Master of Science Thesis in Electrical EngineeringDepartment of Electrical Engineering, Linköping University, 2016

Automatic alignment of 2Dcine morphological imagesusing 4D Flow MRI data

Victoria Härd

Master of Science Thesis in Electrical Engineering

Automatic alignment of 2D cine morphological images using 4D Flow MRI data

Victoria Härd

LiTH-ISY-EX–16/4992–SE

Supervisor: Vikas Gupta, PhDimh, Linköpings universitet

Mariana Bustamante, MScimh, Linköpings universitet

Examiner: Professor Tino Ebbers, PhDimh, Linköpings universitet

Division of Cardiovascular Magnetic ResonanceDepartment of Electrical Engineering

Linköping UniversitySE-581 83 Linköping, Sweden

Copyright © 2016 Victoria Härd

Abstract

Cardiovascular diseases are among the most common causes of death worldwide.One of the recently developed flow analysis technique called 4D flow magneticresonance imaging (MRI) allows an early detection of such diseases. Due to thelimited resolution and contrast between blood pool and myocardium of 4D flowimages, cine MR images are often used for cardiac segmentation. The delin-eated structures are then transferred to the 4D Flow images for cardiovascularflow analysis. Cine MR images are however acquired with multiple breath-holds,which can be challenging for some people, especially, when a cardiovascular dis-ease is present. Consequently, unexpected breathing motion by a patient maylead to misalignments between the acquired cine MR images.

The goal of the thesis is to test the feasibility of an automatic image registrationmethod to correct the misalignment caused by respiratory motion in morpholog-ical 2D cine MR images by using the 4D Flow MR as the reference image. As aregistration method relies on a set of optimal parameters to provide desired re-sults, a comprehensive investigation was performed to find such parameters.Different combinations of registration parameters settings were applied on 20datasets from both healthy volunteers and patients. The best combinations, se-lected on the basis of normalized cross-correlation, were evaluated using the clin-ical gold-standard by employing widely used geometric measures of spatial cor-respondence. The accuracy of the best parameters from geometric evaluation wasfinally validated by using simulated misalignments.

Using a registration method consisting of only translation improved the resultsfor both datasets from healthy volunteers and patients and the simulated mis-alignment data. For the datasets from healthy volunteers and patients, the regis-tration improved the results from 0.7074 ± 0.1644 to 0.7551 ± 0.0737 in Dice in-dex and from 1.8818 ± 0.9269 to 1.5953 ± 0.5192 for point-to-curve error. Thesevalues are a mean value for all the 20 datasets.

The results from geometric evaluation on the data from both healthy volunteersand patients show that the developed correction method is able to improve thealignment of the cine MR images. This allows a reliable segmentation of 4D flowMR images for cardiac flow assessment.

iii

Acknowledgments

I would like to thank my supervisors, Mariana Bustamante and Vikas Gupta atIMH, Linköpings University, for all the help and feedback during the thesis. Ithas been nice to always know that you support me. I would also like to thankAlexandru G. Fredriksson for the help with the segmentation of all the data. Theresult would not have been so good without your help. Also many thanks to myexaminer, Tino Ebbers.

I would also like to thank Peter Thulin for always supporting and believing inme.

Linköping, August 2016Victoria Härd

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Contents

Notation ix

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Theoretical background 52.1 Magnetic resonance imaging . . . . . . . . . . . . . . . . . . . . . . 52.2 Anatomy of the heart . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 4D Flow MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.4 Balanced steady state free precession MRI . . . . . . . . . . . . . . 6

2.4.1 Short axis view . . . . . . . . . . . . . . . . . . . . . . . . . 72.4.2 Long axis view . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.5 Image registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.5.1 Similarity metric . . . . . . . . . . . . . . . . . . . . . . . . 92.5.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5.3 Spatial transformation . . . . . . . . . . . . . . . . . . . . . 112.5.4 Other parameters . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Method 153.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.1.1 Datasets used . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Automatic image cropping . . . . . . . . . . . . . . . . . . . . . . . 173.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.1 Geometric measure based evaluation . . . . . . . . . . . . . 193.3.2 Intensity based evaluation . . . . . . . . . . . . . . . . . . . 203.3.3 Simulated misalignments . . . . . . . . . . . . . . . . . . . 213.3.4 Visualization of the results . . . . . . . . . . . . . . . . . . . 213.3.5 Region based analysis . . . . . . . . . . . . . . . . . . . . . . 22

4 Results 23

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viii Contents

4.1 Parameter evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2 Registration using translation transformation . . . . . . . . . . . . 25

4.2.1 Comparison of the datasets and regions of the heart . . . . 294.3 Registration using rigid transformation . . . . . . . . . . . . . . . . 33

4.3.1 Comparison of the datasets and regions of the heart . . . . 374.4 Comparison between translation and rigid transformation . . . . . 414.5 Simulated misalignments . . . . . . . . . . . . . . . . . . . . . . . . 43

5 Discussion 455.1 Cropping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.2 Registration parameters . . . . . . . . . . . . . . . . . . . . . . . . . 465.3 Geometric accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.4 Evaluation of the results . . . . . . . . . . . . . . . . . . . . . . . . 485.5 Simulated misalignments . . . . . . . . . . . . . . . . . . . . . . . . 485.6 Implentation details . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

6 Conclusions 49

Bibliography 51

Notation

Abbreviation

Abbreviation Meaning

bssfp Balanced steady state free precession MRIla Long axismi Mutual Informationmri Magnetic Resonance Imagingncc Normalized Cross-Correlationnmi Normalized Mutual Informationsa Short axis

ix

1Introduction

This is a master thesis in image processing and has been written at the Faculty ofMedical and Health Sciences (IMH), Department of Cardiovascular Medicine, atLinköpings University.

1.1 Background

Cardiovascular diseases are one of the most common causes of death worldwide[2], [9]. Cardiovascular magnetic resonance imaging (MRI) is a commonly usedtechnique to non-invasively study the structure and function of the heart for theirearly detection. Many of the cardiovascular MR acquisition methods require mul-tiple breath-holds. This can be hard for some patients and thus, motion artefactsare introduced in the acquired images. As a result, the myocardial segmenta-tion performed in these images may lead to reduced certainty in the analysis of4D Flow images. Manual alignment of these images is time consuming, tedious,and prone to observer bias. An automatic method using image registration tech-niques is therefore desired. Figure 1.1 depicts the extent of spatial misalignmentscaused by the motion artefacts. Commonly employed measures of spatial corre-spondence are used here. Further details on these measures are provided in thesubsequent chapters.

1

2 1 Introduction

(a)

(b)

Figure 1.1: Dice index and point-to-curve errors for the different regions ofthe heart. A: Apical, B: Basal, M-V: Mid-ventricle.

1.2 Literature review 3

1.2 Literature review

Carminati et al. attempted to translate each short-axis image in a CMR dataset byoptimizing the normalized cross-correlation (NCC) of the pixel intensities at theslice intersection with the long-axis images. The method produced good resultsbut was only tested on a phantom, which could be misleading when it appliedon clinical data [5]. In a study made by Elen et al. post-processing is presentedbased on the constrained optimization of the intensity similarity in the line of in-tersection between the different images. This was a fully automatic approach inwhich four different cost functions were evaluated to obtain the best possible re-sults. Their analysis showed that using Absolute Difference of Derivative resultedin better misalignment correction [8]. Another article of interest was written byChandler et al. where rigid registration, with NMI as similarity measure, wasused to register slices of the SA image to a high-resolution 3D MR axial cardiacvolume. The method was evaluated in a group of volunteers who had movedbetween breath-holds when the datasets was created, resulting in significant mis-alignment correction [7]. Barajas et al. also developed a method that corrects thedisplacement artefacts by using Normalized Mutual Information (NMI) as a mea-surement of registration accuracy [2]. More examples of this type of solutions arefound in [4], [6], [16], [17] and [20].

All these articles use the intersection line between the short-axis and the long-axisimages. On these intersection lines, the intensities are measured and compared.A transformation is then used to correct the moving image so it is as similar tothe fixed image as possible. The difference between theses articles and this the-sis is the image that is used as the reference image for the registration. To ourknowledge, previous studies have not used 4D Flow MRI images as a reference.

1.3 Aim

The goal of this thesis is to implement and validate an automatic method forcorrection of misalignments in Balanced steady state free precession MR images,often introduced due to breathing motion. We propose a technique that uses,already existing registration techniques in order to align short-axis 4D Flow andbalanced images. The method that produces the best result will be evaluated ina group of dataset acquired from both healthy volunteers and patients.

4 1 Introduction

1.4 Outline

The outline of this document is the following:

Chapter 2 provides theoretical background to the thesis.

Chapter 3 describes the methods that were used during the thesis.

Chapter 4 presents the results if proposed methods evaluation.

Chapter 5 discusses the results and motivates all the choices that have been made.Also some future work is presented here.

Chapter 6 contains the conclusion of the thesis.

2Theoretical background

2.1 Magnetic resonance imaging

MRI is a medical imaging technique based on the principle of nuclear spin andmagnetic field. In a human body, there are a large number of hydrogen atomsthat can be affected by a magnetic field. During image acquisition, the changes inmagnetic field leads to the alignment of hydrogen atoms inside the body. Becauseof the different number of hydrogen atoms in the various tissue types, the mag-netization varies accordingly. This variation is then used to distinguish betweendifferent types of tissues. To measure it, a radio wave with the same frequencyas the resonant frequency is sent into the body. The radio frequency pulse causestransversal magnetization which is orthogonal to the magnetic field. The fre-quency is then turned off and the magnetization is measured in the orthogonalplane. Measurement from this result, together with the density of hydrogen inthe tissue, results in an MR-image in which the differences between the tissuescan be observed [23].

2.2 Anatomy of the heart

The heart is a muscular organ, placed in the middle thoracic cavity, between boththe lungs. It is a part of the circulatory system and provides the whole body withblood. The heart is divided into four different chambers: two ventricles and twoatria. Between the ventricles and atria there are valves that prevent the back-flowof the blood. Blood with low oxygen enters the right atrium, and goes throughthe tricuspid valve to the right ventricle. From the right ventricle, it enters thepulmonary circulation where it receives oxygen and releases carbon dioxid. Theblood that has been oxygenated returns to the left atrium [9].

5

6 2 Theoretical background

Figure 2.1: Anatomy of the heart [11].

In this image, the upper chambers are the atria and the lower are the ventri-cles. In addition, oxygenated and deoxygenated blood are shown in red and bluecolors, respectively [9].

2.3 4D Flow MRI

4D Flow MRI is a 3D+time image or more specifically, a three-dimensional datasetacquired in a time-resolved, ECG-gated manner, with velocity encoding in allthree spatial directions [18]. Using 4D Flow MRI, it is possible to measure andvisualize the blood flow patterns within an acquired 3D volume [18], [21]. This3D volume is composed of isotropic or nearly isotropic voxels and allows for vi-sualization at any location within it. Additionally, the 4D Flow MRI is acquiredusing navigator gating without the need for a breath-hold. Consequently, breath-hold motion errors are not present in these images. So with these benefits, a slicethat represents the short axis view can be extracted from this 3D volume andthen be used as a reference image during motion correction. One disadvantageof 4D Flow MRI is its resolution. Even though its voxels have the same size in alldirections, the in-plane resolution of the image is lower when compared to thebalanced images.

2.4 Balanced steady state free precession MRI

The cine MR images are acquired using the balanced steady state free precession(bSSFP) technique [19], which usually requires several breath-holds. Typically,11-16 slices are obtained to encompass the entire heart.

2.5 Image registration 7

This images have higher in-plane resolution than the 4D Flow MRI images butmay have slice thickness up to 10 mm. In addition, the through plane resolutionis much lower than for a 4D Flow MRI image and motion artefacts due to respi-ratory motion introduce slice misalignments. These images can be reconstructedin different ways to analyse the heart from various views. The most commonlyused reconstructed views are explained in the following text.

2.4.1 Short axis view

Short axis (SA) view, of the heart that shows the left and right ventricles. Thisview is chosen so that it is perpendicular to the mitral valve plane. Therefore, itis also called the orthogonal view. An image of the SA view can be seen in Figure2.2a [3].

2.4.2 Long axis view

Long axis (LA) view, shows two, three or four of the heart’s chambers. It is aprojection parallel to the ventricular axis. An image of the LA view can be seenin Figure 2.2b [3].

(a) Short axis view. (b) Long axis view.

Figure 2.2: SA and LA view of the heart in balanced SSFP images.

2.5 Image registration

Image registration has been used extensively to compare images acquired at dif-ferent time points or using different imaging techniques. The aim is to find amapping function that best describes the global movement between the differentimages. Therefore, a model that matches the movement between the images iscreated, and the solution that fits all the image-points as good as possible is se-lected and used on the images [22].

It requires at least 2 images: a fixed and a moving image, see Figure 2.3. Themoving image (Im) is the misaligned image that needs to be aligned to the fixed

8 2 Theoretical background

image (If), also called as the reference image. Both images need to have the samesize, but their spatial domains can be different. These images will be comparedto each other and a mapping function will be found. This mapping function de-scribes the displacement field for each of the points in the two images. It can bedefined as

T (x) = x + u(x) (2.1)

where T(x) is the transformation and u(x) is the displacement. x represents thedifferent pixels in the images. The problem is to find the transformation thatmakes Im(T(x)) spatially aligned to If(x). [15]

This is illustrated in Figure 2.3, where the image registration is between the fixedimage to the left, and the moving image to the right. T is the transformationbetween them.

Figure 2.3: Image registration between the fixed image and the moved im-age.

A program that can solve this type of registration problem is a program calledElastix. In this program, different algorithms are implemented and can be ap-plied on images [14]. By create different combinations of theses algorithms, abest combination can be find and be used in the registration. In Figure 2.4, ageneral registration method is shown.

2.5 Image registration 9

Figure 2.4: Block scheme of a typical registration [10].

Figure 2.4 shows how the reference and target images are sent in to the reg-istration. In this thesis, the reference image will be called the fixed image andthe target image will be called the moving image. The registration starts mea-suring the similarity between the fixed and the moving image. Then it tunesthe transform matrices accordingly based on the optimization strategies. Whenthe registration is finished, the moving image is supposed to be aligned with thefixed image when the obtained transformation is applied. All the different steps,similarity metric, optimization and spatial transformation are described in detailbelow [15].

2.5.1 Similarity metric

The first component of a registration is the similarity metric. The metrics definesthe quality of alignment between the two images. In this thesis, two differentmetrics are used and are described below: Normalized mutual information andMutual information [15].

Normalized mutual information

The normalized mutual information, NMI, is a common method used for bothmono-modal and multi-modality registration. A mono-modal image set capturedon the same device and the images have the same or similar brightness range. Amulti-modal image set is instead captured on different devices and therefore thebrightness range for the images is different.

To understand NMI, marginal entropy and joint entropy must first be presented.Marginal entropy is defined as the probability that a random variable has a spe-cific value. By knowing the entropy for one of the image intensity distribution,the other intensity distributions can be predicted. The entropy becomes zero ifthe two images are homogeneous and that means that there is no uncertaintyabout the intensity. Entropy is instead high if the images are inhomogeneous.Marginal entropy is calculated as shown in Equation 2.2.

10 2 Theoretical background

H(X) = −∑a

PX(a)logPX(a) (2.2)

where X is a random variable and PX is the probability that X has value a.

Joint entropy is instead computed between two random variables using Equation2.3.

H(X, Y ) = −∑a,b

PXY (a, b)logPXY (a, b) (2.3)

Here, X and Y are two variables and PXY is the probability that both X and Yhave the value a and b. All this can also be shown in a Venn diagram. Then it canbe easier to see how H(X) and H(X, Y ) are related to each other [15].

Figure 2.5: A Venn diagram that shows the marginal entropy H(X), the jointentropy H(X,Y) and the mutual information I(X,Y). [24].

By taking this two entropies, NMI can be defined as shown in Equation 2.4. Fand M are the two different images that should be compared and the calculationsare done for all the pixels in the image and then a mean value of all the pixels iscomputed.

NMI(M, F) =H(M) + H(F)H(M, F)

(2.4)

where H(M) and H(F) are the marginal entropies and H(M,F) a joint entropy ofthe data.

Mutual Information

For the Mutual Information, MI, there should only be a relation between theprobability distributions of the intensities. This type of measure is good for imageregistration because it can handle both mono-modal and multi-modal images. MImeasures the marginal and joint/conditional entropies between the two imagesand is very similar to NMI [15].

2.5 Image registration 11

MI(IF , IM ) =∑m∈LM

∑f ∈LF

p(f , m)log2

(p(f , m)

pF(f )pM (m)

)(2.5)

where p(f , m) is the joint probability distribution of F and M. pF(f ) and pM (m)are the marginal probability distribution of F and M respectively.

2.5.2 Optimization

The optimizer controls the spatial correspondence between two images: in thiscase between the fixed image and the moving image. The optimizer decides whento stop the iterations and converge. Two different optimizers were used in this the-sis. Standard gradient descent, (SGD) and Adaptive stochastic gradient descent,(ASGD). Both are methods that find the local minimum and in this case, findwhich combination of parameters produces the best result. That is, the resultthat minimizes the difference between the images. The optimizer will stop whenthe result is found, or after a specified maximum number of iterations. ASGDis a more advanced version of SGD and is less sensitive to the other parametersettings in the registration [13]. ASGD will adapt the step to the steepness of theerror curve and SGD will always take steps of equal lenght. As a result, ASGDis often faster in finding the minimum but it can be difficult to determine howreliable it is [15].

