Layer Separation for Vessel Enhancement in Interventional X-ray Angiograms Using Morphological Filtering and Robust PCA. In: Linte CA, Yaniv Z, Fallavollita P, editors. Augmented Environments for Computer-Assisted Interventions. Cham: Springer International Publishing; 2015. pp. 104-13. [DOI: 10.1007/978-3-319-24601-7_11]

Assessment of cone-beam CT technical image quality indicators and radiation dose for optimal STL model used in visual surgical planning

OBJECTIVES

The aim of this study was to identify cone-beam computed tomography (CBCT) protocols that offer an optimal balance between effective dose (ED) and 3D model for orthognathic virtual surgery planning, using CT as a reference, and to assess whether such protocols can be defined based on technical image quality metrics.

METHODS

Eleven CBCT (VISO G7, Planmeca Oy, Helsinki, Finland) scan protocols were selected out of 32 candidate protocols, based on ED and technical image quality measurements. Next, an anthropomorphic RANDO SK150 phantom was scanned using these 11 CBCT protocols and 2 CT scanners for bone quantity assessments. The resulting DICOM (Digital Imaging and Communications in Medicine) files were converted into Standard Tessellation Language (STL) models that were used for bone volume and area measurements in the predefined orbital region to assess the validity of each CBCT protocol for virtual surgical planning.

RESULTS

The highest CBCT bone volume and area of the STL models were obtained using normal dose protocol (F2) and ultra-low dose protocol (J13), which resulted in 48% and 96% of the mean STL bone volume and 48% and 95% of the bone area measured on CT scanners, respectively.

CONCLUSIONS

The normal dose CBCT protocol "F2" offered optimal bone area and volume balance for STL. The optimal CBCT protocol can be defined using contrast-to-noise ratio and modulation transfer function values that were similar to those of the reference CT scanners'. CBCT scanners with selected protocols can offer a viable alternative to CT scanners for acquiring STL models for virtual surgical planning at a lower effective dose.

Pretrained subtraction and segmentation model for coronary angiograms

This study introduces a novel self-supervised learning method for single-frame subtraction and vessel segmentation in coronary angiography, addressing the scarcity of annotated medical samples in AI applications. We pretrain a U-Net model on a large dataset of unannotated coronary angiograms using an image-to-image translation framework, then fine-tune it on a limited set of manually annotated samples. The pretrained model excels at comprehensive single-frame subtraction, outperforming existing DSA methods. Fine-tuning with just 40 samples yields a Dice coefficient of 0.828 for vessel segmentation. On the public XCAD dataset, our model sets a new state-of-the-art benchmark with a Dice coefficient of 0.755, surpassing both unsupervised and supervised learning approaches. This method achieves robust single-frame subtraction and demonstrates that combining pretraining with minimal fine-tuning enables accurate coronary vessel segmentation with limited manual annotations. We successfully apply this approach to assist physicians in visualizing potential vascular stenosis sites during coronary angiography. Code, dataset, and a live demo will be available available at: https://github.com/newfyu/DeepSA .

Assessment of subjective image quality, contrast to noise ratio and modulation transfer function in the middle ear using a novel full body cone beam computed tomography device

BACKGROUND

Multi slice computed tomography (MSCT) is the most common used method in middle ear imaging. However, MSCT lacks the ability to distinguish the ossicular chain microstructures in detail resulting in poorer diagnostic outcomes. Novel cone beam computed tomography (CBCT) devices' image resolution is, on the other hand, better than MSCT resolution. The aim of this study was to optimize imaging parameters of a novel full body CBCT device to obtain optimal contrast to noise ratio (CNR) with low effective dose, and to optimize its clinical usability.

METHODS

Imaging of five anonymous excised human cadaver temporal bones, the acquisition of the effective doses and the CNR measurements were performed for images acquired on using Planmed XFI® full body CBCT device (Planmed Oy, Helsinki, Finland) with a voxel size of 75 µm. All images acquired from the specimens using 10 different imaging protocols varying from their tube current exposure time product (mAs) and tube voltage (kVp) were analyzed for eight anatomical landmarks and evaluated by three evaluators.

RESULTS

With the exception of protocol with 90 kVp 100 mAs, all other protocols used are competent to image the finest structures. With a moderate effective dose (86.5 µSv), protocol with 90 kV 450 mAs was chosen the best protocol used in this study. A significant correlation between CNR and clinical image quality of the protocols was observed in linear regression model. Using the optimized imaging parameters, we were able to distinguish even the most delicate middle ear structures in 2D images and produce accurate 3D reconstructions.

CONCLUSIONS

In this ex vivo experiment, the new Planmed XFI® full body CBCT device produced excellent 2D resolution and easily created 3D reconstructions in middle ear imaging with moderate effective doses. This device would be suitable for middle ear diagnostics and for e.g., preoperative planning. Furthermore, the results of this study can be used to optimize the effective dose by selecting appropriate exposure parameters depending on the diagnostic task.

Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

Background

Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures.

Methods

A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method.

Results

Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence.

Conclusions

The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms

Effective vessel enhancement in x-ray coronary angiograms (XCA) is essential for the diagnosis of coronary artery disease, yet challenged by complex background structures of varying intensities as well as motion patterns. As a typical layer-separation method, robust principal component analysis (RPCA) has been proposed to automatically improve vessel visibility via sparse and low-rank decomposition. However, the attenuated motion of vessels in x-ray angiograms leads to the unsatisfactory vessel enhancement performance of the decomposition framework. To address this problem, we propose a vesselness-constrained RPCA method (VC-RPCA), where a vessel-like appearance prior is incorporated into the layer separation framework for accurate vessel enhancement. We first pre-compute the vessel-like appearance prior based on a Frangi filter to highlight the curvilinear structures. After removing large-scale background structures via a morphological closing operation, we then integrate the pre-computed vessel-like appearance prior into a low-rank decomposition framework to separate the fine vessel structures. In addition, we develop an adaptive regularization strategy that imposes structured-sparse constraints to solve the scale issue and capture vessels without salient motion. The proposed method was validated on 13 clinical XCA sequences containing 777 images in total. The contrast-to-noise ratio, Dice coefficient and area under the ROC curve were employed for quantitative evaluation of the vessel enhancement performance. Experiments show that (1) the adaptive regularization strategy helps to obtain a complete coronary tree in the separated vessel layer; (2) our low-rank decomposition framework is robust against false positive/negative responses of the Frangi filter; and (3) the proposed VC-RPCA is computationally fast and outperforms other state-of-the-art RPCA methods for vessel enhancement in the full-contrast and low-contrast scenarios. The results demonstrate that the proposed VC-RPCA can accurately separate coronary arteries and prominently improve vessel visibility in x-ray angiograms.