Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions

X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging. The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework. The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model. Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Carotid plaque stress analysis by fluid structure interaction based on in-vivo MRI: Implications to plaque vulnerability assessment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 2010.Stroke is one of the leading causes of death in the world, resulting mostly from the sudden rupture of atherosclerotic plaques. From a biomechanical view, plaque rupture can be considered as a mechanical failure caused by extremely high plaque stress. In this PhD project, we are aiming to predict 3D plaque stress based on in-vivo MRI by using fluid structure interaction (FSI) method, and provide information for plaque rupture risk assessment. Fluid structure interaction was implemented with ANSYS 11.0, followed by a parameter study on fibrous cap thickness and lipid core size with realistic carotid plaque geometry. Twenty patients with carotid plaques imaged by in-vivo MRI were provided in the project. A framework of reconstructing 3D plaque geometry from in-vivo multispectral MRI was designed. The followed reproducibility study on plaque geometry reconstruction procedure and its effect on plaque stress analysis filled the gap in the literature on imaging based plaque stress modeling. The results demonstrated that current MRI technology can provide sufficient information for plaque structure characterization; however stress analysis result is highly affected by MRI resolution and quality. The application of FSI stress analysis to 4 patients with different plaque burdens has showed that the whole procedure from plaque geometry reconstruction to FSI stress analysis was applicable. In the study, plaque geometries from three patients with recent transient ischemic attack were reconstructed by repairing ruptured fibrous cap. The well correlated relationship between local stress concentrations and plaque rupture sites indicated that extremely high plaque stress could be a factor responsible for plaque rupture. Based on the 20 reconstructed carotid plaques from two groups (symptomatic and asymptomatic), fully coupled fluid structure interaction was performed. It was found that there is a significant difference between symptomatic and asymptomatic patients in plaque stress levels, indicating plaque stress could be used as one of the factors for plaque vulnerability assessment. A corresponding plaque morphological feature study showed that plaque stress is significantly affected by fibrous cap thickness, lipid core size and fibrous cap surface irregularities (curvedness). A procedure was proposed for predicting plaque stress by using fibrous cap thickness and curvedness, which requires much less computational time, and has the potential for clinical routine application. The effects of residual stress on plaque stress analysis and arterial wall material property characterization by using in-vivo MRI data were also discussed for patient specific modeling. As the further development, histological study of plaque sample has been combined with conventional plaque stress analysis by assigning material properties to each computational element, based on the data from histological analysis. This method could bridge the gap between biochemistry and biomechanical study of atherosclerosis plaques. In conclusion, extreme stress distributions in the plaque region can be predicted by modern numerical methods, and used for plaque rupture risk assessment, which will be helpful in clinical practice. The combination of plaque MR imaging analysis, computational modelling, and clinical study/ validation would advance our understandings of plaque rupture, prediction of future rupture, and establish new procedures for patient diagnose, management, and treatment.Financial Support was obtained from British Heart Foundation, Brunel Institute for Bioengineering and Brunel Graduate School

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of +/- 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 +/- 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 +/- 192 ms, 78 +/- 183 ms and 59 +/- 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.Cardiovascular Aspects of Radiolog

Imaging of Coronary Atherosclerosis with Computed Tomography Coronary Angiography

Coronary atherosclerosis is a worldwide pandemic disease and accounts for almost 17 million deaths annually. The detection and accurate quantification of coro