Today, the subcutaneous, minimally invasive procedures performed in interventional radiology are usually guided by 2D X-ray fluoroscopy. In 2D X-ray fluoroscopy a series of 2D X-ray images is displayed. For challenging procedures however, 3D X-ray fluoroscopy would be advantageous. In 3D X-ray fluoroscopy, a series of 3D images, which is reconstructed from a series of 2D X-ray images, is displayed. Because the number of images used for guiding an intervention is very high, little dose can be spent per 3D reconstruction of a 3D fluoroscopy. To save dose and to minimize motion artifacts, a reconstruction algorithm that requires very few X-ray projections is desirable. Earlier work showed that guidewires, stents and coils, which are commonly used in interventions, can be reconstructed using only four synthetic X-ray projections. The reconstruction from two or three X-ray projections was only studied briefly. In this work, we improve the method by using a more suitable neural network architecture and by using a multi-channel backprojection instead of a single-channel backprojection. We then apply the improved method to more realistic data measured in an anthropomorphic phantom. The results show that the method produces 3D reconstructions of stents and guidewires with submillimeter accuracy using only three measured X-ray projections.
Today, 2D+T fluoroscopy is usually used for image guidance in interventional radiology. For challenging procedures, 4D (3D+T) image guidance would be advantageous. The difficulty in realizing X-ray-based 4D interventional guidance lies in the development of a very dose efficient reconstruction algorithm. To this end, we improve on a previously presented algorithm for the reconstruction of interventional tools. By incorporating temporal information into a 3D convolutional neural network, we reduce the number of X-ray projections that need to be acquired for the 3D reconstruction of guidewires from four to two, thereby halving dose and decreasing the demands put on imaging devices implementing the algorithm. In experiments with two moving guidewires in an anthropomorphic phantom, we observe little deviation of our 3D reconstructions from the ground truth.
Interventional guidance aims at providing the radiologist with detailed information about the location and orientation of interventional tools such as guide wires and stents. Most commonly, this is done by acquiring fluoroscopic images using an interventional C-arm system. Due to its projective nature, fluoroscopy is restricted to provide information from two spatial dimensions, preventing an exact 3D localization of the interventional tools. Analogous to computed tomography for diagnostic imaging, four-dimensional (three spatial dimensions plus the temporal dimension) interventional guidance has the potential to drastically improve both the speed and accuracy of such interventions, but is currently impractical due to the excessively high dose that would be necessary for continuous cone-beam CT (CBCT) scanning at high frame rates.
In this work we develop a novel deep learning-based approach to reconstruct interventional tools from only four x-ray projections. We train and test this deep tool reconstruction (DTR) network on simulated data. Only small deviations from the ground truth (GT) reconstruction of the tools were observed, both quantitatively and qualitatively, showing that deep learning-based four-dimensional interventional guidance has the potential to overcome the drawbacks of conventional interventional guidance in the future.
Digital Subtraction Angiography (DSA) aims at selectively displaying vessels by subtracting an unenhanced mask image from a contrast-enhanced fluoroscopic image. This strategy requires the data to be static, i.e. to be acquired without patient or C-arm motion. Thus, conventional DSA cannot be applied to dynamic acquisition protocols such as bolus injection chases, which are particularly useful for the diagnosis of peripheral arterial disease (PAD). Preliminary studies have shown that convolutional neural networks (CNNs) are capable of overcoming this drawback, by predicting DSA-like images directly from their corresponding fluoroscopic x-ray images without the need for the acquisition of a mask image. Here, we demonstrate the potential of this approach for fluoroscopic acquisitions of the lower extremities. We apply the network to twelve different patient exams of which nine are without C-arm motion and the remaining three are bolus chase studies with C-arm motion. For cases where a conventional DSA is feasible we examine very small deviations and observe predictions for the bolus chase studies of similar visual impression as with conventional DSA. The results indicate that Deep DSA has the potential to improve the diagnosis of PAD by generating DSA-equivalent images from bolus chase studies of the lower extremities.
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