| 1. |
- Engblom, Henrik, et al.
(författare)
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A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance : Experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data
- 2016
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Ingår i: Journal of Cardiovascular Magnetic Resonance. - : Springer Science and Business Media LLC. - 1097-6647 .- 1532-429X. ; 18:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. Methods: The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images). Results: Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49). Conclusions: The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. Clinical trial registration: CHILL-MI: NCT01379261. MITOCARE: NCT01374321.
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| 2. |
- Hakacova, Nina, et al.
(författare)
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Aspects of Left Ventricular Morphology Outperform Left Ventricular Mass for Prediction of QRS Duration
- 2010
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Ingår i: Annals of Noninvasive Electrocardiology. - 1082-720X. ; 15:2, s. 124-129
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Tidskriftsartikel (refereegranskat)abstract
- Methods: The study population of healthy adult volunteers was divided into a sample for development of a prediction model (n = 63) and a testing sample (n = 30). Magnetic resonance imaging data were used to assess anatomical characteristics of the left ventricle: the angle between papillary muscles (PMA), the length of the left ventricle (LVL) and left ventricular mass (LVM). Twelve-lead electrocardiogram (ECG) was used for measurement of the QRS duration. Multiple linear regression analysis was used to develop a prediction model to estimate the QRS duration. The accuracy of the prediction model was assessed by comparing predicted with measured QRS duration in the test set. Results: The angle between PMA and the length of the LVL were statistically significant predictors of QRS duration. Correlation between QRS duration and PMA and LVL was r = 0.57, P = 0.0001 and r = 0.45, P = 0.0002, respectively. The final model for prediction of the QRS was: QRS(Predicted) = 97 + (0.35 x LVL) - (0.45 x PMA). The predicted and real QRS duration differed with median 1 ms. Conclusions: The model for prediction of QRS duration opens the ability to predict case-specific normal QRS duration. This knowledge can have clinical importance, when determining the normality on case-specific basis. Ann Noninvasive Electrocardiol 2010;15(2):124-129.
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| 3. |
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| 4. |
- Tufvesson, Jane
(författare)
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Automatic segmentation in CMR - Development and validation of algorithms for left ventricular function, myocardium at risk and myocardial infarction
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis four new algorithms are presented for automatic segmentation in cardiovascular magnetic resonance (CMR); automatic segmentation of the left ventricle, myocardial infarction, and myocardium at risk in two different image types. All four algorithms were implemented in freely available software for image analysis and were validated against reference delineations with a low bias and high regional agreement. CMR is the most accurate and reproducible method for assessment of left ventricular mass and volumes and reference standard for assessment of myocardial infarction. CMR is also validated against single photon emission computed tomography (SPECT) for assessment of myocardium at risk up to one week after acute myocardial infarction. However, the clinical standard for quantification of left ventricular mass and volumes is manual delineation which has been shown to have a large bias between observers from different sites and for myocardium at risk and myocardial infarction there is no clinical standard due to varying results shown for the previously suggested threshold methods. The new automatic algorithms were all based on intensity classification by Expectation Maximization (EM) and incorporation of a priori information specific for each application. Validation was performed in large cohorts of patients with regards to bias in clinical parameters and regional agreement as Dice Similarity Coefficient (DSC). Further, images with reference delineation of the left ventricle were made available for future benchmarking of left ventricular segmentation, and the new automatic algorithms for segmentation of myocardium at risk and myocardial infarction were directly compared to the previously suggested intensity threshold methods. Combining intensity classification by EM with a priori information as in the new automatic algorithms was shown superior to previous methods and specifically to the previously suggested threshold methods for myocardium at risk and myocardial infarction. Added value of using a priori information and intensity correction was shown significant measured by DSC even though not significant for bias. For the previously suggested methods of infarct quantification a poorer result was found in the new multi-center, multi-vendor patient data than in the original validation in animal studies or single center patient studies. Thus, the results in this thesis also show the importance ofusing both bias and DSC for validation and performing validation in images of representative quality as in multi-center, multi-vendor patient studies.
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| 5. |
- Tufvesson, Jane, et al.
(författare)
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Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT.
- 2016
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Ingår i: BMC Medical Imaging. - : Springer Science and Business Media LLC. - 1471-2342. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP.
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| 6. |
- Tufvesson, Jane, et al.
(författare)
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Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance
- 2012
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Ingår i: Journal of Cardiovascular Magnetic Resonance. - 1097-6647. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Background: T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods: Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results: MaR was 32.9 +/- 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 +/- 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 +/- 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 +/- 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 +/- 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 +/- 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 +/- 9.6%, R = 0.47 (p < 0.001) for Otsu. Conclusions: There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR.
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| 7. |
- Tufvesson, Jane, et al.
(författare)
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Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.
- 2015
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Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2015
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Tidskriftsartikel (refereegranskat)abstract
- Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.
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| 8. |
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