Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance

Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus

BACKGROUND AND OBJECTIVE

The cone-beam computed tomography (CBCT) provides three-dimensional volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. We introduce the development of AI-based computer-aided diagnosis for CBCT considering the intrinsic imaging noise and evaluate its efficacy and implications.

METHODS

We propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The proposed method is model agnostic and compatible with various existing and future AI-based denoising or diagnosis models.

RESULTS

The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average area under the curve, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. In addition, the physician's ability to evaluate the AI-derived diagnosis results may be enhanced compared with existing solutions.

CONCLUSION

This pioneering study on AI-based diagnosis using CBCT indicates that denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions. Furthermore, we believe that the performance enhancement will expedite the adoption of automated diagnostic solutions using CBCT, especially in locations with a shortage of skilled clinicians and limited access to high-dose scanning.

Advancements in total knee arthroplasty kinematics: 3D implant computer aided design model creation through X-ray or fluoroscopic images

BACKGROUND

3D-to-2D fluoroscopic registration is a popular and important step for analyzing total-knee-arthroplasty weight-bearing kinematics. Unfortunately, in vivo analyses using these techniques cannot be completed if the associated computer-aided design implant models are not available. This study introduces a novel method that enables the accessible computation of knee replacement patients' kinematics from fluoroscopy, achieved through the reconstruction of 3-dimensional knee component models using a limited set of 2-dimensional X-ray or fluoroscopic images.

METHODS

The proposed non-rigid morphing algorithm, based on the coherent point drift algorithm, scales and transforms the shape of the template model to fit with the silhouette of the corresponding fluoroscopic images without changing the structure of the knee implant. While a greater number of fluoroscopic images can lead to higher accuracy, our study utilizes only 4 images.

FINDINGS

The morphed models show excellent results in comparison with known models with a 0.52 mm average root-mean-square error and a 2.82 mm largest source error for 17 tested knee models of various implant types. The proposed algorithm also enables direct output of patient kinematics using fluoroscopy, with an average error of only 0.54 ± 0.42 mm for femorotibial contact and 0.86 ± 0.34 degrees for axial rotation.

INTERPRETATION

A novel methodology was introduced to overcome common 3-dimentional to 2-dimensional registration limitations by recreating entire families of 3 dimensional models from a limited number of fluoroscopic images for both cruciate-retaining and posterior-stabilized knee replacement implants. Our algorithm has demonstrated high levels of accuracy with multiple potential extended applications.

Real-time 3D reconstruction of guidewires and stents using two update X-ray projections in a rotating imaging setup

BACKGROUND

Vascular diseases are often treated minimally invasively. The interventional material (stents, guidewires, etc.) used during such percutaneous interventions are visualized by some form of image guidance. Today, this image guidance is usually provided by 2D X-ray fluoroscopy, that is, a live 2D image. 3D X-ray fluoroscopy, that is, a live 3D image, could accelerate existing and enable new interventions. However, existing algorithms for the 3D reconstruction of interventional material require either too many X-ray projections and therefore dose, or are only capable of reconstructing single, curvilinear structures.

PURPOSE

Using only two new X-ray projections per 3D reconstruction, we aim to reconstruct more complex arrangements of interventional material than was previously possible.

METHODS

This is achieved by improving a previously presented deep learning-based reconstruction pipeline, which assumes that the X-ray images are acquired by a continuously rotating biplane system, in two ways: (a) separation of the reconstruction of different object types, (b) motion compensation using spatial transformer networks.

RESULTS

Our pipeline achieves submillimeter accuracy on measured data of a stent and two guidewires inside an anthropomorphic phantom with respiratory motion. In an ablation study, we find that the aforementioned algorithmic changes improve our two figures of merit by 75 % (1.76 mm → 0.44 mm) and 59 % (1.15 mm → 0.47 mm) respectively. A comparison of our measured dose area product (DAP) rate to DAP rates of 2D fluoroscopy indicates a roughly similar dose burden.

CONCLUSIONS

This dose efficiency combined with the ability to reconstruct complex arrangements of interventional material makes the presented algorithm a promising candidate to enable 3D fluoroscopy.

The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging

Background

Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment.

Purpose

To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR).

Material and Methods

A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared.

Results

Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively ( P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence ( P = 0.001).

Conclusion

The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR.