A Framework Combining Multi-sequence MRI for Fully Automated Quantitative Analysis of Cardiac Global And Regional Functions

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1 A Framework Combining Multi-sequence MRI for Fully Automated Quantitative Analysis of Cardiac Global And Regional Functions Xiahai Zhuang 1,, Wenzhe Shi 2,SimonDuckett 3, Haiyan Wang 2, Reza Razavi 3,DavidHawkes 1,DanielRueckert 2, and Sebastien Ourselin 1 1 Centre for Medical Image Computing, University College London x.zhuang@ucl.ac.uk 2 Biomedical Image Analysis Group, Imperial College London 3 The Rayne Institute, St Thomas Hospital, King s College London Abstract. In current clinical settings, there are several technological challenges to perform automated functional analysis from cardiac MRI. In this work, we present a framework to automatically segment the heart anatomy, define segments of the left ventricle, and extract myocardial motions for quantitative analysis of cardiac global and regional functions. This framework makes use of the cardiac MRI sequences that are widely available in clinical practice, and improves the performance of the automated processing by combining information from multiple MRI sequences. We employed 2 pathological datasets to evaluate the proposed framework where the automatic analysis was compared with the manual intervention assisted analysis. The results showed high correlation between the two methods for the global function analysis (volume: R 2 >.8, ejection fraction:r 2 =.88), and for the regional dyssynchrony analysis (wall motion: R 2 =.89; thickening: R 2 =.81). We also found that the automated method could fully include apical and basal volume, resulting in consistent overestimation of the left ventricle volume ( 4mL, P<.5) and small underestimation of ejection fraction (.24, P<.1). 1 Introduction Cardiovascular disease is nowadays the world s number one killing disease, accounting for nearly thirty percent of worldwide deaths [1]. There have been tremendous efforts towards developing novel clinical applications using medical imaging and image analysis technology to reduce this death toll. Over the imaging modalities available in clinical routines, Magnetic Resonance Imaging (MRI) is becoming increasingly popular due to its non-ionizing radiation and good contrast in imaging soft tissues. However, one MRI sequence may only provide a limited portion of the full information required in automated image processing [2]. Therefore, combining all the available MRI data from different This work was funded by EPSRC grant EP/H225X/1. D.N. Metaxas and L. Axel (Eds.): FIMH 211, LNCS 6666, pp , 211. c Springer-Verlag Berlin Heidelberg 211

2 368 X. Zhuang et al. 3D amri SA cmri Motion/Shift Correction SA&LA cmri ED phase Combined MRI Segmentation Segmentation Result LA cmri Cardiac Motion Extraction Myocardium Fitting Personalized Bullseye segments Results Functional Index (EF) computation & Myocardial WM/WT Analysis Dyssynchrony Index Computation & Segmental WM/WT Analysis Results Fig. 1. Scheme of the proposed framework. amri: anatomical MRI; SA cmri: multislice, short-axis cine MRI; LA cmri: three-slice, long-axis cine MRI. sequences, such as anatomical 3D MRI (3D amri) and different orientation cine MRI (cmri), has potential to provide better accuracy and robustness for automated functional analysis. In this work, we propose an integrated framework, shown in Fig. 1, to automatically segment the left ventricle and myocardium, and extract myocardial motions for quantitative analysis of cardiac global and regional functions [3]. This process includes three MRI sequences, the balanced Steady State Free Procession (b-ssfp), three dimensional, anatomical MRI (3D amri) [4], the multi-slice, short-axis (SA) cine MRI (SA cmri), and the three-slice, long-axis (LA) cine MRI (LA cmri). To combine the three sequences, we first transform the data into the same world coordinate system and correct the misalignments between inter- and intra- sequences [2]. Then, we develop a registration scheme which propagates the segmentation in a pre-constructed atlas to the subjectspecific coordinate where three MR datasets are defined. To perform regional analysis, we build the personalized myocardial segments by fitting a 16-segment model (bullseye model) to the segmented myocardium [5]. This nonrigid fitting can maximally preserve the equal distribution of the myocardium segments. The cardiac motion within the ventricles are extracted using the combined information of the three LA slices and multiple SA slices. As a result, we can automatically compute the functional indices such as ejection fraction (EF) and perform myocardial wall motion and thickening analysis. Also, from the personalized bullseye model, we can analyze segmental volume, wall motion and thickening and compute the systolic dyssynchrony index (SDI) which is important in cardiac resynchronization therapy [5,6]. In the experiments, we employed twenty pathological data for evaluation. We computed the volumes of the left ventricle and myocardium, EF, SDIs using the proposed framework and the semi-automatic method available in clinical practice respectively, and compared their results.

