查看更多>>摘要：Abstract Purpose Computational fluid dynamics (CFD)-based calculation of intranasal airflow became an important method in rhinologic research. Current evidence shows weak to moderate correlation as well as a systematic underprediction of nasal resistance by numerical simulations. In this study, we investigate whether these differences can be explained by measurement uncertainties caused by rhinomanometric devices and procedures. Furthermore, preliminary findings regarding the impact of tissue movements are reported.Methods A retrospective sample of 17 patients, who reported impaired nasal breathing and for which rhinomanometric (RMM) measurements using two different devices as well as computed tomography scans were available, was investigated in this study. Three patients also exhibited a marked collapse of the nasal valve. Agreement between both rhinomanometric measurements as well as between rhinomanometry and CFD-based calculations was assessed using linear correlation and Bland–Altman analyses. These analyses were performed for the volume flow rates measured at trans-nasal pressure differences of 75 and 150?Pa during inspiration and expiration.Results The correlation between volume flow rates measured using both RMM devices was good (R2?>?0.72 for all breathing states), and no relevant differences in measured flow rates was observed (21.6?ml/s and 14.8?ml/s for 75 and 150?Pa, respectively). In contrast, correlation between RMM and CFD was poor (R2?<?0.5) and CFD systematically overpredicted RMM-based flow rate measurements (231.8?ml/s and 328.3?ml/s). No differences between patients with and without nasal valve collapse nor between inspiration and expiration were observed.Conclusion Biases introduced during RMM measurements, by either the chosen device, the operator or other aspects as for example the nasal cycle, are not strong enough to explain the gross differences commonly reported between RMM- and CFD-based measurement of nasal resistance. Additionally, tissue movement during breathing is most likely also no sufficient explanation for these differences.
查看更多>>摘要：Abstract Purpose Anterior and posterior decompressions for cervical myelopathy and radiculopathy may lead to clinical improvements. However, patients with kyphotic cervical alignment have sometimes shown poor clinical outcomes with posterior decompression. There is a lack on report of mechanical analysis of the decompression procedures for kyphotic cervical alignment. Methods This study employed a three-dimensional finite element (FE) model of the cervical spine (C2-C7) with the pre-operative kyphotic alignment (Pre-OK) model and compared the biomechanical parameters (range of motion (ROM), annular stresses, nucleus stresses, and facet contact forces) for four decompression procedures at two levels (C3-C5); laminectomy (LN), laminoplasty (LP), posterior decompression with fusion (PDF), and anterior decompression with fusion (ADF). Pure moment with compressive follower load was applied to these models.Results PDF and ADF models’ global ROM were 40% at C2-C7 less than the Pre-OK, LN, and LP models. The annular and nucleus stresses decreased more than 10% at the surgery levels for ADF, and PDF, compared to the Pre-OK, LN, and LP models. However, the annular stresses at the adjacent cranial level (C2-C3) of ADF were 20% higher. The nucleus stresses of the caudal adjacent level (C5-C6) of PDF were 20% higher, compared to other models. The PDF and ADF models showed a less than 70% decrease in the facet forces at the surgery levels, compared to the Pre-OK, LN, and LP models.Conclusion The study concluded that posterior decompression, such as LN or LP, increases ROM, disc stress, and facet force and thus can lead to instability. Although there is the risk of adjacent segment disease (ASD), PDF and ADF can stabilize the cervical spine even for kyphotic alignments.
查看更多>>摘要：Abstract Purpose We present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.Methods Two three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.Results Experiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34?±?0.45?mm, 2.25?±?0.72?mm, 2.71?±?0.82?mm, and 2.50?±?1.11?mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49?±?1.88?mm and 5.02?±?2.03?mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96?±?2.05?mm and 2.97?±?1.37?mm at 50% and 25%, respectively.Conclusion Experimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.
查看更多>>摘要：Abstract Purpose Most cardiology procedures are guided using X-ray (XR) fluoroscopy. However, the projective nature of the XR fluoroscopy does not allow for true depth perception as required for safe and efficient intervention guidance in structural heart diseases. For improving guidance, different methods have been proposed often being radiation-intensive, time-consuming, or expensive. We propose a simple 3D localization method based on a single monoplane XR projection using a co-registered centerline model.Methods The method is based on 3D anatomic surface models and corresponding centerlines generated from preprocedural imaging. After initial co-registration, 2D working points identified in monoplane XR projections are localized in 3D by minimizing the angle between the projection lines of the centerline points and the working points. The accuracy and reliability of the located 3D positions were assessed in 3D using phantom data and in patient data projected to 2D obtained during placement of embolic protection system in interventional procedures.Results With the proposed methods, 2D working points identified in monoplane XR could be successfully located in the 3D phantom and in the patient-specific 3D anatomy. Accuracy in the phantom (3D) resulted in 1.6?mm (±?0.8?mm) on average, and 2.7?mm (±?1.3?mm) on average in the patient data (2D).Conclusion The use of co-registered centerline models allows reliable and accurate 3D localization of devices from a single monoplane XR projection during placement of the embolic protection system in TAVR. The extension to different vascular interventions and combination with automatic methods for device detection and registration might be promising.
