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Jafari R, Kandpal A, Verma R, Aggarwal V, Gupta RK, Singh A. Automatic pipeline for segmentation of LV myocardium on quantitative MR T1 maps using deep learning model and computation of radial T1 and ECV values. NMR IN BIOMEDICINE 2024:e5230. [PMID: 39097976 DOI: 10.1002/nbm.5230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 08/06/2024]
Abstract
Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV) calculation, and improves myocardial viability assessment. Accurate and precise segmenting of the left ventricular (LV) myocardium on T1 maps is crucial for assessing myocardial tissue characteristics and diagnosing cardiovascular diseases (CVD). This study presents a deep learning (DL)-based pipeline for automatically segmenting LV myocardium on T1 maps and automatic computation of radial T1 and ECV values. The study employs a multicentric dataset consisting of retrospective multiparametric MRI data of 332 subjects to develop and assess the performance of the proposed method. The study compared DL architectures U-Net and Deep Res U-Net for LV myocardium segmentation, which achieved a dice similarity coefficient of 0.84 ± 0.43 and 0.85 ± 0.03, respectively. The dice similarity coefficients computed for radial sub-segmentation of the LV myocardium on basal, mid-cavity, and apical slices were 0.77 ± 0.21, 0.81 ± 0.17, and 0.61 ± 0.14, respectively. The t-test performed between ground truth vs. predicted values of native T1, post-contrast T1, and ECV showed no statistically significant difference (p > 0.05) for any of the radial sub-segments. The proposed DL method leverages the use of quantitative T1 maps for automatic LV myocardium segmentation and accurately computing radial T1 and ECV values, highlighting its potential for assisting radiologists in objective cardiac assessment and, hence, in CVD diagnostics.
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Affiliation(s)
- Raufiya Jafari
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Ankit Kandpal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Radhakrishan Verma
- Department of Radiology, Fortis Memorial Research Institute, Gurugram, India
| | - Vinayak Aggarwal
- Department of Cardiology, Fortis Memorial Research Institute, Gurugram, India
| | - Rakesh Kumar Gupta
- Department of Radiology, Fortis Memorial Research Institute, Gurugram, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, AIIMS, New Delhi, India
- Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India
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Aung N, Bartoli A, Rauseo E, Cortaredona S, Sanghvi MM, Fournel J, Ghattas B, Khanji MY, Petersen SE, Jacquier A. Left Ventricular Trabeculations at Cardiac MRI: Reference Ranges and Association with Cardiovascular Risk Factors in UK Biobank. Radiology 2024; 311:e232455. [PMID: 38563665 DOI: 10.1148/radiol.232455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to (a) assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and (b) establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed. Automated segmentation of trabeculations was performed using a deep learning algorithm. After excluding individuals with known CV diseases, White adults without CV risk factors (reference group) and those with preexisting CV risk factors (hypertension, hyperlipidemia, diabetes mellitus, or smoking) (exposed group) were compared. Multivariable regression models, adjusted for potential confounders (age, sex, and height), were fitted to evaluate the associations between individual characteristics and CV risk factors and trabeculated LVM. Results Of 43 038 participants (mean age, 64 years ± 8 [SD]; 22 360 women), 28 672 individuals (mean age, 66 years ± 7; 14 918 men) were included in the exposed group, and 7384 individuals (mean age, 60 years ± 7; 4729 women) were included in the reference group. Higher body mass index (BMI) (β = 0.66 [95% CI: 0.63, 0.68]; P < .001), hypertension (β = 0.42 [95% CI: 0.36, 0.48]; P < .001), and higher physical activity level (β = 0.15 [95% CI: 0.12, 0.17]; P < .001) were associated with higher trabeculated LVM. In the reference group, the median trabeculated LVM was 6.3 g (IQR, 4.7-8.5 g) for men and 4.6 g (IQR, 3.4-6.0 g) for women. Median trabeculated LVM decreased with age for men from 6.5 g (IQR, 4.8-8.7 g) at age 45-50 years to 5.9 g (IQR, 4.3-7.8 g) at age 71-80 years (P = .03). Conclusion Higher trabeculated LVM was observed with hypertension, higher BMI, and higher physical activity level. Age- and sex-specific reference ranges of trabeculated LVM in a healthy middle-aged White population were established. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kawel-Boehm in this issue.
