Prior-Free Respiratory Motion Estimation in Rotational Angiography

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

Motion estimation based on projective information disentanglement for 3D reconstruction of rotational coronary angiography

The 2D projection space-based motion compensation reconstruction (2D-MCR) is a kind of representative method for 3D reconstruction of rotational coronary angiography owing to its high efficiency. However, due to the lack of accurate motion estimation of the overlapping projection pixels, existing 2D-MCR methods may still have a certain level of under-sampling artifacts or lose accuracy for cases with strong cardiac motion. To overcome this, in this study, we proposed a motion estimation approach based on projective information disentanglement (PID-ME) for 3D reconstruction of rotational coronary angiography. The reconstruction method adopts the framework of 2D-MCR, which is referred to as 2D-PID-MCR. The PID-ME consists of two parts: generation of the reference projection sequence based on the fast simplified distance driven projector (FSDDP) algorithm, motion estimation and correction based on the projective average minimal distance measure (PAMD) model. The FSDDP algorithm generates the reference projection sequence faster and accelerates the whole reconstruction greatly. The PAMD model can disentangle the projection information effectively and estimate the motion of both overlapping and non-overlapping projection pixels accurately. The main contribution of this study is the construction of 2D-PID-MCR to overcome the inherent limitations of the existing 2D-MCR method. Simulated and clinical experiments show that the PID-ME, consisting of FSDDP and PAMD, can estimate the motion of the projection sequence data accurately and efficiently. Our 2D-PID-MCR method outperforms the state-of-the-art approaches in terms of accuracy and real-time performance.

Three-dimensional regions-of-interest-based intra-operative four-dimensional soft tissue perfusion imaging using a standard x-ray system with no gantry rotation: A simulation study for a proof of concept

PURPOSE

Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4-10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative four-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation.

METHODS

IPEN uses two input datasets: (a) the contours and locations of three-dimensional regions-of-interest (ROIs) such as arteries and sub-sections of cancerous lesions, and (b) a series of x-ray projection data obtained from an intra-arterial contrast injection to contrast enhancement to wash-out. IPEN then estimates a time-enhancement curve (TEC) for each ROI directly from projections without reconstructing cross-sectional images by maximizing the agreement between synthesized and measured projections with a temporal roughness penalty. When path lengths through ROIs are known for each x-ray beam, the ROI-specific enhancement can be accurately estimated from projections. Computer simulations are performed to assess the performance of the IPEN algorithm. Intra-arterial contrast-enhanced liver scans over 25 s were simulated using XCAT phantom version 2.0 with heterogeneous tissue textures and cancerous lesions. The following four sub-studies were performed: (a) The accuracy of the estimated TECs with overlapped lesions was evaluated at various noise (dose) levels with either homogeneous or heterogeneous lesion enhancement patterns; (b) the accuracy of IPEN with inaccurate ROI contours was assessed; (c) we investigated how overlapping ROIs and noise in projections affected the accuracy of the IPEN algorithm; and (d) the accuracy of the perfusion indices was assessed.

RESULTS

The TECs estimated by IPEN were sufficiently accurate at a reference dose level with the root-mean-square deviation (RMSD) of 0.0027 ± 0.0001 cm^-1 or 13 ± 1 Hounsfield unit (mean ± standard deviation) for the homogeneous lesion enhancement and 0.0032 ± 0.0005 cm^-1 for the heterogeneous enhancement (N = 20 each). The accuracy was degraded with decreasing doses: The RMSD with homogeneous enhancement was 0.0220 ± 0.0003 cm^-1 for 20% of the reference dose level. Performing 3 × 3 pixel averaging on projection data improved the RMSDs to 0.0051 ± 0.0002 cm^-1 for 20% dose. When the ROI contours were inaccurate, smaller ROI contours resulted in positive biases in TECs, whereas larger ROI contours produced negative biases. The bias remained small, within ± 0.0070 cm^-1 , when the Sorenson-Dice coefficients (SDCs) were larger than 0.81. The RMSD of the TEC estimation was strongly associated with the condition of the problem, which can be empirically quantified using the condition number of a matrix A z that maps a vector of ROI enhancement values z to projection data and a weighted variance of projection data: a linear correlation coefficient (R) was 0.794 (P < 0.001). The perfusion index values computed from the estimated TECs agreed well with the true values (R ≥ 0.985, P < 0.0001).

CONCLUSION

The IPEN algorithm can estimate ROI-specific TECs with high accuracy especially when 3 × 3 pixel averaging is applied, even when lesion enhancement is heterogeneous, or ROI contours are inaccurate but the SDC is at least 0.81.

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

X-ray angiography is the most commonly used imaging modality for the detection of coronary stenoses due to its high spatial and temporal resolution of lumen contour and its utility to guide coronary interventions in real time. However, the high inter- and intra-observer variability in interpreting the geometry of 3D vascular structure based on multiple 2D image projections is a limitation in the accurate determination of lesion severity. This could be addressed by the 3D reconstruction of the coronary arterial (CA) tree. The automated reconstruction of 3D CA tree from 2D projections is challenging due to the existence of several imaging artifacts, such as vessel overlap, foreshortening, and most importantly respiratory and cardiac motion. Along with these artifacts, the acquisition geometry introduces the possibility of generating false vessel segments in the reconstruction. Our approach aims to reduce the motion artifacts in angiographic projections by developing a new method for rigid and non-rigid motion correction. A novel point-cloud based approach is subsequently introduced for reconstruction of 3D vessel centerlines by iteratively minimizing the reconstruction error. The performance of the proposed 3D reconstruction is evaluated using angiographic projections from 45 patients, producing average reprojection errors of 0.092 ±0.055 mm and 0.910 ±0.352 mm for 3D centerlines reconstruction, when co-registered with the parent vessels on projection planes that were/were not used to derive the 3D reconstruction, respectively. A comparison of the reconstructed 3D lumen surface with optical coherence tomography (OCT) measurements has been performed, showing no statistically significant difference in the luminal cross-sections reconstructed with our method, compared to OCT.