1,631 research outputs found

    A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

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    Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of 0.82 +/- 0.06, mean surface distance of 0.43 +/- 0.14 mm and 95% Hausdorff Distance (HD) of 3.57 +/- 1.56 mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    Development and assessment of learning-based vessel biomarkers from CTA in ischemic stroke

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    Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA

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    Background Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. Purpose To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. Methods A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. Results Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6–1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0–1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00–1.00] and 0.99 [0.93–1.00] for calcium and uptake values, respectively). Conclusions We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring

    Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions

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    IntroductionDeep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we hypothesized that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images.MethodThis retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student’s t-test (alpha = 0.05) was used to test the significance in this study.Results & DiscussionThe AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively (n = 15, p-value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a p-value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a p-value of 0.014 (n = 15).ConclusionWe conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes

    Reconstruction and validation of arterial geometries for computational fluid dynamics using multiple temporal frames of 4D flow-MRI magnitude Images

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    Purpose Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data. Methods For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier–Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries. Results Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar. Conclusion This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast

    Whole-Body Magnetic Resonance Imaging Assessment of the Contributions of Adipose and Nonadipose Tissues to Cardiovascular Remodeling in Adolescents

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    BACKGROUND: Greater body mass index is associated with cardiovascular remodeling in adolescents. However, body mass index cannot differentiate between adipose and nonadipose tissues. We examined how visceral and subcutaneous adipose tissue are linked with markers of early cardiovascular remodeling, independently from nonadipose tissue. METHODS AND RESULTS: Whole‐body magnetic resonance imaging was done in 82 adolescents (39 overweight/obese; 36 female; median age, 16.3 [interquartile range, 14.4–18.1] years) to measure body composition and cardiovascular remodeling markers. Left ventricular diastolic function was assessed by echocardiography. Waist, waist:height ratio, and body mass index z scores were calculated. Residualized nonadipose tissue, subcutaneous adipose tissue, and visceral adipose tissue variables, uncorrelated with each other, were constructed using partial regression modeling to allow comparison of their individual contributions in a 3‐compartment body composition model. Cardiovascular variables mostly related to nonadipose rather than adipose tissue. Nonadipose tissue was correlated positively with left ventricular mass (r=0.81), end‐diastolic volume (r=0.70), stroke volume (r=0.64), left ventricular mass:end‐diastolic volume (r=0.37), and systolic blood pressure (r=0.35), and negatively with heart rate (r=−0.33) (all P<0.01). Subcutaneous adipose tissue was associated with worse left ventricular diastolic function (r=−0.42 to −0.48, P=0.0007–0.02) and higher heart rates (r=0.34, P=0.007) but linked with better systemic vascular resistance (r=−0.35, P=0.006). There were no significant relationships with visceral adipose tissue and no associations of any compartment with pulse wave velocity. CONCLUSIONS: Simple anthropometry does not reflect independent effects of nonadipose tissue and subcutaneous adipose tissue on the adolescent cardiovascular system. This could result in normal cardiovascular adaptations to growth being misinterpreted as pathological sequelae of excess adiposity in studies reliant on such measures

    Interaction of elastomechanics and fluid dynamics in the human heart : Opportunities and challenges of light coupling strategies