2.5.3 Spatial transformation

Spatial transformation is decides which type of deformations the mapping func-tion can handle. In this thesis, translation and rigid transformation are used [15].

Translation

Translation between two images allows only movement in straight direction. Theimage cannot change size or be rotated. The mapping function for translation isdefined in Equation 2.6

Tµ(x) = x + t (2.6)

where x is the image and t is a translation matrix or vector [15].

Rigid transform

Rigid transform consists of rotation and translation between the two images. Scal-ing cannot be changed when this transformation is used. The mapping functionfor the rigid transform is defined in Equation 2.7

Tµ(x) = R(x − c) + t + c (2.7)

12 2 Theoretical background

where R is the rotation matrix that contains Euler angles. For 2D, one angle isneeded and for 3D, three angles need to be selected. t is the translation and c isthe centre point of the rotation [15].

2.5.4 Other parameters

There are a few more parameters in the registration that can be chosen and willaffect the result for the registration. They are described in the following text [15].

Image samplers

Image samplers describe different methods to select pixels in the images. TheEquations 2.2 to 2.5 all have a sum over all pixels in the image, which will betime consuming. By reducing the amount of pixels, the registration can be faster,even though some information may be lost. In general, the registration can han-dle fewer pixels and only the time will be positively affected. In this thesis, fourdifferent types of pixel selection methods have been evaluated.

The first methods uses all the pixels in the image. This results in long execu-tion times but no information loss. The second method defines a regular gridon the fixed image from which a number of pixels are selected. The third andfourth method is to select the pixels at random in the image. All the pixels havethe same probability to be selected and only a part of the total are chosen. In thefourth method it is also possible to select values between two pixels [15].

Interpolation

Interpolation is used to estimate values that are between two pixels. When theregistration has been applied, the image has probably been moved to a placewhere its values might be undefined. This means that if the new position is anon-position, a new value for that position must be calculated. Interpolationcan vary in both speed and quality. In this thesis, three different interpolationmethods were used. Nearest neighbour interpolation, linear interpolation andB-spline interpolation [15].

Nearest neighbour is a fast method that can result in lower quality images. Themethod takes the nearest pixel’s intensity value, and selects that value as the newpixel’s intensity. Linear interpolation looks at the surrounding pixels and takesa weighted value of these values. It has the potential to produce high qualityimages. However, it is slower than the nearest neighbour technique. B-spline isa method that can produce higher quality results, but takes longer time to pro-duce. There are different orders of b-spline. The zero and first order representthe nearest neighbour and linear interpolation [15].

2.5 Image registration 13

Data complexity

The complexity of an image describes how much information is contained onthe image. An image with low complexity is very smooth and it can also be downsampled, which means that the sampling factor has been decreased from the orig-inal. If the complexity of an image is low, the amount of data contained in it willbe smaller, and the time required to apply registration to it will be less. In thisthesis, three different types of data complexity reduction techniques have beenused: Gaussian pyramid, Gaussian scale pyramid and Shrinking pyramid. Whenusing a Gaussian pyramid, both down sampling and smoothing on the image areapplied. This will produce an image with low complexity. Gaussian scale pyra-mid applies only smoothing and Shrinking pyramid uses down sampling [15].

The techniques that use down sampling should not be utilized in combinationwith random or random coordinates (as discussed in image sampling section).However, by applying down sampling on an image or using a grid, the registra-tion will save time [15].

3Method

3.1 Experimental setup

The thesis work was evaluated on data from healthy volunteers and patients.

3.1.1 Datasets used

The proposed method was evaluated in 20 subjects including 10 healthy volun-teers and 10 patients with heart failure of different ethologies. These was dividedin 8 female and 2 male for the healthy volunteers and 2 female and 8 male forthe patients respectively. The age for these person are 65 ± 4 year for healthyvolunteers and 65 ± 6 year for patients. The healthy volunteers had no historyof prior or current cardiovascular disease or cardiac medication. The patientswere enrolled from the Department of Cardiology, Linköping University Hospi-tal. Exclusion criteria for the patients were: significant ventricular arrhythmia,heart rate lower than 40 bpm or greater than 100 bpm, cardiovascular shunt, andmore than mild to moderate valvular disorder. The research was performed inline with the Helsinki declaration and was approved by the regional ethics board.All subjects gave written informed consent.

The MRI examinations were performed on a clinical 3T Philips Ingenia scanner(Philips Healthcare, Best, the Netherlands). All subjects were injected with aGadolinium based contrast agent (Magnevist, Bayer Schering Pharma AG) priorto the acquisition for a late-enhancement study.

Cine MR images were acquired using a balanced Steady-State Free Precession(bSSFP) protocol at end-respiratory breath-holds, resulting in 30 time framesover the cardiac cycle. A short-axis stack with resolution of 1 x 1 mm, and slicethickness of 8 mm was acquired. Two-, three-, and four-chamber long-axis views

15

16 3 Method

were also obtained with resolution of 1 x 1 mm.

4D Flow MRI examinations were performed during free-breathing, using a navi-gator gated gradient-echo pulse sequence with interleaved three-directional flow-encoding and retrospective vector cardiogram controlled cardiac gating. Scanparameters included: Candy cane view adjusted to cover both ventricles, velocityencoding (VENC) 120 cm/s, flip angle 10 degrees, echo time 2.6 ms, repetitiontime 4.4 ms, parallel imaging (SENSE) speed up factor 3 (AP direction), k-spacesegmentation factor 3, acquired temporal resolution of 52.8 ms, spatial resolu-tion 2.7 x 2.7 x 2.8 mm3, and elliptical k-space acquisition. The typical scan timewas 7-8 min excluding and 10-15 min including the navigator gating.

