Vessel Enhancement Filter Research Articles

Aethra-net: Single image and video dehazing using autoencoder

A fast and efficient video dehazing system with low computational complexity has a huge demand among drivers during hazy winter nights. There are only a few video dehazing models that exist in literature. Video dehazing requires the sequential extraction and processing of frames. The processed frames must be restored in the same sequence as the original video. However, the existing video dehazing algorithms suffer from color distortion due to the continuous processing of frames. They are not suitable for videos with dense haze. Furthermore, some dehazing systems require hardware, whereas the proposed model is completely software-based to reduce the computational costs. In this paper, an image and video dehazing system called Aethra-Net is developed. A gush enhancer-based autoencoder is modified to obtain the transmission map. The structure of gush enhancement module resembles the processing of light entering the human eye from different paths. The multiple blocks of Resnet-101 layers are employed to overcome vanishing gradient problem. The vessel enhancement filter is also incorporated to enhance the performance of the proposed system. The proposed model has a susceptibility to compute the dehazed images effectively. The proposed model is evaluated on various benchmark datasets and compared with the existing dehazing techniques. Experimental results reveal that the performance of Aethra-Net is found superior as compared to the existing dehazing models.

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules.

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

A Benchmark Framework for Multiregion Analysis of Vesselness Filters.

Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes up-to-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters.

A Tensor-Based Catheter and Wire Detection and Tracking Framework and Its Clinical Applications.

Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. A novel and fully automatic detection framework and its clinical applications are developed. The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.

Robust deep 3-D architectures based on vascular patterns for liver vessel segmentation

Deep Learning is providing new solutions for medical image segmentation problems and becomes a key point for future clinical application. The study of vascular structures is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data.In this study, we present an end-to-end deep learning segmentation method relying on the integration of vessel enhancement filters inside a 3-D U-Net based architecture. In particular, the raw data used in the learning process -or used as input- is preprocessed using these filters. The use of vesselness filters can significantly improve the contrast in raw images; this step can also help to improve segmentation decision especially on bifurcations. 3-D U-Net, Dense U-Net and MultiRes U-Net are pitted against each other in the vessel segmentation task with the public IRCAD dataset. Considering the integration of vesselness filters, the model parameters were optimized in order to identify the optimal configuration for fully automatic segmentation of hepatic vessels. In addition the three architectures were tested on full 3-D images and slabs (stacks of 2-D slices). The results showed that the most accurate setup is the full 3-D process, which provides the highest Dice for most of the considered models. The 3-D Dense U-Net also gives the best results compared to other models with or without vessel enhancement filters.

3-D Super-Resolution Ultrasound Imaging for Monitoring Early Changes in Breast Cancer after Treatment with a Vascular-Disrupting Agent.

The purpose of this research project was to evaluate the use of 3-dimensional (3-D) super-resolution ultrasound (SR-US) imaging to assess any early changes in breast cancer after treatment with a vascular-disrupting agent (VDA). A Vevo 3100 ultrasound system (FUJIFILM VisualSonics Inc) equipped with an MX 201 transducer was used for image acquisition. A total of 2.5 × 107 microbubbles (MBs) were injected into the tail vein of anesthetized breast cancer-bearing mice using repeat bolus injections every 5 min. A total of 10 stacks of ultrasound images were collected as the transducer was mechanically moved across the tumor at 0.6 mm intervals yielding a 6-mm thick volume. At each tumor location, a stack contained 1 × 104 frames of ultrasound data that were acquired at 463 frames/sec and stored as in-phase/quadrature (IQ) format. After motion correction, each temporal stack of ultrasound images was processed separately for clutter signal removal, which was followed by MB localization and enumeration before generation of the final SR-US image. After reconstruction of the 3-D SR-US volume dataset, the tumor microvasculature was enhanced using a multiscale vessel enhancement filter. Vessels from the resultant microvascular network were then segmented using an adaptive thresholding method. Finally, mean microvascular density (MVD) measurements from each tumor volume were computed as a summarizing statistic. While no differences were found between baseline SR-US image-derived measures of MVD (p = 0.76), these same measurements were significantly lower at 24 h after VDA treatment (p < 0.001). Overall, 3-D SR-US imaging detected early tumor changes following treatment with a vascular-targeted drug.