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Affiliation(s)
- Nay Aung
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Axel Bartoli
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Elisa Rauseo
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Sebastien Cortaredona
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Mihir M Sanghvi
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Joris Fournel
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Badih Ghattas
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Mohammed Y Khanji
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Steffen E Petersen
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
| | - Alexis Jacquier
- From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, England (N.A., E.R., M.M.S., M.Y.K., S.E.P.); Department of Radiology, Hôpital de la Timone, AP-HM, 264 rue Saint-Pierre, 13385 Marseille CEDEX 05, France (A.B., A.J.); Center for Magnetic Resonance in Biology and Medicine, UMR CNRS 7339, Aix-Marseille University, Marseille, France (A.B., J.F., A.J.); Institut de Recherche pour le Developpement, VITROME, Aix-Marseille University, Marseille, France (S.C.); Aix-Marseille School of Economics, Aix-Marseille University, Marseille, France (B.G.); Newham University Hospital, Barts Health NHS Trust, London, England (M.Y.K.); Health Data Research UK, London, England (S.E.P.); and Alan Turing Institute, London, England (S.E.P.)
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Priya S, Dhruba DD, Perry SS, Aher PY, Gupta A, Nagpal P, Jacob M. Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification. Acad Radiol 2024; 31:503-513. [PMID: 37541826 DOI: 10.1016/j.acra.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 08/06/2023]
Abstract
RATIONALE AND OBJECTIVES Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. MATERIALS AND METHODS In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). RESULTS The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. CONCLUSION Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation.
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Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa (S.P.).
| | - Durjoy D Dhruba
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.)
| | - Sarah S Perry
- Department of Biostatistics, University of Iowa, Iowa City, Iowa (S.S.P.)
| | - Pritish Y Aher
- Department of Radiology, University of Miami, Miller School of Medicine, Miami, Florida (P.Y.A.)
| | - Amit Gupta
- Department of Radiology, University Hospital Cleveland Medical Center, Cleveland, Ohio (A.G.)
| | - Prashant Nagpal
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin (P.N.)
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa (D.D.D., M.J.)
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Zheng JY, Chen BH, Wu R, An DA, Shi RY, Wu CW, Xie JY, Jiang SS, Jia V, Zhao L, Wu LM. 3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy. J Magn Reson Imaging 2024. [PMID: 38258534 DOI: 10.1002/jmri.29237] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/27/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear. PURPOSE To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images. STUDY TYPE Retrospective. POPULATION 85 ACM patients (mean age: 45 ± 17 years, 52 male). FIELD STRENGTH/SEQUENCE 3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE). ASSESSMENT Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE). STATISTICAL TESTS Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance. RESULTS 26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately. DATA CONCLUSION The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Jin-Yu Zheng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bing-Hua Chen
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Rui Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Aolei An
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruo-Yang Shi
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chong-Wen Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | | | | | - Victor Jia
- University of Michigan, Ann Arbor, Michigan, USA
| | - Lei Zhao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Lian-Ming Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Yao T, St. Clair N, Miller GF, Dorfman AL, Fogel MA, Ghelani S, Krishnamurthy R, Lam CZ, Quail M, Robinson JD, Schidlow D, Slesnick TC, Weigand J, Steeden JA, Rathod RH, Muthurangu V. A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology. Radiol Artif Intell 2024; 6:e230132. [PMID: 38166332 PMCID: PMC10831511 DOI: 10.1148/ryai.230132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 10/05/2023] [Accepted: 10/30/2023] [Indexed: 01/04/2024]
Abstract
Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 cardiac MRI examinations (November 2007-December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (bias: -0.6 mL/m2, LOA: -20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: -1.1 mL/m2, LOA: -18.1 to 15.9 mL/m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (bias: -1.9 g/m2, LOA: -17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: -17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: -12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry. Keywords: Cardiac, Adults and Pediatrics, MR Imaging, Congenital, Volume Analysis, Segmentation, Quantification Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
| | | | - Gabriel F. Miller
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Adam L. Dorfman
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Mark A. Fogel
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Sunil Ghelani
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Rajesh Krishnamurthy
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Christopher Z. Lam
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Michael Quail
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Joshua D. Robinson
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - David Schidlow
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Timothy C. Slesnick
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Justin Weigand
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Jennifer A. Steeden
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Rahul H. Rathod
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
| | - Vivek Muthurangu
- From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.)
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Wang X, Li X, Du R, Zhong Y, Lu Y, Song T. Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images. Bioengineering (Basel) 2023; 10:1267. [PMID: 38002391 PMCID: PMC10669053 DOI: 10.3390/bioengineering10111267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks.