Two acquisitions from each dataset: 4D Flow MRI and bSSFP cine MRI SA stack.The 4D Flow image will be the fixed image and the bSSFP cine MRI SA stack willbecome the moving image when registration is applied. In order to apply regis-tration on these images, they must be prepared so that they represent the samearea of the heart. Therefore, the 4D Flow volume was interpolated in the locationand direction of each plane in the bSSFP SA stack. In Figure 3.1, the originalimage from both bSSFP SA stack and 4D Flow are shown. All images are fromslice 7, a slice in the middle of the stack.

(a) (b) (c)

(d) (e) (f)

Figure 3.1: Original images for different datasets. Top row: bSSFP cine MRISA stack. Bottom row: same slice but on the 4D Flow MRI.

As can be seen in the images, the resolution is not the same and the interpo-lated slices of the 4D Flow volume contain sections where the two images did notintersect. To solve this problem cropping was used.

3.2 Automatic image cropping 17

3.2 Automatic image cropping

The original images present a challenge for the registration. To remove the sec-tions, where the bSSFP cine MRI SA stack and 4D Flow images do not intersect,the fixed and moving were pre-processed using a cropping method. Cropping isapplied on the images and only the registration; the cropped areas are restoredbefore the final transformation (from registration) is applied to the moving im-age. The cropping allows the registration to focus on the most important parts ofthe image, such as the heart.

It is worth noting that the cropping must ensure the inclusion of both the leftand the right ventricle in the images that are being registered. To ensure that, themiddle point of the cropping depends on the position of the heart, and thereforeit will be in different points for each dataset. To find the location of the heart, acropping in the middle area of the original image was used. Figure 3.2 shows theresulting images after cropping the centre of the image.

(a) (b) (c)

Figure 3.2: A smaller image that are used to find the centre of the heart.

These images are then converted to binary images by using Otsu´s method,which performs clustering-based image thresholding. The method calculates theoptimum threshold for the image in order to separate it into different values. Abinary image consists only of pixels that have intensity value 0 or 1. That givesan image that only is black and white and can be seen in Figure 3.3.

(a) (b) (c)

Figure 3.3: Binary images of the initially cropped images.

18 3 Method

Assumptions that are drawn from this image are that the biggest area willbelong to the heart and the other areas will be removed. In figure 3.4, only thebiggest area is left.

(a) (b) (c)

Figure 3.4: Binary images when only the biggest area are left.

After that, the centre of mass of that area is calculated. This point is then usedas the center of the cropping and converts to a point in the original image. Fromthis point, automatic cropping can be applied. It consist of a square box that is80x60. In Figure 3.5, it can be seen that the datasets have different centres andtherefore the cropping will look different for each datasets.

(a) (b) (c)

Figure 3.5: Point in the middle of the biggest area found in the binary image.

The final results from the cropping are shown in Figure 3.6. These images arethen the images that are used in the registration.

3.3 Evaluation 19

(a) (b) (c)

(d) (e) (f)

Figure 3.6: Final images for different datasets. The first row shows the cineMR image and the second the same slice in the 4D Flow MRI.

3.3 Evaluation

Different evaluation methods were used to decide which of all the combinationof parameters generated the best results. By taking all the measurements fromthe methods, a final result can be presented. Three different types of evaluationmethods were used: geometric measure based evaluation, intensity based evalua-tion, and evaluation through simulated misalignments.

3.3.1 Geometric measure based evaluation

Geometricmeasure based evaluation is independent of the intensity in the objects.It uses contours, which are created by segmenting different parts in the area ofinterest. In this case, the segmentation was done manually on the endocardiumand epicardium in the left ventricle, on both the cine MR and the 4D Flow MRimages. The segmentations were done by a clinician with 5 years of experience incardiovascular imaging. The clinical drew the contours manually in the cine MRimages, and then transferred the same contours in the 4D Flow image.

After the registration, the transformation matrix was created from the result andwas applied on the contours. The new contours can then be compared to theground truth, and the difference can be observed. The evaluation methods thatare used in this case are the Dice index and the point-to-curve error. In Figure 3.7,it can be seen how the contours look initially in both the bSSFP cine MR imageand the 4D Flow image.

20 3 Method

(a) (b)

Figure 3.7: Manually delineated contours for both an bSSFP cine MR and an4D Flow image.

Dice index

Dice index measures percentage of overlap between ground truth and registeredcontours. Its value is calculated as the intersecting area between the two con-tours, divided by the average area of the individual contours. This can be seen inEquation 3.1.

Dice index =2|X ∩ Y ||X | + |Y |

(3.1)

where X is the ground truth and Y is the registered contour. The range of the Diceindex is between 0 and 1, where 1 is a perfect overlap, and 0 indicates no overlapat all [1], [10].

Point-to-curve error

Another measure to test the accuracy of the registration is the point-to-curve er-rors. This error is calculated as the Euclidean distance between the ground truthcontour and the registered contour [10].

3.3.2 Intensity based evaluation

Intensity based evaluation compares the similarity in intensities in the whole im-age or in an area of the image after the registration has been performed. Duringassessment of the result, the evaluation method should not be the same as theone selected during the registration, in order to avoid biases in the evaluation.Because of that, NMI and MI were not used during this step and NCC was se-lected for intensity based comparison.

3.3 Evaluation 21

Normalized Cross Correlation (NCC)

NCC, is a measure related to the degree of association between two images. Touse it, a linear relation between the intensity in the two images has to exist. NCCis defined between -1 to 1, where -1 represents a perfect negative correlation, and1 a perfect positive correlation. A value of 0 means that the images do not haveany correlation at all.

3.3.3 Simulated misalignments

To further evaluate the result from the registration and determine if the methodworks as expected, simulated misalignments have been applied on one dataset.This dataset has very small misalignments initially. By doing this, the evaluationis easier to do because the expected correction is known. The misalignment isapplied with different levels of in order to assess if the method works for alllevels of misalignment. To calculate the levels, the mean of the movement for allthe datasets was used.