查看更多>>摘要：Abstract Purpose: Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts near a neurosurgical target after preoperative images have been invalidated by brain shift. We propose an atlas-based intraoperative tract segmentation method, as the standard preoperative method, streamline tractography, is unsuitable for intraoperative implementation.Methods: A tract-specific voxel-wise fibre orientation atlas is constructed from healthy training data. After registration with a target image, a radial tumour deformation model is applied to the orientation atlas to account for displacement caused by lesions. The final tract map is obtained from the inner product of the atlas and target image fibre orientation data derived from intraoperative diffusion MRI.Results: The simple tumour model takes only seconds to effectively deform the atlas into alignment with the target image. With minimal processing time and operator effort, maps of surgically relevant tracts can be achieved that are visually and qualitatively comparable with results obtained from streamline tractography.Conclusion: Preliminary results demonstrate feasibility of intraoperative streamline-free tract segmentation in challenging neurosurgical cases. Demonstrated results in a small number of representative sample subjects are realistic despite the simplicity of the tumour deformation model employed. Following this proof of concept, future studies will focus on achieving robustness in a wide range of tumour types and clinical scenarios, as well as quantitative validation of segmentations.
Hareem,NisarDjalal,FakimDaniel,BainbridgeElvis C. S.,Chen...
9页
查看更多>>摘要：Abstract Purpose Tricuspid valve (TV) interventions face the challenge of imaging the anatomy and tools because of the ‘TEE-unfriendly’ nature of the TV. In edge-to-edge TV repair, a core step is to position the clip perpendicular to the coaptation gap. In this study, we provide a semi-automated method to localize the VC from Doppler intracardiac echo (ICE) imaging in a tracked 3D space, thus providing a pre-mapped location of the coaptation gap to assist device positioning.Methods A magnetically tracked ICE probe with Doppler imaging capabilities is employed in this study for imaging three patient-specific TVs placed in a pulsatile heart phantom. For each of the valves, the ICE probe is positioned to image the maximum regurgitant flow for five cardiac cycles. An algorithm then extracts the regurgitation imaging and computes the exact location of the vena contracta on the image.Results Across the three pathological, patient-specific valves, the average distance error between the detected VC and the ground truth model is (1.22±2.00)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({1.22 \pm 2.00})$$\end{document}mm. For each of the valves, one case represented the outlier where the algorithm misidentified the vena contracta to be near the annulus. In such cases, it is recommended to retake the five-second imaging data.Conclusion This study presented a method for ultrasound-based localization of vena contracta in 3D space. Mapping such anatomical landmarks has the potential to assist with device positioning and to simplify tricuspid valve interventions by providing more contextual information to the interventionalists, thus enhancing their spatial awareness. Additionally, ICE can be used to provide live US and Doppler imaging of the complex TV anatomy throughout the procedure.
查看更多>>摘要：Abstract Purpose Commercial medical ultrasound phantoms are highly specific as they simulate particular clinical scenarios. This makes them expensive to use in multi-target research and training. General approaches to human tissue and organ modeling are described in the manufacturing methodology, access to which is restricted by the manufacturer's trade secret. Our aim is to propose a reproducible methodology to design a head phantom for transcranial ultrasound training and research from widely available materials and to validate its applicability.Methods To create an anthropomorphic phantom, we used data from real patients obtained by CT and MRI scans. We combined FDM and LCD 3D printing to achieve the desired acoustic performance and ergonomics of the phantom. We fabricated the phantom using polyvinyl chloride plastisol, photopolymer, and PLA to simulate brain tissue, temporal acoustic windows, and acoustically opaque parts of the skull, respectively. Notably, the phantom fabrication method uses only readily available materials and is easy to reproduce.Results We developed a basic one and anatomical one versions of the head phantom. The basic version contains a simplified brain: tissue-mimicking material is poured into the skull with needles inserted, which specific pattern is easy to recognize in B-mode images. The anatomical version has an anatomically correct brain dummy extracted from MRI data and contains multiple randomly distributed small metal, plastic, and bony objects ranging in size from 1 to 3?mm each.Conclusion The proposed methodology allows producing head phantoms for transcranial ultrasound training and research. The anatomical accuracy of the model is proved by ultrasonography and CT studies. Both versions of the phantom comprise the skull and the brain and are intended for ultrasound imaging through the temporal bone acoustic window. Needles and small objects serve as navigation targets during the training procedure. The basic version helps learning basic navigation skills, while the anatomical one provides a realistic setting to perform the diagnostic procedure.
查看更多>>摘要：Abstract Purpose During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators.Methods Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated.Results Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results.Conclusion This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.
NSTL
Springer Nature