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Abstract
Ischemic heart disease is the most common cause of cardiovascular morbidity and mortality. Cardiac magnetic resonance (CMR) improves on other noninvasive modalities in detection, assessment, and prognostication of ischemic heart disease. The incorporation of CMR in clinical trials allows for smaller patient samples without the sacrifice of power needed to demonstrate clinical efficacy. CMR can accurately quantify infarct acuity, size, and complications; guide therapy; and prognosticate recovery. Timing of revascularization remains the holy grail of ischemic heart disease, and viability assessment using CMR may be the missing link needed to help reduce morbidity and mortality associated with the disease.
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Affiliation(s)
- Aneesh S Dhore-Patil
- Tulane University Heart and Vascular Center, Tulane University, 1415 Tulane Avenue, New Orleans, LA 70112, USA
| | - Ashish Aneja
- Department of Cardiovascular Diseases, Case Western Reserve University, MetroHealth Medical Center, 2500 MetroHealth Drive, Cleveland, OH 44109, USA.
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Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis. MATHEMATICS 2020. [DOI: 10.3390/math8091511] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort.
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Yang HJ, Oksuz I, Dey D, Sykes J, Klein M, Butler J, Kovacs MS, Sobczyk O, Cokic I, Slomka PJ, Bi X, Li D, Tighiouart M, Tsaftaris SA, Prato FS, Fisher JA, Dharmakumar R. Accurate needle-free assessment of myocardial oxygenation for ischemic heart disease in canines using magnetic resonance imaging. Sci Transl Med 2020; 11:11/494/eaat4407. [PMID: 31142677 DOI: 10.1126/scitranslmed.aat4407] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 05/08/2019] [Indexed: 12/24/2022]
Abstract
Myocardial oxygenation-the ability of blood vessels to supply the heart muscle (myocardium) with oxygen-is a critical determinant of cardiac function. Impairment of myocardial oxygenation is a defining feature of ischemic heart disease (IHD), which is caused by pathological conditions that affect the blood vessels supplying oxygen to the heart muscle. Detecting altered myocardial oxygenation can help guide interventions and prevent acute life-threatening events such as heart attacks (myocardial infarction); however, current diagnosis of IHD relies on surrogate metrics and exogenous contrast agents for which many patients are contraindicated. An oxygenation-sensitive cardiac magnetic resonance imaging (CMR) approach used previously to demonstrate that CMR signals can be sensitized to changes in myocardial oxygenation showed limited ability to detect small changes in signals in the heart because of physiologic and imaging noise during data acquisition. Here, we demonstrate a CMR-based approach termed cfMRI [cardiac functional magnetic resonance imaging (MRI)] that detects myocardial oxygenation. cfMRI uses carbon dioxide for repeat interrogation of the functional capacity of the heart's blood vessels via a fast MRI approach suitable for clinical adoption without limitations of key confounders (cardiac/respiratory motion and heart rate changes). This method integrates multiple whole-heart images within a computational framework to reduce noise, producing confidence maps of alterations in myocardial oxygenation. cfMRI permits noninvasive monitoring of myocardial oxygenation without requiring ionizing radiation, contrast agents, or needles. This has the potential to broaden our ability to noninvasively identify IHD and a diverse spectrum of heart diseases related to myocardial ischemia.
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Affiliation(s)
- Hsin-Jung Yang
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.,University of California, Los Angeles CA 90095, USA
| | | | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.,University of California, Los Angeles CA 90095, USA
| | - Jane Sykes
- Lawson Health Research Institute, University of Western Ontario, London, ON N6C 2R5, Canada
| | - Michael Klein
- University of Toronto and University Health Network, Toronto, ON M5G 2C4, Canada
| | - John Butler
- Lawson Health Research Institute, University of Western Ontario, London, ON N6C 2R5, Canada
| | - Michael S Kovacs
- Lawson Health Research Institute, University of Western Ontario, London, ON N6C 2R5, Canada
| | - Olivia Sobczyk
- University of Toronto and University Health Network, Toronto, ON M5G 2C4, Canada
| | - Ivan Cokic
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Piotr J Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.,University of California, Los Angeles CA 90095, USA
| | - Xiaoming Bi
- MR R&D Collaborations, Siemens Healthineers, Los Angeles, CA 90048, USA
| | - Debiao Li
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.,University of California, Los Angeles CA 90095, USA
| | | | | | - Frank S Prato
- Lawson Health Research Institute, University of Western Ontario, London, ON N6C 2R5, Canada
| | - Joseph A Fisher
- University of Toronto and University Health Network, Toronto, ON M5G 2C4, Canada
| | - Rohan Dharmakumar
- Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. .,University of California, Los Angeles CA 90095, USA
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Machine learning based quantification of ejection and filling parameters by fully automated dynamic measurement of left ventricular volumes from cardiac magnetic resonance images. Magn Reson Imaging 2019; 67:28-32. [PMID: 31838116 DOI: 10.1016/j.mri.2019.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/13/2019] [Accepted: 12/07/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference. METHODS We studied 21 patients undergoing clinical CMR examinations. LV volume-time curves were obtained using the ML-based algorithm (Neosoft), and independently using slice-by-slice, frame-by-frame manual tracing of the endocardial boundaries. Ejection and filling parameters derived from these curves were compared between the two techniques. For each parameter, Bland-Altman bias and limits of agreement (LOA) were expressed in percent of the mean measured value. RESULTS Time-volume curves were generated using the automated ML analysis within 2.5 ± 0.5 min, considerably faster than the manual analysis (43 ± 14 min per patient, including ~10 slices with 25-32 frames per slice). Time-volume curves were similar between the two techniques in magnitude and shape. Size and function parameters extracted from these curves showed no significant inter-technique differences, reflected by high correlations, small biases (<10%) and mostly reasonably narrow LOA. CONCLUSION ML software for dynamic LV volume measurement allows fast and accurate, fully automated analysis of ejection and filling parameters, compared to manual tracing based analysis. The ability to quickly evaluate time-volume curves is important for a more comprehensive evaluation of the patient's cardiac function.
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Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019; 21:61. [PMID: 31590664 PMCID: PMC6778980 DOI: 10.1186/s12968-019-0575-y] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
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Affiliation(s)
- Tim Leiner
- Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Bettina Baeßler
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA USA
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alistair A. Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King’s College London, London, UK
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