Large simulated misalignments

Large simulated misalignments means that the image has been moved a lot. Toknow how much a high value is, the mean and standard deviation of all the orig-inal datasets misalignments were determined. These are calculated for all thedifferent slices and this motion was chosen to be the mean plus two standard de-viation. The first and last three slices were not changed. The 4-6 were moved inone direction and the 7-9 in another direction. So the misalignment is betweenslices 4-9.

Small simulated misalignments

Small simulated misalignments means that the image has been moved little fromthe original image. This motion was chosen to be the mean value plus one stan-dard derivation and is applied on the same way as the large simulated misalign-ments.

3.3.4 Visualization of the results

Box plots were used to visualize the results. A box plot shows the distributionover the result. The plot consists of a box that includes the median of the result,so 50 percentiles of the result will be in the box. The lower boundary is 25 per-centile of the result and the upper boundary is 75 percentile. The line in the boxrepresents the median result. Points that lie further away from the box are calledoutliers and are the extreme values compare to the other values. The lines thatgo from the box represent the lowest and highest value of the result that are notoutliers [12].

22 3 Method

3.3.5 Region based analysis

The number of slices in SA cine MR images varies in different patients becauseof the differences in the size of their hearts. As a result, instead of slicewisecomparison, a region based comparison was used. The heart was divided into 4anatomical regions in order to display and analyse the results, as shown in Figure3.8.

Basal (B)

Superior Mid-Ventricle (S M-V)

Inferior Mid-Ventricle(I M-V)

Apical (A)

Figure 3.8: Regions of the left ventricle used for the analysis of results.

4Results

The results of the evaluation defined in previous chapter are presented here. Theresults are divided into different sections. First, the results from the best combi-nation of parameters is shown. After that, a comparison of translation and rigidtransformations are shown. In the last section, results from the simulated mis-alignments are presented.

4.1 Parameter evaluation

An extensive investigation was performed to find the best possible combinationof parameters for the registration. The registration was applied on 20 datasetsand 72 parameter combinations were evaluated. Based on the evaluation criteria,the best 8 of them were selected. The selection procedure will be described in de-tail in Chapter 5. The 8 parameter combinations found to be the best are shownin Table 4.1.

23

24 4 Results

Table 4.1: Best combinations of parameters.

1 2 3 4Cost function NMI NMI NMI NMIInterpolation Linear Linear Spline SplineTransform Rigid/Trans Rigid/Trans Rigid/Trans Rigid/TransOptimiser SGD ASGD SGD ASGDData complexity G. pyramid G. pyramid G. pyramid G. pyramidImage sampling Full Full Full Full

5 6 7 8Cost function MI MI MI MIInterpolation Linear Linear Spline SplineTransform Rigid/Trans Rigid/Trans Rigid/Trans Rigid/TransOptimiser SGD ASGD SGD ASGDData complexity G. pyramid G. pyramid G. pyramid G. pyramidImage sampling Full Full Full Full

All these 8 combinations have been applied on the entire cohort to computeDice index and point-to-curve error. These numbers show that the combinationsare very similar and therefore the registrations that were less time consumingwere chosen. Subsequently for all the tables, plots, and images, the parametercombination number 1 has been used.

4.2 Registration using translation transformation 25

4.2 Registration using translation transformation

In Figures 4.1 - 4.3, the four images represent slices from different regions ofthe heart. The overlaid contours depict the movement of bSSFP cine MR imagesbefore (red) and after (blue) the registration in comparison to the ground truth(green). Plots of all the contours are presented in Figure 4.4.

(a) (b)

(c) (d)

Figure 4.1: Different slices in a heart with misalignments. a) represent a slicein the apical region, b) and c) is slices in the mid-ventricle region and d) is aslice in the basal region. The overlaid contours depict the movement of cineMR images before (red) and after (blue) the registration in comparison to theground truth (green).

26 4 Results

(a) (b)

(c) (d)

Figure 4.2: Different slices in a heart with small misalignments. a) representa slice in the apical region, b) and c) is slices in the mid-ventricle region andd) is a slice in the basal region. The overlaid contours depict the movement ofcine MR images before (red) and after (blue) the registration in comparisonto the ground truth (green).

4.2 Registration using translation transformation 27

(a) (b)

(c) (d)

Figure 4.3: Different slices in a heart with very small misalignments. a)represent a slice in the apical region, b) and c) is slices in the mid-ventricleregion and d) is a slice in the basal region. The overlaid contours depict themovement of cine MR images before (red) and after (blue) the registration incomparison to the ground truth (green).

28 4 Results

(a) (b)

(c) (d)

(e) (f)

Figure 4.4: Datasets with different levels of misalignment. a) large misalign-ment, c) small misalignment and e) no or very small misalignment. b), d)and f) present the images after the registration for the different datasets.

4.2 Registration using translation transformation 29

4.2.1 Comparison of the datasets and regions of the heart

The data from the entire cohort was registered with the finally selected parame-ters and the quality of registration was evaluated using the Dice index and point-to-curve error. Figure 4.5 and Table 4.2 show the patientwise comparison andFigure 4.6 and Table 4.3 show how the different regions of the heart were affectedby the registration.

Comparison between datasets

First a comparison between the different datasets for the two types of calcula-tions. In Figure 4.5a, Dice index and in Figure 4.5b, point-to-curve error. Thecorresponding numbers from the plots are shown in Table 4.2.

(a)

(b)

Figure 4.5: Dice index and point-to-curve errors for all the datasets.

30 4 Results

Table 4.2: Minimum, mean and maximum values for Dice index and point-to-curve error for all datasets.

