Vessel tractography using an intensity based tensor model

In this paper, we propose a novel tubular structure segmen- tation method, which is based on an intensity-based tensor that fits to a vessel. Our model is initialized with a single seed point and it is ca- pable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrated the performance of our algorithm on 3 complex contrast varying tubular structured synthetic datasets for quantitative validation. Additionally, extracted arteries from 10 CTA (Computed Tomography An- giography) volumes are qualitatively evaluated by a cardiologist expert’s visual scores

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

An automatic branch and stenoses detection in computed tomography angiography

In this work, we present an automatic branch and stenoses de- tection method that is capable of detecting all types of plaques in Computed Tomography Angiography (CTA) modality. Our method is based on the vessel extraction algorithm we pro- posed in [1], and detects branches and stenoses in a very fast way. We demonstrate the performance of our branch detection method on 3 complex tubular structured synthetic datasets for quantitative validation. Additionally, we show the preliminary results of stenoses detection algorithm on 11 CTA volumes, which are qualitatively evaluated by a cardiol- ogist expert

Manifold learning for image-based gating of intravascular ultrasound(IVUS) pullback sequences

Intravascular Ultrasound(IVUS) is an imaging technology which provides cross-sectional images of internal coronary vessel struc- tures. The IVUS frames are acquired by pulling the catheter back with a motor running at a constant speed. However, during the pullback, some artifacts occur due to the beating heart. These artifacts cause inaccu- rate measurements for total vessel and lumen volume and limitation for further processing. Elimination of these artifacts are possible with an ECG (electrocardiogram) signal, which determines the time interval cor- responding to a particular phase of the cardiac cycle. However, using ECG signal requires a special gating unit, which causes loss of impor- tant information about the vessel, and furthermore, ECG gating function may not be available in all clinical systems. To address this problem, we propose an image-based gating technique based on manifold learning. Quantitative tests are performed on 3 different patients, 6 different pull- backs and 24 different vessel cuts. In order to validate our method, the results of our method are compared to those of ECG-Gating method

Current barriers and recommendations on the diagnosis of transthyretin amyloid cardiomyopathy: a Delphi study

ObjectivesThis study has been conducted to investigate the non-invasive diagnostic journey of patients with a transthyretin amyloid cardiomyopathy (aTTR-CM) in Turkey, identify the challenges and uncertainties encountered on the path to diagnosis from the perspectives of expert physicians, and develop recommendations that can be applied in such cases.MethodsThis study employed a three-round modified Delphi method and included 10 cardiologists and five nuclear medicine specialists. Two hematologists also shared their expert opinions on the survey results related to hematological tests during a final face-to-face discussion. A consensus was reached when 80% or more of the panel members marked the “agree/strongly agree” or “disagree/strongly disagree” option.ResultsThe panelists unanimously agreed that the aTTR-CM diagnosis could be established through scintigraphy (using either 99mTc-PYP, 99mTc-DPD, or 99mTc-HMPD) in a patient with suspected cardiac amyloidosis (CA) without a further investigation if AL amyloidosis is ruled out (by sFLC, SPIE and UPIE). In addition, scintigraphy imaging performed by SPECT or SPECT-CT should reveal a myocardial uptake of Grade ≥2 with a heart-to-contralateral (H/CL) ratio of ≥1.5. The cardiology panelists recommended using cardiovascular magnetic resonance (CMR) and a detailed echocardiographic scoring as a last resort before considering an endomyocardial biopsy in patients with suspected CA whose scintigraphy results were discordant/inconclusive or negative but still carried a high clinical suspicion of aTTR-CM.ConclusionThe diagnostic approach for aTTR-CM should be customized based on the availability of diagnostic tools/methods in each expert clinic to achieve a timely and definitive diagnosis

Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy. Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study. Results: Final results were evaluated in 2D projection space for patient data; and with ground truth values and landmark distances for the images acquired with a solid phantom vessel. Results validate that rotation recovery in frequency domain is robust against differences in segmentations in two modalities. Distance-map translation is successful in aligning coronary trees with highest possible overlap. Conclusions: Numerical and qualitative results show that single view rigid alignment in projection space is successful. This work can be extended with multiple views to resolve depth ambiguity and with deformable registration to account for nonrigid motion in patient data. (c) 2013 American Association of Physicists in Medicine

Template-based CTA x-ray angio rigid registration of coronary arteries in frequency domain

This study performs 3D to 2D rigid registration of segmented pre-operative CTA coronary arteries with a single segmented intra-operative X-ray Angio frame in both frequency and spatial domains for real-time Angiography interventions by C-arm fluoroscopy. Most of the work on rigid registration in literature required a close initialization of poses and/or positions because of the abundance of local minima and high complexity that searching algorithms face. This study avoids such setbacks by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. First, template DRRs as candidate poses of 3D vessels of segmented CTA are produced by rotating the camera (image intensifier) around the DICOM angle values with a wide range as in C-arm setup. We have compared the 3D poses of template DRRs with the real X-ray after equalizing the scales (due to disparities in focal length distances) in 3 domains, namely Fourier magnitude, Fourier phase and Fourier polar. The best pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that these methods are robust against noise and occlusion which was also validated by our results. Translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of our objective function without local minima due to distance maps. Final results were evaluated in 2D projection space rather than with actual values in 3D due to lack of ground truth, ill-posedness of the problem which we intend to address in future