Cardiac magnetic resonance imaging for preprocedural planning of percutaneous left atrial appendage closure

IntroductionPercutaneous closure of the left atrial appendage (LAA) facilitates stroke prevention in patients with atrial fibrillation. Optimal device selection and positioning are often challenging due to highly variable LAA shape and dimension and thus require accurate assessment of the respective anatomy. Transesophageal echocardiography (TEE) and x-ray fluoroscopy (XR) represent the gold standard imaging techniques. However, device underestimation has frequently been observed. Assessment based on 3-dimensional computer tomography (CTA) has been reported as more accurate but increases radiation and contrast agent burden. In this study, the use of non-contrast-enhanced cardiac magnetic resonance imaging (CMR) to support preprocedural planning for LAA closure (LAAc) was investigated.MethodsCMR was performed in thirteen patients prior to LAAc. Based on the 3-dimensional CMR image data, the dimensions of the LAA were quantified and optimal C-arm angulations were determined and compared to periprocedural data. Quantitative figures used for evaluation of the technique comprised the maximum diameter, the diameter derived from perimeter and the area of the landing zone of the LAA.ResultsPerimeter- and area-based diameters derived from preprocedural CMR showed excellent congruency compared to those measured periprocedurally by XR, whereas the respective maximum diameter resulted in significant overestimation (p < 0.05). Compared to TEE assessment, CMR-derived diameters resulted in significantly larger dimensions (p < 0.05). The deviation of the maximum diameter to the diameters measured by XR and TEE correlated well with the ovality of the LAA. C-arm angulations used during the procedures were in agreement with those determined by CMR in case of circular LAA.DiscussionThis small pilot study demonstrates the potential of non-contrast-enhanced CMR to support preprocedural planning of LAAc. Diameter measurements based on LAA area and perimeter correlated well with the actual device selection parameters. CMR-derived determination of landing zones facilitated accurate C-arm angulation for optimal device positioning

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net

Purpose!#!Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches.!##!Methods!#!We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D.!##!Results!#!Automatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm.!##!Conclusions!#!In this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions

3D-XGuide: open-source X-ray navigation guidance system

Purpose!#!With the growing availability and variety of imaging modalities, new methods of intraoperative support have become available for all kinds of interventions. The basic principles of image fusion and image guidance have been widely adopted and are commercialized through a number of platforms. Although multimodal systems have been found to be useful for guiding interventional procedures, they all have their limitations. The integration of more advanced guidance techniques into the product functionality is, however, not easy due to the proprietary solutions of the vendors. Therefore, the purpose of this work is to introduce a software system for image fusion, real-time navigation, and working points documentation during transcatheter interventions performed under X-ray (XR) guidance.!##!Methods!#!An interactive software system for cross-modal registration and image fusion of XR fluoroscopy with CT or MRI-derived anatomic 3D models is implemented using Qt application framework and VTK visualization pipeline. DICOM data can be imported in retrospective mode. Live XR data input is realized by a video capture card application interface.!##!Results!#!The actual software release offers a graphical user interface with basic functionality including data import and handling, calculation of projection geometry and transformations between related coordinate systems, rigid 3D-3D registration, and template matching-based tracking and motion compensation algorithms in 2D and 3D. The link to the actual software release on GitHub including source code and executable is provided to support independent research and development in the field of intervention guidance.!##!Conclusion!#!The introduced system provides a common foundation for the rapid prototyping of new approaches in the field of XR fluoroscopic guidance. As a pure software solution, the developed system is potentially vendor-independent and can be easily extended to be used with the XR systems of different manufacturers

Fast diffusion tensor magnetic resonance imaging of the mouse brain at ultrahigh-field: aiming at cohort studies.

INTRODUCTION: In-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain is often limited by the low signal to noise ratio (SNR) resulting from the required small voxel sizes. Recently, cryogenically cooled resonators (CCR) have demonstrated significant increase of the effective SNR. It is the objective of this study to enable fast DTI of the mouse brain. In this context, CCRs appear attractive for SNR improvement. METHODS: Three mice underwent a DTI examination at 156²×250 µm³ spatial resolution with a CCR at ultrahigh field (11.7T). Diffusion images were acquired along 30 gradient directions plus 5 references without diffusion encoding, resulting in a total acquisition time of 35 minutes. For comparison, mice additionally underwent a standardized 110 minutes acquisition protocol published earlier. Fractional anisotropy (FA) and fiber tracking (FT) results including quantitative tractwise fractional anisotropy statistics (TFAS) were qualitatively and quantitatively compared. RESULTS: Qualitative and quantitative assessment of the calculated fractional anisotropy maps and fibre tracking results showed coinciding outcome comparing 35 minute scans to the standardized 110 minute scan. Coefficients of variation for ROI-based FA-comparison as well as for TFAS revealed comparable results for the different scanning protocols. CONCLUSION: Mouse DTI at 11.7 T was performed with an acquisition time of approximately 30 minutes, which is considered feasible for cohort studies. The rapid acquisition protocol reveals reliable and reproducible FA-values and FT reconstructions, thus allowing an experimental setup for in-vivo large scale whole brain murine DTI cohort studies

Statistical analysis.

<p><b>Left panel</b> – ROI analysis: FA-values (standard deviation as error bars) for different mice with different scanning protocols (SP) (c<i>olors red, green, blue represent scanning protocols A,B,C, respectively)</i> and coefficients of variance (CV) averaged for 3 different ROIs in 3 different mice for the 3 scanning protocols. <b>Right panel</b> – TFAS: FA-values (standard deviation as error bars) for different mice with different scanning protocols (SP) (c<i>olors red, green, blue represent scanning protocols A,B,C, respectively)</i> and coefficients of variance (CV) averaged for 3 different TFAS in 3 different mice for the 3 scanning protocols.</p

FT results for seed points in the genu and along the corpus callosum, near the lateral septal nucleus, and in the olfactory path.

<p><b>Left column:</b> Location of the seed points. <b>Columns 2–4:</b> FT for mouse 1 with different scanning protocols (SP). <b>Columns 5–6:</b> FT for mice 2 and 3, respectively, with SP A (35 minute scan).</p