. This method offers the possibility of new therapies where the soft-tissue contrast of MRI and the high-resolution imaging of XA are both needed.
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