Viewpoint planning for quantitative coronary angiography

An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation

PURPOSE

Percutaneous fracture fixation involves multiple X-ray acquisitions to determine adequate tool trajectories in bony anatomy. In order to reduce time spent adjusting the X-ray imager's gantry, avoid excess acquisitions, and anticipate inadequate trajectories before penetrating bone, we propose an autonomous system for intra-operative feedback that combines robotic X-ray imaging and machine learning for automated image acquisition and interpretation, respectively.

METHODS

Our approach reconstructs an appropriate trajectory in a two-image sequence, where the optimal second viewpoint is determined based on analysis of the first image. A deep neural network is responsible for detecting the tool and corridor, here a K-wire and the superior pubic ramus, respectively, in these radiographs. The reconstructed corridor and K-wire pose are compared to determine likelihood of cortical breach, and both are visualized for the clinician in a mixed reality environment that is spatially registered to the patient and delivered by an optical see-through head-mounted display.

RESULTS

We assess the upper bounds on system performance through in silico evaluation across 11 CTs with fractures present, in which the corridor and K-wire are adequately reconstructed. In post hoc analysis of radiographs across 3 cadaveric specimens, our system determines the appropriate trajectory to within 2.8 ± 1.3 mm and 2.7 ± 1.8[Formula: see text].

CONCLUSION

An expert user study with an anthropomorphic phantom demonstrates how our autonomous, integrated system requires fewer images and lower movement to guide and confirm adequate placement compared to current clinical practice. Code and data are available.

CoroFinder: A New Tool for Real Time Detection and Tracking of Coronary Arteries in Contrast-Free Cine-Angiography

Coronary Angiography (CA) is the standard of reference to diagnose coronary artery disease. Yet, only a portion of the information it conveys is usually used. Quantitative Coronary Angiography (QCA) reliably contributes to improving the measurable assessment of CA. In this work, we developed a new software, CoroFinder, able to automatically identify epicardial coronary arteries and to dynamically track the vessel profile in dye-free frames. The coronary tree is automatically segmented by Frangi’s filter in the angiogram’s frames where vessels are contrasted (“template frames”). Afterward, the image similarity among each template frame and the dye-free images is scored by cross-correlation. Finally, each dye-free image is associated with the most similar template frame, resulting in an estimation of vessel contour. CoroFinder allows locating the position of coronary arteries in absence of contrast dye. The developed algorithm is robust to diverse vessel curvatures, variation of vessel widths, and the presence of stenoses. This article describes the newly developed CoroFinder algorithm and the associated software and provides an overview of its potential application in research and for translation to the clinic.

Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video

Aim: The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear.

Methods: The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model.

Results: The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively.

Conclusion: An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.

Co-localized augmented human and X-ray observers in collaborative surgical ecosystem

PURPOSE

Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy.

METHODS

In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping.

RESULTS

We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and [Formula: see text], respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm.

CONCLUSIONS

The proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.

Impact of Cine Frame Selection on Quantitative Coronary Angiography Results

We evaluated intra- and interobserver variability of quantitative coronary angiography (QCA) due to cine frame selection for 9 coronary stenoses. The projection was selected in advance. Cine frames were selected by 2 blinded experts (blind frame QCA) followed by assignment by supervisor (pre-selected frame QCA). Each expert analyzed 18 frames twice with a 3-month interval. A total of 72 measurements by 2 experts were used for intra- and interobserver variability analysis in calibration factor (CF), minimal lumen diameter (MLD), percent diameter stenosis (%DS), interpolated reference diameter (Int R), and lesion length (LL). Accuracy, precision, and coefficient of variation (CV) were calculated based on 2 measurements. For interobserver variability, intraclass correlation coefficient (ICC) was evaluated. Regarding intraobserver variability, precision (CV) was 0.0026 (1.45), 0.220 (25.1), 0.282 (11.0), 7.626 (11.8), and 4.042 (28.7) for blind frame QCA and 0.0044 (2.46), 0.094 (11.2), 0.225 (8.6), 3.924 (5.9), and 1.941 (12.1) for pre-selected frame QCA and regarding interobserver variability, precision (CV) was 0.0037 (2.09), 0.271 (31.8), 0.307 (11.9), 10.10 (15.4), and 5.121 (39.5) for blind frame QCA and 0.0050 (2.82), 0.098 (11.4), 0.246 (9.5), 5.253 (8.0), and 2.857 (19.0) for pre-selected frame QCA in CF, MLD, Int R, %DS, and LL, respectively. Intraclass correlation coefficient of Int R was almost perfect in blind and pre-selected frame QCA. Intraclass correlation coefficient of MLD, %DS, and LL were substantial/lower by blind frame QCA and improved to almost perfect by pre-selected frame QCA. Blind cine film selection might affect intra- and interobserver variability, especially in MLD and LL. In the multiple linear regression analysis, blind frame QCA was selected as an explanatory factor of QCA variability in MLD, %DS, and LL. The error range due to frame selection must be taken into consideration in clinical use.