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Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.

Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.

Low frequency current density imaging (LFCDI) is a magnetic resonance imaging (MRI) technique which enables calculation of current pathways within the medium of study. The induced current produces a magnetic flux which presents itself in phase images obtained through MRI scanning. A class of LFCDI challenges arises from the subject rotation requirement, which calls for reliability analysis metrics and specific image registration techniques. In this study these challenges are formulated and in light of proposed discussions, the reliability analysis of calculation of current pathways in a designed phantom and a pig heart is presented. The current passed is measured with less than 5% error for phantom, using CDI method. It is shown that Gauss's law for magnetism can be treated as reliability metric in matching the images in two orientations. For the phantom and pig heart the usefulness of image registration for mitigation of rotation errors is demonstrated. The reliability metric provides a good representation of the degree of correspondence between images in two orientations for phantom and pig heart. In our CDI experiments this metric produced values of 95% and 26%, for phantom, and 88% and 75% for pig heart, for mismatch rotations of 0 and 20 degrees respectively.

Accuracy and robustness are fundamental requirements of any automated method used for segmentation of medical images. Model-based segmentation (MBS) is a well established technique, where uncertainties in image content can be to a certain extent compensated by the use of prior shape information. This approach is, however, often problematic in cases where image information does not allow for generating a strong feature response, one example being soft tissue organs in CT data, which typically appear in low contrast. In this paper, we enhance our recently proposed framework for voxel classification-based refinement of MBS using a level-set segmentation technique with shape priors. We also introduce a novel feature weighting methodology that improves the performance of the classifier, demonstrating results superior to the previous feature selection method. Results of fully automated segmentation of low contrast organs in head and neck CT are presented. Compared to our previous approach, we have achieved an increase of up to 22% in segmentation accuracy.

The procedure required for registering a surgical navigation system prior to use in a surgical procedure is conventionally a time-consuming manual process that is prone to human errors and must be repeated as necessary through the course of a procedure. The conventional procedure becomes even more time consuming when intra-operative 3D imaging such as the C-arm cone-beam CT (CBCT) is introduced, as each updated volume set requires a new registration. To improve the speed and accuracy of registering image and world reference frames in image-guided surgery, a novel automatic registration algorithm was developed and investigated. The surgical navigation system consists of either Polaris (Northern Digital Inc., Waterloo, ON) or MicronTracker (Claron Technology Inc., Toronto, ON) tracking camera(s), custom software (Cogito running on a PC), and a prototype CBCT imaging system based on a mobile isocentric C-arm (Siemens, Erlangen, Germany). Experiments were conducted to test the accuracy of automatic registration methods for both the MicronTracker and Polaris tracking cameras. Results indicate the automated registration performs as well as the manual registration procedure using either the Claron or Polaris camera. The average root-mean-squared (rms) observed target registration error (TRE) for the manual procedure was 2.58 +/- 0.42 mm and 1.76 +/- 0.49 mm for the Polaris and MicronTracker, respectively. The mean observed TRE for the automatic algorithm was 2.11 +/- 0.13 and 2.03 +/- 0.3 mm for the Polaris and MicronTracker, respectively. Implementation and optimization of the automatic registration technique in Carm CBCT guidance of surgical procedures is underway.

Respiratory motion varies on a daily basis in abdominal cancer patients, affecting the ability to successfully deliver local therapy and requiring increased treatment margins to account for this variation. Deformable registration techniques can accurately describe respiratory motion, however, online application can be limited by long computational times and user intervention. A technique has been developed to quickly quantify patient breathing motion from respiratory-sorted volumetric images by calculating 1D shifts in image intensities between spatially corresponding regions of interest (navigator channels) on patient's images. The 1D motion at the superior, inferior, anterior, and posterior liver edges was detected and applied to adapt a population liver respiratory motion model. For validation, deformable registration was performed for each patient using a validated technique, MORFEUS, for relative validation, and vessel bifurcations, identified on patient's inhale and exhale images, for absolute validation. The accuracy of the adapted-population model to describe the patient respiratory motion was (absolute mean +/- SD) 0.26 +/- 0.11 cm and 0.30 +/- 0.21 cm in the superior-inferior (SI) and anterior-posterior (AP) directions, respectively. The accuracy of predicting the tumor COM motion was 0.30 +/- 0.22 cm, and 0.34 +/- 0.31, while the absolute validation, based on bifurcations was 0.26 +/- 0.16 cm and 0.13 +/- 0.04 cm in the SI and AP directions, respectively. This technique was developed to complement and quickly adapt a full 3D biomechanical based deformable registration technique, MORFEUS, to be applied in the online setting.