Dataset Dice index Point-to-curve errorMin Mean Max Min Mean Max

1 0.5121 0.6590 0.7411 2.2115 2.3894 2.67632 0.5474 0.8229 0.8601 0.8837 1.0177 1.82643 0.3932 0.7075 0.7904 1.2297 1.5676 2.80304 0 0.8632 0.9183 0.4754 0.7452 45 0.4345 0.7217 0.7918 1.4261 1.5258 2.15306 0.1755 0.6939 0.8584 0.9031 1.4902 3.27177 0.2438 0.4931 0.5957 2.2792 2.5943 3.19498 0.4006 0.8071 0.8551 0.8244 0.9849 2.36099 0.1039 0.8413 0.9055 0.6945 0.9232 3.598010 0.4310 0.8055 0.8911 0.6742 1.2470 2.607211 0 0.7215 0.8928 0.9795 1.7386 412 0.2626 0.7086 0.8705 1.0319 1.6669 3.153213 0.4473 0.7797 0.9109 1.1127 1.7353 3.149714 0.3179 0.7290 0.8563 1.0754 1.4270 2.772815 0.4601 0.6490 0.6801 2.1569 2.5689 3.547316 0.1657 0.6949 0.8021 1.8088 2.2298 3.536017 0.3138 0.6509 0.7617 1.5076 1.8476 2.977618 0.5369 0.8264 0.8475 1.5331 1.5382 2.375519 0.5131 0.7759 0.8335 1.5205 2.1019 2.939120 0.5246 0.7865 0.8597 1.3523 1.7551 2.6172

4.2 Registration using translation transformation 31

Comparison between different regions of the heart

Then a comparison between the regions in the heart for the two types of calcu-lations. In Figure 4.6a, Dice index and in Figure 4.6b, point-to-curve error. Thecorresponding numbers from the plots are shown in Table 4.3.

(a)

(b)

Figure 4.6: Dice index and point-to-curve errors for the different regions ofthe heart.

32 4 Results

Table 4.3: Minimum and maximum values for Dice index and point-to-curveerror for all different regions of the heart. A: Apical, B: Basal, M-V: Mid-ventricle.

Regions ofheart

Dice index Point-to-curve errorMin Max Min Max

A 0 0.5474 1.8264 4Inferior M-V 0.3185 0.8975 0.6742 2.6447Superior M-V 0.5580 0.9032 0.7411 3.5473B 0.3932 0.9183 0.4754 2.9581

4.3 Registration using rigid transformation 33

4.3 Registration using rigid transformation

Here are the results from the rigid transformation. In Figures 4.7 - 4.9, the fourimages represent different slices of the heart. Also plots of all the contours to-gether are presented. These are shown in Figure 4.10.

(a) (b)

(c) (d)

Figure 4.7: Different slices in a heart with misalignments. a) represent a slicein the apical region, b) and c) is slices in the mid-ventricle region and d) is aslice in the basal region. The overlaid contours depict the movement of cineMR images before (red) and after (blue) the registration in comparison to theground truth (green).

34 4 Results

(a) (b)

(c) (d)

Figure 4.8: Different slices in a heart with small misalignments. a) representa slice in the apical region, b) and c) is slices in the mid-ventricle region andd) is a slice in the basal region. The overlaid contours depict the movement ofcine MR images before (red) and after (blue) the registration in comparisonto the ground truth (green).

4.3 Registration using rigid transformation 35

(a) (b)

(c) (d)

Figure 4.9: Different slices in a heart with very small misalignments. a)represent a slice in the apical region, b) and c) is slices in the mid-ventricleregion and d) is a slice in the basal region. The overlaid contours depict themovement of cine MR images before (red) and after (blue) the registration incomparison to the ground truth (green).

36 4 Results

(a) (b)

(c) (d)

(e) (f)

Figure 4.10: Datasets with different levels of misalignment. a) dataset withlarge misalignment, c) dataset with small misalignment and e) dataset withno or very small misalignment. b), d) and f) present the images after theregistration for the different datasets.

4.3 Registration using rigid transformation 37

4.3.1 Comparison of the datasets and regions of the heart

In the same way as presented in the previous section, when using translationtransformation during the registration, the Dice index and the point-to-curve er-ror where also used to evaluate the results. Figure 4.11 and Table 4.4 show thepatient comparison and Figure 4.12 and Table 4.5 show how the different regionsof the heart were affected by the registration.

Comparison between datasets

First a comparison between the different datasets for the two types of calcula-tions. In Figure 4.11a, Dice index and in Figure 4.11b, point-to-curve error. Thecorresponding numbers from the plots are shown in Table 4.4.

(a)

(b)

Figure 4.11: Dice index and point-to-curve errors for all the datasets.

38 4 Results

Table 4.4: Minimum and maximum values for Dice index and point-to-curveerror for all datasets.

Datasets Dice index Point-to-curve errorMin Max Min Max

1 0.4169 0.8025 1.7129 4.03972 0.0581 0.9079 0.5834 11.43133 0.0386 0.7951 1.2424 11.48084 0 0.9134 0.4903 7.17965 0.0359 0.8260 1.2668 11.68786 0.1598 0.8265 1.0542 3.32477 0.2402 0.5807 2.4916 4.15598 0.0511 0.8655 0.7685 11.10549 0 0.9173 0.6022 9.567210 0.0855 0.9051 0.5799 7.340811 0 0.8731 1.2115 6.384112 0.0528 0.8494 1.2995 11.874313 0.3546 0.9079 1.1695 5.234714 0.2190 0.8221 1.3091 3.616615 0.0614 0.5639 2.7222 12.142916 0.0561 0.8068 1.7857 4.700217 0.0773 0.7780 1.3800 8.054618 0.0626 0.6513 2.2277 11.113119 0.1117 0.7821 1.9286 9.354520 0.1363 0.8399 1.4671 5.2488

4.3 Registration using rigid transformation 39

Comparison between different regions of the heart

Then a comparison between the regions in the heart for the two types of calcula-tions. In Figure 4.12a, Dice index and in Figure 4.12b, point-to-curve error. Thecorresponding numbers from the plots are shown in Table 4.5.

(a)

(b)

Figure 4.12: Dice index and point-to-curve errors for the different regions ofthe heart.

40 4 Results

Table 4.5: Minimum and maximum values for Dice index and point-to-curveerror for all different regions of the heart. A: Apical, B: Basal, M-V: Mid-ventricle.

