Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN). MATERIALS AND METHODS: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. RESULTS: The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. CONCLUSION: The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.

Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

PURPOSE: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination. MATERIALS AND METHODS: An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies. On examinations without adenopathies, the score was given by the formula M/(M+A) where M was the mean adenopathy volume per patient and A the volume segmented by the algorithm. The networks were trained on 117 annotated CT acquisitions. RESULTS: The test set included 150 additional CT acquisitions unseen during the training. The performance on the test set yielded a mean score of 0.63. CONCLUSION: Despite limited available data and partial annotations, our CNN based approach achieved promising results in the task of cervical lymphadenopathy segmentation. It has the potential to bring precise quantification to the clinical workflow and to assist the clinician in the detection task.

Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions.

PURPOSE: X-ray fluoroscopically guided cardiac electrophysiology (EP) procedures are commonly carried out to treat patients with arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of a three-dimensional (3D) roadmap derived from preprocedural volumetric images can be used to add anatomical information. It is useful to know the position of the catheter electrodes relative to the cardiac anatomy, for example, to record ablation therapy locations during atrial fibrillation therapy. Also, the electrode positions of the coronary sinus (CS) catheter or lasso catheter can be used for road map motion correction. METHODS: In this paper, the authors present a novel unified computational framework for image-based catheter detection and tracking without any user interaction. The proposed framework includes fast blob detection, shape-constrained searching and model-based detection. In addition, catheter tracking methods were designed based on the customized catheter models input from the detection method. Three real-time detection and tracking methods are derived from the computational framework to detect or track the three most common types of catheters in EP procedures: the ablation catheter, the CS catheter, and the lasso catheter. Since the proposed methods use the same blob detection method to extract key information from x-ray images, the ablation, CS, and lasso catheters can be detected and tracked simultaneously in real-time. RESULTS: The catheter detection methods were tested on 105 different clinical fluoroscopy sequences taken from 31 clinical procedures. Two-dimensional (2D) detection errors of 0.50 ± 0.29, 0.92 ± 0.61, and 0.63 ± 0.45 mm as well as success rates of 99.4%, 97.2%, and 88.9% were achieved for the CS catheter, ablation catheter, and lasso catheter, respectively. With the tracking method, accuracies were increased to 0.45 ± 0.28, 0.64 ± 0.37, and 0.53 ± 0.38 mm and success rates increased to 100%, 99.2%, and 96.5% for the CS, ablation, and lasso catheters, respectively. Subjective clinical evaluation by three experienced electrophysiologists showed that the detection and tracking results were clinically acceptable. CONCLUSIONS: The proposed detection and tracking methods are automatic and can detect and track CS, ablation, and lasso catheters simultaneously and in real-time. The accuracy of the proposed methods is sub-mm and the methods are robust toward low-dose x-ray fluoroscopic images, which are mainly used during EP procedures to maintain low radiation dose.

Registration of 3D trans-esophageal echocardiography to X-ray fluoroscopy using image-based probe tracking.

Two-dimensional (2D) X-ray imaging is the dominant imaging modality for cardiac interventions. However, the use of X-ray fluoroscopy alone is inadequate for the guidance of procedures that require soft-tissue information, for example, the treatment of structural heart disease. The recent availability of three-dimensional (3D) trans-esophageal echocardiography (TEE) provides cardiologists with real-time 3D imaging of cardiac anatomy. Increasingly X-ray imaging is now supported by using intra-procedure 3D TEE imaging. We hypothesize that the real-time co-registration and visualization of 3D TEE and X-ray fluoroscopy data will provide a powerful guidance tool for cardiologists. In this paper, we propose a novel, robust and efficient method for performing this registration. The major advantage of our method is that it does not rely on any additional tracking hardware and therefore can be deployed straightforwardly into any interventional laboratory. Our method consists of an image-based TEE probe localization algorithm and a calibration procedure. While the calibration needs to be done only once, the GPU-accelerated registration takes approximately from 2 to 15s to complete depending on the number of X-ray images used in the registration and the image resolution. The accuracy of our method was assessed using a realistic heart phantom. The target registration error (TRE) for the heart phantom was less than 2mm. In addition, we assess the accuracy and the clinical feasibility of our method using five patient datasets, two of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures. The registration results showed our technique had mean registration errors of 1.5-4.2mm and 95% capture range of 8.7-11.4mm in terms of TRE.

Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures.

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.

Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking.

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3D roadmaps derived from pre-procedural volumetric data can be used to add anatomical information. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory motion. We propose a novel method to correct for respiratory motion using real-time image-based coronary sinus (CS) catheter tracking. The first step of the proposed technique is to use a blob detection method to detect all possible catheter electrodes in the Xray data. We then compute a cost function to select one CS catheter from all catheter-like objects. For correcting respiratory motion, we apply a low pass filter to the 2D motion of the CS catheter and update the 3D roadmap using this filtered motion. We tested our CS catheter tracking method on 1048 fluoroscopy frames from 15 patients and achieved a success rate of 99.3% and an average 2D tracking error of 0.4 mm +/- 0.2 mm. We also validated our respiratory motion correction strategy by computing the 2D target registration error (TRE) at the pulmonary veins and achieved a TRE of 1.6 mm +/- 0.9 mm.