3 A Framework Combining Multi-sequence MRI for Fully Automated 369 The paper is organized as follows: Section 2 describes the methodological framework in detail; Section 3 presents the experiments and results; our conclusions are given in Section 4. 2 Method 2.1 Motion and Shift Correction Images from different cardiac MRI sequences of a patient may be misaligned due to movements caused by body motions and respiration during acquisition. For cine MRI, which are used for myocardium motion tracking, this misalignment also happens between the slices, causing the slice shift problem [2], as Fig. 2 demonstrates. This shift problem exists in both the SA and LA slices which are generally taken from different breath holds. In this framework, we register all available MRI sequences to the reference coordinate system defined by the 3D amri. The 3D amir provides good spatial resolution, 1 1 1to2 2 2 mm, for accurate slice-to-volume registration. Therefore, we extract the end-diastolic phase from both the SA cmri and LA cmri, which is the same phase as 3D amri, and register them to 3D amri. The resultant transformation of each slice in end-diastolic phase is then applied to the same slice in the other phases of the cmri data. As a result, both the inter-sequence misalignment and intra-sequence slice shift are corrected and all images are transformed to the same spatial coordinate. Fig. 2 shows an example of the ED phase of a SA cmri before and after correction. 2.2 Multiple Image Registration for Segmentation Propagation For the automatic segmentation, we employ the image registration and atlas propagation-based method [8,9]. This method was mainly designed to extract the whole heart anatomy from 3D amri. However, 3D amri may not provide Fig. 2. Top row demonstrates multi-slice cine MRI before (left) and after (right) shift and motion correction. Both MR and segmentation images are interpolated into 1x1x1 mm using shape-based method [7]. Bottom row shows manual segmentation (left) and automated segmentation (right) without interpolation, red arrows point out the main difference by the two segmentation methods.

4 37 X. Zhuang et al. good contrast for myocardium, leading to less accurate delineation. By contrast, SA and LA cmri provide much better image quality within slices, but the spatial resolution between slices is limited. Therefore, we extend the method and propose to combine all the three MRI sequences, including 3D amri, LA cmri and SA cmri, for the registration propagation. In this registration scheme, the target space has multiple aligned target images. The cost function in this multiple image registration is given by: C([I 3D,I LA,I SA ],I at,t)=a 1 S(I 3D,T(I at )) + a 2 S(I LA,T(I at )) + a 3 S(I SA,T(I at )) + br(t ), (1) where I at, I 3D, I LA, I SA are the atlas image, 3D amri, LA cmri and SA cmri at end-diastolic phase, respectively; T, the transformation model, is a series of concatenated transformations including global affine, locally affine, and freeform deformtions (FFDs) with adaptive control point status [9]; S is similarity measure, the normalized mutual information (NMI) [1], normalized with the number of sample points N, suchass = 1 N NMI; R is the bending energy for regularization of the transformation; a 1, a 2, a 3 and b are weighting factors. To perform regional analysis, we define the 16-segment (bullseye) model for the myocardium using the same definition in [5]. This is done by fitting a pre-constructed model to the automatically segmented left ventricle and myocardium, as the framework shows in Fig Serial Registration for Cardiac Motion Tracking We use serial propagation registration [11] to model the large deformation field required for registration of phases which are far away from the ED phase. In the serial propagation, we first register ED phase to its neighboring phase using a cost function as follows: C([I LA (ED),I SA (ED)], [I LA (i),i SA (i)],t)= a 1 S(I LA (ED),T(I LA (i))) + a 2 S(I SA (ED),T(I SA (i))) + br(t ), (2) where ED and i indicate the cardiac phases. The resulting transformation is used to initialize the registration of the next phase. This process continues until all the phases are registered to the reference image. This registration is applied to the combined SA cmri and LA cmri data. The ventricular motions are then recorded in the deformation fields from each phase to the ED phase. 2.4 Cardiac Functional Analysis Using the framework in Fig. 1, we can perform both the global and regional function analysis. We will demonstrate the analysis on volume computation, EF, SDI from regional blood pool volume [5], wall motion and thickening analysis. Global LV dyssynchrony is calculated from the difference of the time taken to reach maximum of regional volume (motion, thickness) for the 16 segments. A SDI is then defined as the standard deviation of these timings, with a high SDI indicating more dyssynchrony. To allow for comparisons between patients with

