Assessment of tissue edema in patients with acute myocardial infarction by computer-assisted quantification of triple inversion recovery prepared MRI of the myocardium

Clinical Importance of Myocardial T2 Mapping and Texture Analysis

Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is valuable for diagnosis and assessment of the severity of various myocardial diseases owing to its potential to visualize myocardial scars. T₁ mapping is complementary to LGE because it can quantify the degree of myocardial fibrosis or edema. As such, T₁-weighted imaging techniques, including LGE using an inversion recovery sequence, contribute to cardiac MRI. T₂-weighted imaging is widely used to characterize the tissue of many organs. T₂-weighted imaging is used in cardiac MRI to identify myocardial edema related to chest pain, acute myocardial diseases, or severe myocardial injuries. However, it is difficult to determine the presence and extent of myocardial edema because of the low contrast between normal and diseased myocardium and image artifacts of T₂-weighted images and the lack of an established method to quantify the images. T₂ mapping quantifies myocardial T₂ values and help identify myocardial edema. The T₂ values are significantly related to the clinical symptoms or severity of nonischemic cardiomyopathy. Texture analysis is a postprocessing method to quantify tissue alterations that are reflected in the T₂-weighted images. Texture analysis provides a variety of parameters, such as skewness, entropy, and grey-scale non-uniformity, without the need for additional sequences. The abnormal signal intensity on T₂-weighted images or T₂ values may correspond to not only myocardial edema but also other tissue alterations. In this review, the techniques of cardiac T₂ mapping and texture analysis and their clinical relevance are described.

Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT

BACKGROUND

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.

METHODS

The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation).

RESULTS

MaR assessed by manual and automatic segmentation were 36 ± 10% and 37 ± 11%LVM respectively with bias 1 ± 6%LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10%LVM and 29 ± 7%LVM respectively with bias 2 ± 7%LVM. Inter-observer variability was 0 ± 3%LVM for manual delineation and -1 ± 2%LVM for automatic segmentation.

CONCLUSIONS

Automatic segmentation of MaR in CE-SSFP was validated against manual delineation in multi-center, multi-vendor studies with low bias and high regional agreement. Bias and variability was similar to inter-observer variability of manual delineation and inter-observer variability was decreased by automatic segmentation. Thus, the proposed automatic segmentation can be used to reduce subjectivity in quantification of MaR in RCT.

CLINICAL TRIAL REGISTRATION

NCT01379261. NCT01374321.

Computer-based assessment for facioscapulohumeral dystrophy diagnosis

The paper presents a computer-based assessment for facioscapulohumeral dystrophy (FSHD) diagnosis through characterisation of the fat and oedema percentages in the muscle region. A novel multi-slice method for the muscle-region segmentation in the T1-weighted magnetic resonance images is proposed using principles of the live-wire technique to find the path representing the muscle-region border. For this purpose, an exponential cost function is used that incorporates the edge information obtained after applying the edge-enhancement algorithm formerly designed for the fingerprint enhancement. The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index, indicating a high accuracy of the proposed method. Finally, the fat and oedema are quantified from the muscle region in the T1-weighted and T2-STIR magnetic resonance images, respectively, using the fuzzy c-mean clustering approach for 10 FSHD patients.

Comparison of semi-automated methods to quantify infarct size and area at risk by cardiovascular magnetic resonance imaging at 1.5T and 3.0T field strengths

BACKGROUND

There is currently no gold standard technique for quantifying infarct size (IS) and ischaemic area-at-risk (AAR [oedema]) on late gadolinium enhancement imaging (LGE) and T2-weighted short tau inversion recovery imaging (T2w-STIR) respectively. This study aimed to compare the accuracy and reproducibility of IS and AAR quantification on LGE and T2w-STIR imaging using Otsu's Automated Technique (OAT) with currently used methods at 1.5T and 3.0T post acute ST-segment elevation myocardial infarction (STEMI).

METHODS

Ten patients were assessed at 1.5T and 10 at 3.0T. IS was assessed on LGE using 5-8 standard-deviation thresholding (5-8SD), full-width half-maximum (FWHM) quantification and OAT. AAR was assessed on T2w-STIR using 2SD and OAT. Accuracy was assessed by comparison with manual quantification. Interobserver and intraobserver variabilities were assessed using Intraclass Correlation Coefficients and Bland-Altman analysis. IS using each technique was correlated with left ventricular ejection fraction (LVEF).

RESULTS

FWHM and 8SD-derived IS closely correlated with manual assessment at both field strengths (1.5T: 18.3 ± 10.7% LV Mass [LVM] with FWHM, 17.7 ± 14.4% LVM with 8SD, 16.5 ± 10.3% LVM with manual quantification; 3.0T: 10.8 ± 8.2% LVM with FWHM, 11.4 ± 9.0% LVM with 8SD, 11.5 ± 9.0% LVM with manual quantification). 5SD and OAT overestimated IS at both field strengths. OAT, 2SD and manually quantified AAR closely correlated at 1.5T, but OAT overestimated AAR compared with manual assessment at 3.0T. IS and AAR derived by FWHM and OAT respectively had better reproducibility compared with manual and SD-based quantification. FWHM IS correlated strongest with LVEF.

CONCLUSIONS

FWHM quantification of IS is accurate, reproducible and correlates strongly with LVEF, whereas 5SD and OAT overestimate IS. OAT accurately assesses AAR at 1.5T and with excellent reproducibility. OAT overestimated AAR at 3.0T and thus cannot be recommended as the preferred method for AAR quantification at 3.0T.

Diffuse diseases of the myocardium: MRI-pathologic review of cardiomyopathies with dilatation

OBJECTIVE

In this radiologic-pathologic review of the cardiomyopathies, we present the pertinent imaging findings of diffuse myocardial diseases that are associated with ventricular dilatation, including ischemic cardiomyopathy, nonischemic dilated cardiomyopathy, cardiac sarcoidosis, and iron overload cardiomyopathy.

CONCLUSION

Correlation of the key radiologic findings with gross and microscopic pathologic features is presented, to provide the reader with a focused and in-depth review of the pathophysiology underlying each entity and the basis for the corresponding imaging characteristics.

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

BACKGROUND

T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.

METHODS

We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.

RESULTS

The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.

CONCLUSION

Compared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice.

LV wall segmentation using the variational level set method (LSM) with additional shape constraint for oedema quantification

In this paper an automatic algorithm for the left ventricle (LV) wall segmentation and oedema quantification from T2-weighted cardiac magnetic resonance (CMR) images is presented. The extent of myocardial oedema delineates the ischaemic area-at-risk (AAR) after myocardial infarction (MI). Since AAR can be used to estimate the amount of salvageable myocardial post-MI, oedema imaging has potential clinical utility in the management of acute MI patients. This paper presents a new scheme based on the variational level set method (LSM) with additional shape constraint for the segmentation of T2-weighted CMR image. In our approach, shape information of the myocardial wall is utilized to introduce a shape feature of the myocardial wall into the variational level set formulation. The performance of the method is tested using real CMR images (12 patients) and the results of the automatic system are compared to manual segmentation. The mean perpendicular distances between the automatic and manual LV wall boundaries are in the range of 1-2 mm. Bland-Altman analysis on LV wall area indicates there is no consistent bias as a function of LV wall area, with a mean bias of -121 mm(2) between individual investigator one (IV1) and LSM, and -122 mm(2) between individual investigator two (IV2) and LSM when compared to two investigators. Furthermore, the oedema quantification demonstrates good correlation when compared to an expert with an average error of 9.3% for 69 slices of short axis CMR image from 12 patients.

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

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.