Effect of vessel enhancement filters on the repeatability of measurements obtained from widefield swept-source optical coherence tomography angiography

We assessed the inter-visit repeatability of 15 × 9-mm2 swept-source OCTA (SS-OCTA; PLEX Elite 9000, Carl Zeiss Meditec) metrics in 14 healthy participants. We analysed the perfusion density (PD) of large vessels, superficial capillary plexus (SCP), and deep capillary plexus (DCP) as well as choriocapillaris flow voids in 2 different regions: the macular region and peripheral region. Also, retinal plexus metrics were processed further using different filters (Hessian, Gabor and Bayesian) while choriocapillaris flow voids were calculated with 1 and 1.25 standard deviation (SD) thresholding algorithms. We found excellent repeatability in the perfusion densities of large vessels (ICC > 0.96). Perfusion densities varied with different filters in the macular region (SCP: 24.12–38.57% and DCP: 25.16–38.50%) and peripheral (SCP: 30.52–39.84% and DCP: 34.19–41.60%) regions. The ICCs were lower in the macular region compared to the peripheral region and lower for DCP than for SCP. For choriocapillaris flow voids, the 1.25 SD threshold resulted in fewer flow voids, while a good ICC (ICC > 0.81) was achieved using either threshold settings for flow void features in both regions. Our results suggest good repeatability of widefield SS-OCTA for the measurements of retinal perfusion density and choriocapillaris flow voids, but measurements from different filters should not be interchanged.

Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T.

The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging. High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B0 perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases. Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization. Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.

Intra-session repeatability of quantitative metrics using widefield optical coherence tomography angiography (OCTA) in elderly subjects.

PurposeTo assess the repeatability of retinal vascular metrics using different postprocessing methods as obtained from the swept‐source optical coherence tomography angiography (SS‐OCTA).MethodsThirty‐two participants (63% males; mean [SD] age, 70 [7] years) underwent SS‐OCTA imaging (PLEX ® Elite 9000, Carl Zeiss Meditec, Inc., Dublin, USA). Each participant underwent 2 repeated scans of 2 scan protocols: a macular‐centred 3 × 3‐mm2 and a widefield 12 × 12‐mm2 for a total of 4 acquisitions. Images of superficial vascular plexuses (SVP) and deep vascular plexuses (DVP) were processed using different filters to generate the perfusion density (PD) and vessel density (VD). Vessel enhancement filters ranged from vessel targeted (Hessian and Gabor filters), classical denoising (Gaussian filter), to a scale‐selective adaption (modified Bayesian residual transform [MBRT]). Intra‐session repeatability of the different filters and their correlation with the original data set were calculated with the intraclass correlation coefficient (ICC) and Pearson's r.ResultsOf the 32 eyes, 17 and 15 were right and left eyes, respectively. For 3 × 3‐mm2 scans, both MBRT and Gabor filters yielded very good repeatable PD and VD (both ICCs > 0.87) values. Gabor filter was the most correlated with the original data set for the OCTA metrics (r = 0.95–0.97). For 12 × 12‐mm2 scans, MBRT filter produced good‐to‐moderate ICC values for SVP (ICC>0.89) and DVP (ICC>0.73) metrics. Both the MBRT and Gabor filters were highly correlated with the original 12 × 12‐mm2 scan data set (r = 0.96–0.98). The ICCs for the agreement between 3 × 3‐mm2 and cropped 12 × 12‐mm2 were high only for the PD values at the SVP layer and were poor for the VD at SVP and DVP measurements (ICC < 0.50).ConclusionOur findings show that with the proper choice of postimaging processing methods, SS‐OCTA metrics can be obtained with high repeatability, which supports its use in various clinical settings.

Multiscale quantification of tumor microarchitecture for predicting therapy response using dynamic contrast-enhanced ultrasound imaging.

Hepatocellular carcinoma (HCC) is the most common liver cancer with 1 million cases globally. A current clinical challenge is to determine which patients will respond to transarterial chemoembolization (TACE) as effective delivery of the embolic material may be influenced by the tumor vascular supply. The purpose of this study is to develop a novel image processing algorithm for improved quantification of tumor microvascular morphology features using contrast-enhanced ultrasound (CEUS) images and to predict the TACE response based on these biomarkers before treatment. A temporal sequence of CEUS images was corrected from rigid and non-rigid motion artifacts using affine and free form deformation models. Subsequently, a principal component analysis based singular value filter was applied to remove the clutter signal from each frame. A maximum intensity projection was created from high-resolution images. A multiscale vessel enhancement filter was first utilized to enhance the tubular structures as a preprocessing step before segmentation. Morphological image processing methods are used to extract the morphology features, namely, number of vessels (NV) and branching points (NB), vessel-to-tissue ratio (VR), and the mean vessel length (VL), tortuosity (VT), and diameter (VD) from the tumor vascular network. Finally, a support vector machine (SVM) is trained and validated using leave-one-out cross-validation technique. The proposed image analysis strategy was able to predict the patient outcome with 90% accuracy when the SVM was trained with the three features together (NB, NV, VR). Experimental results indicated that morphological features of tumor microvascular networks may be significant predictors for TACE response. Reliable prediction of the TACE therapy response may help provide effective therapy planning.

Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning

Background and objectiveStereotactical procedures require exact trajectory planning to avoid blood vessels in the trajectory path. Innovation in imaging and image recognition techniques have facilitated the automatic detection of blood vessels during the planning process and may improve patient safety in the future.To assess the feasibility of a vessel detection and warning system using currently available imaging and vessel segmentation techniques. MethodsImage data were acquired from post-contrast, isovolumetric T1-weighted sequences (T1CE) and time.-of-flight MR angiography at 3T or 7T from a total of nine subjects. Vessel segmentation by a combination of a vessel-enhancement filter with subsequent level-set segmentation was evaluated using three different methods (Vesselness, FastMarching and LevelSet) in 45 stereotactic trajectories. Segmentation results were compared to a gold-standard of manual segmentation performed jointly by two human experts. ResultsThe LevelSet method performed best with a mean interclass correlation coefficient (ICC) of 0.76 [0.73, 0.81] compared to the FastMarching method with ICC 0.70 [0.67, 0.73] respectively. The Vesselness algorithm achieved clearly inferior overall performance with a mean ICC of 0.56 [0.53, 0.59]. The differences in mean ICC between all segmentation methods were statistically significant (p < 0.001 with post-hoc p < 0.026). The LevelSet method performed likewise good in MPRAGE and 3T-TOF images and excellent in 7T-TOF image data. The negative predictive value (NPV) was very high (>97%) for all methods and modalities. Positive predictive values (PPV) were found in the overall range of 65–90% likewise depending on algorithm and modality. This pattern reflects the disposition of all segmentation methods – in case of misclassification - to produce preferentially false-positive than false-negative results. In a clinical setting, two to three potential collision warnings would be given per trajectory on average with a PPV of around 50%. ConclusionsIt is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Both, T1CE and MRA sequences are suitable as image data for such an application.

Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing.

Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy. To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing. First, the airway wall from the lung lobes is eliminated from CT images by using multi-scale morphological operations. Second, a Hessian-based multi-scale vesselness filter and medialness filter are applied to detect and enhance the potential vessel. Third, an anisotropic diffusion filter is used to remove noise and enhance the tube-like structures in CT images. Last, the vascular tree is segmented by applying variational region growing algorithm. Applying to the CT images collected from the entire dataset of VESSEL12 challenge, we achieved an average sensitivity of 92.9%, specificity of 91.6% and the area under the ROC curve of AUC = 0.972. This study demonstrated feasibility of segmenting the pulmonary vessel effectively by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing algorithm. Our method cannot only segment both large and peripheral vessels, but also distinguish the vessels from the adjacent tissues, especially the airway walls.

Assessment of Brain Venous Structure in Military Traumatic Brain Injury Patients using Susceptibility Weighted Imaging and Quantitative Susceptibility Mapping.

Brain venous volume above the lateral ventricle in military patients with traumatic brain injury (TBI) was assessed using two segmentation approaches on susceptibility weighted images (SWI) and quantitative susceptibility maps (QSM). This retrospective study included a total of 147 subjects: 14 patients with severe TBI; 38 patients with moderate TBI, 58 patients with mild TBI (28 with blast-related injuries and 30 with non-blast-related injuries), and 37 control subjects without history of TBI. Using the multiscale vessel enhancement filter on SWI images, patients with severe TBI demonstrated significantly higher segmented venous volumes compared with controls. Using a threshold approach on QSM images, TBI patients with different severities all demonstrated increased segmented volumes compared with control subjects: in the whole brain (severe, p = 0.001; moderate, p = 0.008; mild, p = 0.042, compared with controls), in the left hemisphere (severe, p = 0.01; moderate, p = 0.038, compared with controls), in the right hemisphere (severe, p = 0.001; moderate, p = 0.013; mild, p = 0.027, compared with controls). While segmented volumes on SWI appear to overlay directly on the visualized venous structures, the QSM-derived segments also encompass some perivascular and deep white matter areas. This might represent the damage in the perivascular regions associated with iron deposition or astroglial scarring.

Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks.

Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemic-euglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above 150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.

Accurate Extraction of Coronary Vascular Structures in 2D X-ray Angiogram Using Vascular Topology Information in 3D Computed Tomography Angiography

Since the 2D X-ray angiogram enables the detection of vascular stenosis in real-time, it is essential for percutaneous coronary intervention (PCI). However, the accurate vascular structure is very difficult to determine due to background clutter and loss of depth information of 2D projection. To cope with these difficulties, we propose a fast and accurate extraction method of a vascular structure in 2D X-ray angiogram (XA) based on the vascular topology information of 3D computed tomography angiography (CTA) of the same patient. First, an initial vascular structure is robustly extracted based on vessel enhancement filtering. Then, 2D XA and 3D CTA are spatially aligned by 2D–3D registration. Finally, the 2D vascular structure is accurately reconstructed based on 3D vascular topology information by measuring the similarity of 2D and 3D vascular segments. Experimental results showed the fast and accurate extraction of a vascular structure using 10 images each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a computer-aided technology for PCI. Our method can be applied to the study of other various types of vessels.

Segmentation Guided Registration for 3D Spectral-Domain Optical Coherence Tomography Images

Medical image registration can be used for combining information from multiple imaging modalities, monitoring changes in size, shape or image intensity over time intervals. However, the development of such technique can be challenging for 3D spectral-domain optical coherence tomography (SD-OCT) imaging, because SD-OCT image is inherently noisy and its high resolution leads to high complexity of non-rigid registration. In this paper, a new segmentation guided approach is reported for registration of retinal OCT data. The proposed method models the 3D registration as a two-stage registration including x-y direction registration and z direction registration. In x-y direction registration, the vessel maps of OCT projection images between the template and the subject are registered to find out x-y direction displacement. The multi-scale vessel enhancement filter and morphological thinning methods are used to extract the vessel maps from the projection image of 3D OCT scans. And then x-y direction displacement is estimated by matching Speeded-Up Robust Features of the vessel maps. In z direction registration, using the tissue map instead of the original intensity image, A-scans are aligned to get the local displacements in z direction. The proposed method was evaluated on 45 longitudinal retinal OCT scans from 15 subjects. Experimental results show that the proposed method is accurate and very efficient.

A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures.

PurposeCatheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X‐ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction.MethodsFor the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire‐like structures in the X‐ray images. After applying adaptive binarization method, the centerlines of wire‐like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire‐like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire‐like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases.ResultsDetection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%.ConclusionThe proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real‐time. The accuracy of the proposed framework is sub‐mm and the methods are robust toward low‐dose X‐ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose.

Quantitative morphological analysis of microvasculature in thyroid nodules using non-contrast high-resolution Doppler imaging

We hypothesize that pattern of microvasculature in the thyroid nodules can be a biomarker of thyroid cancer. In this study, morphological features of microvasculature networks are assessed as potential biomarkers for differentiation of benign, suspicious and malignant thyroid nodules. This prospective study included n = 81 patients, with a total of 61 benign, 12 suspicious and 8 malignant nodules verfied by pathology and cytology findings. Thyroid nodules were first identified using conventional B-mode ultrasound. A sequence of raw ultrasound data were acquired from each nodule site in two perpendicular planes at ~600 frames/sec. Power Doppler image of the microvasculature was formed using singular value decomposition clutter removal filtering. Gross images were further processed using a combination of morphology and Hessian-based vessel enhancement filtering. Background-free images of the microvasculature networks were segmented (vessels segment length &amp;gt;385μm and diameter &amp;gt;90μm) for morphological analysis. Using a Bayesian framework, measures of vessels density, diameter and tortuosity were tested for significant differences in the three groups. Among all measures, the probability of vessel diameter &amp;gt;600μm in benign cases was significantly higher than that in malignant cases with the p-value &amp;lt;0.01 using Wilcoxon-Rank-Sum test, suggesting reliable cancer detection performance of the proposed method. [Acknowledgment: NIH Grant EB17213.]

Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI.

Extraction of liver parenchyma is an important step in the evaluation of R2*-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T2*-thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability. To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R2*-based HIC. Retrospective analysis of clinical data. Data from 511 MRI exams performed on 257 patients were analyzed. All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC. An automated method based on a multiscale vessel enhancement filter was investigated for three input data types-contrast-optimized composite image, T2* map, and R2* map-to segment blood vessels and extract liver tissue for R2*-based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*-thresholding technique performed by a radiologist. The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R2* results, obtained with the automated and reference techniques. Mean liver R2* values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R2 > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T2*-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* values compared to the automated method. The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542-1551.

Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

Background and objectiveAccurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation. MethodsThis paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan–Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model. ResultsThe accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method. ConclusionsWe demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.