5 A Framework Combining Multi-sequence MRI for Fully Automated 371 different heart rates, SDIs are normalized and expressed as percentages of the cardiac cycle. Also, a constraint is used for the computation of SDIs to exclude the segments of scared muscle. For SDIs computed from regional volume and motion analysis, we exclude the segments whose motion magnitudes are less than 1% of the maximum; and for that from wall thickening, we exclude the segments whose thickness changes are less than 1 mm. For regional analysis, graphs with curves indicating changes over the cardiac cycle can also be formed for each segment, allowing a visual investigation of segments during LV contraction. 3 Experiment We employed pathological data from 2 patients who had severe heart failure fulfilling standard criteria for cardiac resynchronization therapy for this study. Each of these patients had an MRI scan using the three MRI sequences, 3D amri, LA cmri and SA cmri. We performed both the functional analysis using the fully automatic framework (shown in Fig. 1) and the semi-automatic method available in clinical practice. In the semi-automatic analysis, the segmentation were achieved manually slice by slice from the SA cmri on the ED phase, and then propagated to other phases by the same registration scheme. Fig. 3 presents the linear regression and Bland-Altman plots of the segmented volumes and EF by the two methods, and Fig. 4 provides those of the SDIs. The 8 Endocardium Volume 12 Epicardium Volume.5 Ejection Fraction automatic segmentation (ml) Y=1.1847X+( ) R 2 =.8167 automatic segmentation (ml) Y=1.1189X+( ) R 2 =.8253 from automatic method Y=.9147X+(.14) R 2 = manual segmentation (ml) Enodocardium Volume (ml) manual segmentation (ml) Epicardium Volume (ml) auto manu from manual method.5.5 Ejection Fraction Fig. 3. Linear regression and Bland-Altman plots of the results from the automated segmentation and manual segmentation

6 372 X. Zhuang et al. from automatic method SDI from volume change Y=.6879X+(.361) R 2 =.617 from automatic method SDI from wall motion Y=.897X+(.68) R 2 =.8887 from automatic method SDI from wall thickening Y=.8699X+(.181) R 2 = from manual method from manual method from manual method.8 SDI from volume change.4 SDI from wall motion.8 SDI from wall thickening Fig. 4. Linear regression and Bland-Altman plots of SDIs by the fully automatic analysis and semi-automatic analysis measurements from the automatic and manual segmentation resulted in high correlation, as the coefficients for the left ventricle endocardium volume (ENDO) and epicardium volume (EPI) were R 2 =.817 and R 2 =.825 respectively. Also, the Dice were as high as.883 ±.36 and.896 ±.31. However, the automatic segmentation consistently overestimated the volume with significant bias, 4.5mL for ENDO and 38.8mL for EPI, and both had P<.5 from pair t-test. We found that this overestimation mainly came from the difference of the apical and basal segmentation, as Fig. 2 (bottom row) shows, the automated method could fully include these regions, thanks to the usage of 3D amri, while the manual segmentation, achieved from the SA cmri, could mis-segment them. The EF values by the two methods were highly correlated (R 2 =.877), though the volume overestimation resulted in a small but statistically significant bias (-.24, P<.1). From Fig. 4, we found that the two methods did not have high correlation (R 2 =.617) for SDIs computed from volume changes. This may be due to two bad cases as well as the difference from volume estimation, as the correlation coefficient increases to R 2 =.847 when the two cases are excluded. Fig. 4 also shows that the SDIs from wall motion and thickening analysis by the two methods resulted in much higher correlation (R 2 =.889 and R 2 =.87). For all the SDIs from volume change, wall motion and wall thickening, we found that the automatic method produced small biases compared with the results from the other method. However, none of these biases were statistically significant, as P =.11, P =.48 and P =.54 respectively. Finally,

7 A Framework Combining Multi-sequence MRI for Fully Automated 373 Fig. 5. An example of regional functional analysis using volume change (left), wall motion (middle) and wall thickening (right) from the fully automatic method (top row) and semi-automatic method (bottom row) Fig. 5 demonstrates the regional functional analysis using volume change, wall motion and wall thickening and the results from a pathological case. 4 Conclusion In this paper, we have proposed a fully automated framework for cardiac global and regional functional analysis. The registration used in the automated segmentation and motion tracking combines image information from multiple MRI sequences, which can provide accurate results for functional analysis. We employed 2 pathological datasets and compute the functional indices using both the proposed fully automatic method and the semi-automatic method available in current clinical practice. The results showed that the proposed method could fully segment the left ventricle, including the apical and basal regions, which is however generally difficult to manually delineate from SA cmri. This segmentation difference resulted in a consistent overestimation of the volume by the automatic segmentation, and large variations to the SDI analysis from volume changes. However, the segmentation difference and volume overestimation only caused a small bias (-.24) to the EF and SDIs from wall motion and wall thickening. These measurements from the proposed method also resulted in high correlation to these from the semi-automated method. Finally, we did not include the myocardial strain analysis, which is mainly available using speckle tracking from echocardiogram [6] or motion tracking from tagged MRI [11,12,13]. In the future work, we will extend the framework to include the tagged MRI for strain analysis.

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