Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy

Background: Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy. Method: Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively. Results: High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively. Conclusions: This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems

A statistical method for retrospective cardiac and respiratory motion gating of interventional cardiac x-ray images

Purpose: Image-guided cardiac interventions involve the use of fluoroscopic images to guide the insertion and movement of interventional devices. Cardiorespiratory gating can be useful for 3D reconstruction from multiple x-ray views and for reducing misalignments between 3D anatomical models overlaid onto fluoroscopy. Methods: The authors propose a novel and potentially clinically useful retrospective cardiorespiratory gating technique. The principal component analysis (PCA) statistical method is used in combination with other image processing operations to make our proposed masked-PCA technique suitable for cardiorespiratory gating. Unlike many previously proposed techniques, our technique is robust to varying image-content, thus it does not require specific catheters or any other optically opaque structures to be visible. Therefore, it works without any knowledge of catheter geometry. The authors demonstrate the application of our technique for the purposes of retrospective cardiorespiratory gating of normal and very low dose x-ray fluoroscopy images. Results: For normal dose x-ray images, the algorithm was validated using 28 clinical electrophysiology x-ray fluoroscopy sequences (2168 frames), from patients who underwent radiofrequency ablation (RFA) procedures for the treatment of atrial fibrillation and cardiac resynchronization therapy procedures for heart failure. The authors established end-systole, end-expiration, and end-inspiration success rates of 97.0%, 97.9%, and 97.0%, respectively. For very low dose applications, the technique was tested on ten x-ray sequences from the RFA procedures with added noise at signal to noise ratio (SNR) values of √50, √10, √8, √6, √5, √2 and √1 to simulate the image quality of increasingly lower dose x-ray images. Even at the low SNR value of √2, representing a dose reduction of more than 25 times, gating success rates of 89.1%, 88.8%, and 86.8% were established. Conclusions: The proposed technique can therefore extract useful information from interventional x-ray images while minimizing exposure to ionizing radiation. © 2014 American Association of Physicists in Medicine

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Continuous roadmapping in liver TACE procedures using 2D–3D catheter-based registration

PURPOSE: Fusion of pre/perioperative images and intra-operative images may add relevant information during image-guided procedures. In abdominal procedures, respiratory motion changes the position of organs, and thus accurate image guidance requires a continuous update of the spatial alignment of the (pre/perioperative) information with the organ position during the intervention. METHODS: In this paper, we propose a method to register in real time perioperative 3D rotational angiography images (3DRA) to intra-operative single-plane 2D fluoroscopic images for improved guidance in TACE interventions. The method uses the shape of 3D vessels extracted from the 3DRA and the 2D catheter shape extracted from fluoroscopy. First, the appropriate 3D vessel is selected from the complete vascular tree using a shape similarity metric. Subsequently, the catheter is registered to this vessel, and the 3DRA is visualized based on the registration results. The method is evaluated on simulated data and clinical data. RESULTS: The first selected vessel, ranked with the shape similarity metric, is used more than 39 % in the final registration and the second more than 21 %. The median of the closest corresponding points distance between 2D angiography vessels and projected 3D vessels is 4.7–5.4 mm when using the brute force optimizer and 5.2–6.6 mm when using the Powell optimizer. CONCLUSION: We present a catheter-based registration method to continuously fuse a 3DRA roadmap arterial tree onto 2D fluoroscopic images with an efficient shape similarity

Respiratory Motion Compensation in Coronary Magnetic Resonance Angiography: Analysis and Optimization of Self-Navigation

Coronary Magnetic Resonance Imaging requires prolonged acquisition times; for this reason, respiratory movements of the heart have a great impact on the final image quality. The aim of this thesis was to provide possible optimization of the "self-navigation" approach to compensate this type of motion. Two developed methods were tested in 11 volunteer, thus providing statistically significant results. The purposed solutions provided optimal image quality in individal cases

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures