Artificial intelligence in cardiovascular imaging and intervention.

Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these novel technologies enables concurrent text and image analysis by so-called vision-language transformer models. They not only allow for automatic derivation of image reports, synthesis of novel images conditioned on certain textual properties, and visual questioning and answering in an oral or written dialogue style, but also for the retrieval of medical images from a large database based on a description of the pathology or specifics of the dataset of interest. Federated learning is an additional ingredient in these novel developments, facilitating multi-centric collaborative training of AI approaches and therefore access to large clinical cohorts. In this review paper, we provide an overview of the recent developments in the field of cardiovascular imaging and intervention and offer a future outlook.

C-arm positioning for standard projections during spinal implant placement.

Fluoroscopy-guided trauma and orthopedic surgeries involve the repeated acquisition of correct anatomy-specific standard projections for guidance, monitoring, and evaluating the surgical result. C-arm positioning is usually performed by hand, involving repeated or even continuous fluoroscopy at a cost of radiation exposure and time. We propose to automate this procedure and estimate the pose update for C-arm repositioning directly from a first X-ray without the need for a patient-specific computed tomography scan (CT) or additional technical equipment. Our method is trained on digitally reconstructed radiographs (DRRs) which uniquely provide ground truth labels for an arbitrary number of training examples. The simulated images are complemented with automatically generated segmentations, landmarks, and with simulated k-wires and screws. To successfully achieve a transfer from simulated to real X-rays, and also to increase the interpretability of results, the pipeline was designed to closely reflect the actual clinical decision-making process followed by spinal neurosurgeons. It explicitly incorporates steps such as region-of-interest (ROI) localization, detection of relevant and view-independent landmarks, and subsequent pose regression. The method was validated on a large human cadaver study simulating a real clinical scenario, including k-wires and screws. The proposed procedure obtained superior C-arm positioning accuracy of dθ=8.8°±4.2° average improvement (pt-test≪0.01), robustness, and generalization capabilities compared to the state-of-the-art direct pose regression framework.

Automatic plane adjustment of orthopedic intraoperative flat panel detector CT-volumes.

Purpose: To assess the result in orthopedic trauma surgery, usually three-dimensional volume data of the treated region is acquired. With mobile C-arm systems, these acquisitions can be performed intraoperatively, reducing the number of required revision surgeries. However, the acquired volumes are typically not aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. To speed up and ease the workflow, an automatic parameterization of these planes is needed. Approach: We present a detailed study of multitask learning (MTL) regression networks to estimate the parameters of the MPR planes. First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, two different MTL network architectures based on the PoseNet are compared with a single task learning network. Results: Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7 deg to 7.3 deg in the mean value for single anatomies. The multihead approach improves the regression of the plane position from 7.4 to 6.1 mm, whereas the orientation does not benefit from this approach. Thus, the achieved accuracy meets the reported interrater variance in similarly complex body regions of up to 6.3 deg for the normals and up to 9.3 mm for the plane position. Conclusions: The use of a multihead approach with shared features leads to more accurate plane regression compared with the use of individual networks for each task. It also improves the angle estimation for the ankle region. The reported results are in the same range as manual plane adjustments. The use of a combined network with shared parameters requires less memory, which is a great benefit for the implementation of an application for the surgical environment.

Computer-assisted contralateral side comparison of the ankle joint using flat panel technology.

PURPOSE: Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation. METHOD: In this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment. RESULTS: Experiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of [Formula: see text] mm and a median angular error between 2.98[Formula: see text] and 3.71[Formula: see text] for the plane normals. CONCLUSION: Standard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery.

Toward automatic C-arm positioning for standard projections in orthopedic surgery.

PURPOSE: Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error prone and involves repeated manual acquisitions or even continuous fluoroscopy. To reduce time and radiation exposure for patients and clinical staff and to avoid errors in fracture reduction or implant placement, we aim at guiding-and in the long-run automating-this procedure. METHODS: In contrast to the state of the art, we tackle this inherently ill-posed problem without requiring patient-individual prior information like preoperative computed tomography (CT) scans, without the need of registration and without requiring additional technical equipment besides the projection images themselves. We propose learning the necessary anatomical hints for efficient C-arm positioning from in silico simulations, leveraging masses of 3D CTs. Specifically, we propose a convolutional neural network regression model that predicts 5 degrees of freedom pose updates directly from a first X-ray image. The method is generalizable to different anatomical regions and standard projections. RESULTS: Quantitative and qualitative validation was performed for two clinical applications involving two highly dissimilar anatomies, namely the lumbar spine and the proximal femur. Starting from one initial projection, the mean absolute pose error to the desired standard pose is iteratively reduced across different anatomy-specific standard projections. Acquisitions of both hip joints on 4 cadavers allowed for an evaluation on clinical data, demonstrating that the approach generalizes without retraining. CONCLUSION: Overall, the results suggest the feasibility of an efficient deep learning-based automated positioning procedure, which is trained on simulations. Our proposed 2-stage approach for C-arm positioning significantly improves accuracy on synthetic images. In addition, we demonstrated that learning based on simulations translates to acceptable performance on real X-rays.

Computer-assisted intra-operative verification of surgical outcome for the treatment of syndesmotic injuries through contralateral side comparison.

PURPOSE: Fracture reduction and fixation of syndesmotic injuries is a common procedure in trauma surgery. An intra-operative evaluation of the surgical outcome is challenging due to high inter-individual anatomical variation. A comparison to the contralateral uninjured ankle would be highly beneficial but would also incur additional radiation and time consumption. In this work, we pioneer automatic contralateral side comparison while avoiding an additional 3D scan. METHODS: We reconstruct an accurate 3D surface of the uninjured ankle joint from three low-dose 2D fluoroscopic projections. Through CNN complemented 3D shape model segmentation, we create a reference model of the injured ankle while addressing the issues of metal artifacts and initialization. Following 2D-3D multiple bone reconstruction, a final reference contour can be created and matched to the uninjured ankle for contralateral side comparison without any user interaction. RESULTS: The accuracy and robustness of individual workflow steps were assessed using 81 C-arm datasets, with 2D and 3D images available for injured and uninjured ankles. Furthermore, the entire workflow was tested on eleven clinical cases. These experiments showed an overall average Hausdorff distance of [Formula: see text] mm measured at clinical evaluation level. CONCLUSION: Reference contours of the contralateral side reconstructed from three projection images can assist surgeons in optimizing reduction results, reducing the duration of radiation exposure and potentially improving postoperative outcomes in the long term.