Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Zhu, Ying; Tsin, Yanghai; Sundar, Hari; Sauer, Frank

doi:10.1007/978-3-642-15711-0_36

Cited by 17 publications

(25 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, according to this definition Li et al (2006b), Rohlfing et al (2004), and Zhu et al (2010) are not classified as motion models, as they only make motion estimates at a number of discrete respiratory positions; whereas Rit et al (2009) is classified as a motion model, as the proposed technique is capable of interpolating a motion estimate between these discrete positions.…”

Section: Scope Of the Papermentioning

confidence: 98%

See 1 more Smart Citation

Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

348

343

View full text Add to dashboard Cite

The problem of respiratory motion has proved a serious obstacle in developing techniques to acquire images or guide interventions in abdominal and thoracic organs. Motion models offer a possible solution to these problems, and as a result the field of respiratory motion modelling has become an active one over the past 15 years. A motion model can be defined as a process that takes some surrogate data as input and produces a motion estimate as output. Many techniques have been proposed in the literature, differing in the data used to form the models, the type of model employed, how this model is computed, the type of surrogate data used as input to the model in order to make motion estimates and what form this output should take. In addition, a wide range of different application areas have been proposed. In this paper we summarise the state of the art in this important field and in the process highlight the key papers that have driven its advance. The intention is that this will serve as a timely review and comparison of the different techniques proposed to date and as a basis to inform future research in this area.

show abstract

Section: Scope Of the Papermentioning

confidence: 98%

“…The term 'motion model' has been used in the literature to refer to a number of different concepts, including a series of geometric transformations to different respiratory positions, both with (Rit et al, 2009) and without (Li et al, 2006b;Rohlfing et al, 2004;Zhu et al, 2010) interpolation between them.…”

Section: Scope Of the Papermentioning

confidence: 99%

Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

348

343

View full text Add to dashboard Cite

show abstract

“…The proposed method is compared with registrationbased approach [24]. In their method, the first image is fixed as the reference image.…”

Section: Vascular Structure Trackingmentioning

confidence: 99%

“…We use the same evaluation metric for catheter tracking. Registration-based method [24] and bipartite graph matching are also compared with our method. The comparison results of catheter tracking are listed in Table 2.…”

Section: Catheter Trackingmentioning

confidence: 99%

“…Bhagalia et al [4] presented an importance sampling approach which used sampled image points with larger gradients to perform registration. To cope with static structures in X-ray images, Zhu et al [24] introduced a method to separate static structures from moving structures through temporal statistics across frames. The registration is achieved by variational approach [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Cheng

Pang

Zhu

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

Self Cite

View full text Add to dashboard Cite

Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field as a result of complex 3D anatomical movements projected into 2D images. To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation. Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 1 tensor approximation. The model prior is propagated in the course of deformable tracking. Both the higher order information and the model prior provide powerful discriminative cues for reducing ambiguity arising from the complex background, and consequently improve the tracking robustness. To validate the proposed approach, we applied it to catheter and vascular structures tracking and tested on X-ray fluoroscopic sequences obtained from 17 clinical cases. The results show, both quantitatively and qualitatively, that our approach achieves a mean tracking error of 1.4 pixels for vascular structure and 1.3 pixels for catheter tracking.

show abstract

Real‐time respiratory motion compensated roadmaps for hepatic arterial interventions

et al. 2021

View full text Add to dashboard Cite

Purpose During hepatic arterial interventions, catheter or guidewire position is determined by referencing or overlaying a previously acquired static vessel roadmap. Respiratory motion leads to significant discrepancies between the true position and configuration of the hepatic arteries and the roadmap, which makes navigation and accurate catheter placement more challenging and time consuming. The purpose of this work was to develop a dynamic respiratory motion compensated device guidance system and evaluate the accuracy and real‐time performance in an in vivo porcine liver model. Methods The proposed device navigation system estimates a respiratory motion model for the hepatic vasculature from prenavigational X‐ray image sequences acquired under free‐breathing conditions with and without contrast enhancement. During device navigation, the respiratory state is tracked based on live fluoroscopic images and then used to estimate vessel deformation based on the previously determined motion model. Additionally, guidewires and catheters are segmented from the fluoroscopic images using a deep learning approach. The vessel and device information are combined and shown in a real‐time display. Two different display modes are evaluated within this work: (1) a compensated roadmap display, where the vessel roadmap is shown moving with the respiratory motion; (2) an inverse compensated device display, where the device representation is compensated for respiratory motion and overlaid on a static roadmap. A porcine study including seven animals was performed to evaluate the accuracy and real‐time performance of the system. In each pig, a guidewire and microcatheter with a radiopaque marker were navigated to distal branches of the hepatic arteries under fluoroscopic guidance. Motion compensated displays were generated showing real‐time overlays of the vessel roadmap and intravascular devices. The accuracy of the motion model was estimated by comparing the estimated vessel motion to the motion of the X‐ray visible marker. Results The median (minimum, maximum) error across animals was 1.08 mm (0.92 mm, 1.87 mm). Across different respiratory states and vessel branch levels, the odds of the guidewire tip being shown in the correct vessel branch were significantly higher (odds ratio = 3.12, p < 0.0001) for motion compensated displays compared to a noncompensated display (median probabilities of 86 and 69%, respectively). The average processing time per frame was 17 ms. Conclusions The proposed respiratory motion compensated device guidance system increased the accuracy of the displayed device position relative to the hepatic vasculature. Additionally, the provided display modes combine both vessel and device information and do not require the mental integration of different displays by the physician. The processing times were well within the range of conventional clinical frame rates.

show abstract

Image-Based Respiratory Motion Compensation for Fluoroscopic Coronary Roadmapping

Cited by 17 publications

References 13 publications

Respiratory motion models: A review

Respiratory motion models: A review

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Real‐time respiratory motion compensated roadmaps for hepatic arterial interventions

Contact Info

Product

Resources

About