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    Das menschliche Herz ist das hochkomplexe HerzstĂŒck des kardiovaskulĂ€ren Systems, das permanent, zuverlĂ€ssig und autonom den Blutfluss im Körper aufrechterhĂ€lt. In Computermodellen wird die FunktionalitĂ€t des Herzens nachgebildet, um Simulationsstudien durchzufĂŒhren, die tiefere Einblicke in die zugrundeliegenden PhĂ€nomene ermöglichen oder die Möglichkeit bieten, relevante Parameter unter vollstĂ€ndig kontrollierten Bedingungen zu variieren. Angesichts der Tatsache, dass Herz-Kreislauf-Erkrankungen die hĂ€ufigste Todesursache in den LĂ€ndern der westlichen HemisphĂ€re sind, ist ein Beitrag zur frĂŒhzeit- igen Diagnose derselben von großer klinischer Bedeutung. In diesem Zusammenhang können computergestĂŒtzte Strömungssimulationen wertvolle Einblicke in die Blutflussdynamik liefern und bieten somit die Möglichkeit, einen zentralen Bereich der Physik dieses multiphysikalischen Organs zu untersuchen. Da die Verformung der EndokardoberflĂ€che den Blutfluss antreibt, mĂŒssen die Effekte der Elastomechanik als Randbedingungen fĂŒr solche Strömungssimulationen berĂŒcksichtigt werden. Um im klinischen Kontext relevant zu sein, muss jedoch ein Mittelweg zwischen dem Rechenaufwand und der erforderlichen Genauigkeit gefunden werden, und die Modelle mĂŒssen sowohl robust als auch zuverlĂ€ssig sein. Daher werden in dieser Arbeit die Möglichkeiten und Herausforderungen leichter und daher weniger komplexer Kopplungsstrategien mit Schwerpunkt auf drei SchlĂŒsselaspekten bewertet: Erstens wird ein auf dem Immersed Boundary-Ansatz basierender Fluiddynamik-Löser implementiert, da diese Methode mit einer sehr robusten Darstellung von bewegten Netzen besticht. Die grundlegende FunktionalitĂ€t wurde fĂŒr verschiedene vereinfachte Geometrien verifiziert und zeigte eine hohe Übereinstimmung mit der jeweiligen analytischen Lösung. Vergleicht man die 3D-Simulation einer realistischen Geometrie des linken Teils des Herzens mit einem körperangepassten Netzbeschreibung, so wurden grundlegende globale GrĂ¶ĂŸen korrekt reproduziert. Allerdings zeigten Variationen der Randbedingungen einen großen Einfluss auf die Simulationsergebnisse. Die Anwendung des Lösers zur Simulation des Einflusses von Pathologien auf die Blutströmungsmuster ergab Ergebnisse in guter Übereinstimmung mit Literaturwerten. Bei Simulationen der Mitralklappeninsuffizienz wurde der rĂŒckströmende Anteil mit Hilfe einer Partikelverfolgungsmethode visualisiert. Bei hypertropher Kardiomyopathie wurden die Strömungsmuster im linken Ventrikel mit Hilfe eines passiven Skalartransports bewertet, um die lokale Konzentration des ursprĂŒnglichen Blutvolumens zu visualisieren. Da in den vorgenannten Studien nur ein unidirektionaler Informationsfluss vom elas- tomechanischen Modell zum Strömungslöser berĂŒcksichtigt wurde, wird die RĂŒckwirkung des rĂ€umlich aufgelösten Druckfeldes aus den Strömungssimulationen auf die Elastomechanik quantifiziert. Es wird ein sequenzieller Kopplungsansatz eingefĂŒhrt, um fluiddynamische EinflĂŒsse in einer Schlag-fĂŒr-Schlag-Kopplungsstruktur zu berĂŒcksichtigen. Die geringen Abweichungen im mechanischen Solver von 2 mm verschwanden bereits nach einer Iteration, was darauf schließen lĂ€sst, dass die RĂŒckwirkungen der Fluiddynamik im gesunden Herzen begrenzt ist. Zusammenfassend lĂ€sst sich sagen, dass insbesondere bei Strömungsdynamiksimula- tionen die Randbedingungen mit Vorsicht gewĂ€hlt werden mĂŒssen, da sie aufgrund ihres großen Einflusses die AnfĂ€lligkeit der Modelle erhöhen. Nichtsdestotrotz zeigten verein- fachte Kopplungsstrategien vielversprechende Ergebnisse bei der Reproduktion globaler fluiddynamischer GrĂ¶ĂŸen, wĂ€hrend die AbhĂ€ngigkeit zwischen den Lösern reduziert und Rechenaufwand eingespart wird

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
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