Background: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and... Show moreBackground: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly. Methods: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results. Results: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation. Conclusions: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition. Show less
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac... Show moreIn recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. Show less
Sun, X.W.; Cheng, L.H.; Plein, S.; Garg, P.; Moghari, M.H.; Geest, R.J. van der 2023
Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI,... Show morePurpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27 degrees. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26 degrees. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams. Show less
Objective: In CheckMate 743 (NCT02899299), nivolumab + ipilimumab significantly prolonged overall survival in patients with unresectable malignant pleural mesothelioma (MPM). We present patient... Show moreObjective: In CheckMate 743 (NCT02899299), nivolumab + ipilimumab significantly prolonged overall survival in patients with unresectable malignant pleural mesothelioma (MPM). We present patient-reported outcomes (PROs). Materials and Methods: Patients (N = 605) were randomized to nivolumab + ipilimumab or chemotherapy. Changes in disease-related symptom burden and health-related quality of life (HRQoL) were evaluated descriptively using the Lung Cancer Symptom Scale (LCSS)-Mesothelioma (Meso) average symptom burden index (ASBI), LCSS-Meso 3-item global index (3-IGI), 3-level EuroQol 5-dimensional (EQ-5D-3L) visual analog score (VAS), and EQ-5D-3L utility index. PROs were assessed at baseline and every 2 (nivolumab + ipilimumab) or 3 weeks (chemotherapy) through 12 weeks, every 6 weeks through 12 months, every 12 weeks thereafter, and at specified follow-ups. Mixed-effect model repeated measures (MMRM) and time to deterioration analyses were conducted. Results: Completion rates were generally > 80%. LCSS-Meso ASBI mean changes from baseline trended to improve over time with nivolumab + ipilimumab and deteriorate with chemotherapy, but did not meet clinically important difference thresholds [& PLUSMN;10 score change]. EQ-5D-3L VAS mean scores improved over time with nivolumab + ipilimumab; by week 60, patients had scores consistent with United Kingdom normal population values. MMRM analyses favored nivolumab + ipilimumab for all individual symptoms except cough. Nivolumab + ipilimumab delayed time to definitive deterioration in HRQoL (hazard ratio 0.52 [95% confidence interval 0.36-0.74]) and showed a trend in symptom delay versus chemotherapy. Conclusions: Nivolumab + ipilimumab decreased the risk of deterioration in disease-related symptoms and HRQoL versus chemotherapy and maintained QoL in patients with unresectable MPM. Show less
Sun, X.W.; Garg, P.; Plein, S.; Geest, R.J. van der 2021
Purpose Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi-slice multi-phase cine MRI requires accurate segmentation of the LV in many images. We... Show morePurpose Quantification of left ventricular (LV) volume, ejection fraction and myocardial mass from multi-slice multi-phase cine MRI requires accurate segmentation of the LV in many images. We propose a stack attention-based convolutional neural network (CNN) approach for fully automatic segmentation from short-axis cine MR images.Methods To extract the relevant spatiotemporal image features, we introduce two kinds of stack methods, spatial stack model and temporal stack model, combining the target image with its neighboring images as the input of a CNN. A stack attention mechanism is proposed to weigh neighboring image slices in order to extract the relevant features using the target image as a guide. Based on stack attention and standard U-Net, a novel Stack Attention U-Net (SAUN) is proposed and trained to perform the semantic segmentation task. A loss function combining cross-entropy and Dice is used to train SAUN. The performance of the proposed method was evaluated on an internal and a public dataset using technical metrics including Dice, Hausdorff distance (HD), and mean contour distance (MCD), as well as clinical parameters, including left ventricular ejection fraction (LVEF) and myocardial mass (LVM). In addition, the results of SAUN were compared to previously presented CNN methods, including U-Net and SegNet.Results The spatial stack attention model resulted in better segmentation results than the temporal stack model. On the internal dataset comprising of 167 post-myocardial infarction patients and 57 healthy volunteers, our method achieved a mean Dice of 0.91, HD of 3.37 mm, and MCD of 1.08 mm. Evaluation on the publicly available ACDC dataset demonstrated good generalization performance, yielding a Dice of 0.92, HD of 9.4 mm, and MCD of 0.74 mm on end-diastolic images, and a Dice of 0.89, HD of 7.1 mm and MCD of 1.03 mm on end-systolic images. The Pearson correlation coefficient of LVEF and LVM between automatically and manually derived results were higher than 0.98 in both datasets.Conclusion We developed a CNN with a stack attention mechanism to automatically segment the LV chamber and myocardium from the multi-slice short-axis cine MRI. The experimental results demonstrate that the proposed approach exceeds existing state-of-the-art segmentation methods and verify its potential clinical applicability. Show less