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Grants
- Grant 12126610, Grant 81971691, Grant 81801809, Grant 81830052, Grant 81827802, and Grant U1811461,Grant 201804020053,Grant 2018B030312002,Grant 20190302108GX,grant 18DZ2260400, grant 2020B1212060032, Grant 2021B0101190003. Yao Lu
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Affiliation(s)
- Xuefang Wang
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511400, China;
| | - Xinyi Li
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China;
| | - Ruxu Du
- Guangzhou Janus Biotechnology Co., Ltd., Guangzhou 511400, China;
| | - Yong Zhong
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511400, China;
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Oncology in South China, Guangzhou 510060, China
| | - Ting Song
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China;
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7
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Liu Q, Lu Q, Chai Y, Tao Z, Wu Q, Jiang M, Pu J. Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification. Diagnostics (Basel) 2023; 13:diagnostics13091544. [PMID: 37174935 PMCID: PMC10177511 DOI: 10.3390/diagnostics13091544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/23/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Purpose: This study aimed to assess the value of radiomic features derived from the myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) detection and hypertrophic cardiomyopathy (HCM) versus hypertensive heart disease (HHD) differentiation. Methods: There were 345 subjects who underwent cardiovascular magnetic resonance (CMR) examinations that were analyzed. After quality control and manual segmentation, the 3D radiomic features were extracted from the MYO and PM. The data were randomly split into training (70%) and testing (30%) datasets. Feature selection was performed on the training dataset. Five machine learning models were evaluated using the MYO, PM, and MYO+PM features in the detection and differentiation tasks. The optimal differentiation model was further evaluated using CMR parameters and combined features. Results: Six features were selected for the MYO, PM, and MYO+PM groups. The support vector machine models performed best in both the detection and differentiation tasks. For LVH detection, the highest area under the curve (AUC) was 0.966 in the MYO group. For HCM vs. HHD differentiation, the best AUC was 0.935 in the MYO+PM group. Comparing the radiomics models to the CMR parameter models for the differentiation tasks, the radiomics models achieved significantly improved the performance (p = 0.002). Conclusions: The radiomics model with the MYO+PM features showed similar performance to the models developed from the MYO features in the detection task, but outperformed the models developed from the MYO or PM features in the differentiation task. In addition, the radiomic models performed better than the CMR parameters' models.
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Affiliation(s)
- Qiming Liu
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Qifan Lu
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Yezi Chai
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Zhengyu Tao
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Qizhen Wu
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Meng Jiang
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
| | - Jun Pu
- Department of Cardiology, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, China
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8
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Corrado PA, Wentland AL, Starekova J, Dhyani A, Goss KN, Wieben O. Fully automated intracardiac 4D flow MRI post-processing using deep learning for biventricular segmentation. Eur Radiol 2022; 32:5669-5678. [PMID: 35175379 DOI: 10.1007/s00330-022-08616-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/14/2021] [Accepted: 01/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES 4D flow MRI allows for a comprehensive assessment of intracardiac blood flow, useful for assessing cardiovascular diseases, but post-processing requires time-consuming ventricular segmentation throughout the cardiac cycle and is prone to subjective errors. Here, we evaluate the use of automatic left and right ventricular (LV and RV) segmentation based on deep learning (DL) network that operates on short-axis cine bSSFP images. METHODS A previously published DL network was fine-tuned via retraining on a local database of 106 subjects scanned at our institution. In 26 test subjects, the ventricles were segmented automatically by the network and manually by 3 human observers on bSSFP MRI. The bSSFP images were then registered to the corresponding 4D flow images to apply the segmentation to 4D flow velocity data. Dice coefficients and the relative deviation between measurements (automatic vs. manual and interobserver manual) of various hemodynamic parameters were assessed. RESULTS The automated segmentation resulted in similar Dice scores (LV: 0.92, RV: 0.86) and lower relative deviations from manual segmentation in left ventricular (LV) average kinetic energy (KE) (8%) and RV KE (15%) than the Dice scores (LV: 0.91, RV: 0.87) and relative deviations between manual segmentation observers (LV KE: 11%, p = 0.01; RV KE: 19%, p = 0.03). CONCLUSIONS The automated post-processing method using deep learning resulted in hemodynamic measurements that differ from a manual observer's measurements equally or less than the variation between manual observers. This approach can be used to decrease post-processing time on intraventricular 4D flow data and mitigate interobserver variability. KEY POINTS • Our proposed method allows for fully automated post-processing of intraventricular 4D flow MRI data. • Our method resulted in hemodynamic measurements that matched those derived from manual segmentation equally as well as interobserver variability. • Our method can be used to greatly accelerate intraventricular 4D flow post-processing and improve interobserver repeatability.
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Affiliation(s)
- Philip A Corrado
- University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
| | - Andrew L Wentland
- University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA
| | - Jitka Starekova
- University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA
| | - Archana Dhyani
- University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA
| | - Kara N Goss
- UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA
| | - Oliver Wieben
- University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA
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