Regions ofheart

Dice index Point-to-curve errorMin Max Min Max

A 0 0.4169 3.3247 12.1429Inferior M-V 0.1958 0.9051 0.5799 7.2405Superior M-V 0.3947 0.9173 0.5834 4.5156B 0.3988 0.9134 0.4903 2.8966

4.4 Comparison between translation and rigid transformation 41

4.4 Comparison between translation and rigidtransformation

To compare the transformations, they were plotted against each other. This hasbeen done for each region and for both Dice index and point-to-curve error. InFigure 4.13, Dice index and in Figure 4.14, point-to-curve error. a),b), c) and d)represent the regions, starting with apical and ending with basal.

(a) (b)

(c) (d)

Figure 4.13: Dice index after using translation and rigid transformation ina) apical region, b) inferior mid-ventricle region, c) superior mid-ventricleregion and d) basal region.

42 4 Results

(a) (b)

(c) (d)

Figure 4.14: Point-to-curve errors after using translation and rigid trans-formation in a) Apical region, b) Inferior mid-ventricle region, c) Superiormid-ventricle region and d) Basal region.

4.5 Simulated misalignments 43

4.5 Simulated misalignments

As previously explained, different levels of misalignment were applied to onedataset. The parameters that produced the best results were used to register thisset. The result from this registration are shown in Table 4.6 and Table 4.7. InFigure 4.15, the contours from different datasets are shown before and after theregistration.

Table 4.6: Dice index for different levels of simulated misalignments. Uand R stand for unregistered and registered images, respectively. In the firstcolumn, different levels of misalignments are shown. µ and σ are the meanand standard deviation of the original movement, respectively. The value inthe last row does not include the results from the apical region.

Simulatedmisalignment

Regions Mean valueA I M-V S M-V B

µ + 2 σ U 0.8254 0.4759 0.5985 0.9223 0.6656R 0.3388 0.9381 0.8853 0.9063 0.9126

µ + σ U 0.8254 0.6985 0.5616 0.9223 0.8960R 0.3401 0.9034 0.9121 0.9055 0.9099

None U 0.8254 0.8222 0.9239 0.9223 0.8894R 0.3117 0.8975 0.8973 0.9055 0.9001

Table 4.7: Point-to-curve (in mm) for different levels of simulated misalign-ments. U and R stand for unregistered and registered images, respectively.µ and σ are the mean and standard deviation of the original movement, re-spectively. The value in the last row does not include the results from theapical region.

Simulatedmisalignment

Regions Mean valueA I M-V S M-V B

µ + 2 σ U 1.0836 2.6120 2.4973 0.5581 1.8892R 2.6352 0.3975 0.6963 0.7805 0.6248

µ + σ U 1.0836 2.1084 2.6155 0.5581 0.7451R 2.6305 0.4145 0.8418 0.6948 0.6656

None U 1.0836 1.2502 0.5504 0.5581 0.7862R 2.7941 0.7105 0.7411 0.6945 0.7154

44 4 Results

(a) (b)

(c) (d)

(e) (f)

Figure 4.15: Contours for different levels of simulated misalignments. a)large level of misalignment, c) small level of misalignment and e) no mis-alignment. b), d) and f) present the images after the registration.

5Discussion

The results show that the misalignments caused by respiratory motion artefactscan be mitigated by the proposed method. In the following text, we discuss howthe different components of the proposed registration approach influence the ac-curacy of the resulting alignments.

It was a challenge to use intensity based registration on the types of images usedbecause of the big difference in resolution and intensity between them. One prob-lem that developed from the large difference in resolution was during the evalua-tion of the method. It was hard to compare the two images to each other and alsohard to find an intensity based method that worked on both images. However,when using an evaluation method that only depended on the distance and the ge-ometric properties of the images, a better comparison was possible. The resultsshow that the developed correction method is able to improve the alignment ofthe bSSFP cine MR SA stack. The method was further validated by the results ob-tained on the simulated misalignments, even when presented with small or largeerrors.

5.1 Cropping

Cropping of the images is an important step because without it, the registrationmight result in suboptimal alignments. Since both the left and right ventriclesare visible in the SA format and present the desired information, the croppingwas implemented in this work to ensure those structures. But this cannot beguaranteed for all cases, because of the variations in size and location of the heartacross the patient population. It is often hard to find a small heart and hence itscentroid, especially in the apical slices. In that case, the automatic cropping maymiss the heart. On the other hand, when a patient has a large heart, cropping

45

46 5 Discussion

might not include the entire heart. In both of these cases, there is a higher proba-bility of suboptimal registration.

For this work, we used a constant cropping size for the entire cohort with anadditional visual check to make sure that the complete heart is included in thecropping. After the registration, the cropped area will be restored and the wholeimage is moved. To avoid the visualization step, the implemented tool can beimproved by using a weight function instead. This function will then adjust thecrop based on the size of the heart and the location of the slice to be registered.Another possible improvement would be to use the location of the LA images. Acenter-line of the left ventricle of the heart can be found and its position can beused as the center for the cropping.

5.2 Registration parameters

In total, 72 parameter combinations were tested. Eventually, NCC and the pro-cessing time were used to select the best 8 combinations. These combinations canbe seen in Table 4.1. It can be observed from this table that they include only onetype of image sampling (full) and one type of data complexity (Gaussian pyra-mid). The Gaussian pyramid does both down sampling and smoothing of theimage, which results in a low complexity and by using full image sampling, allthe pixels in the images are used.

From theses combinations, similarities were found. There was either a small orno difference between the interpolation methods (linear and spline) and betweenthe optimization strategies (SGD and ASGD). This can be explained by the factthat the images being used do not have very high resolution. From this, it can beconcluded that a finer interpolation like spline and a more advanced optimiza-tion like ASGD is not needed. The selection of the cost function was from thebeginning rather straightforward. Due to the difference in the intensity distribu-tion and resolution of the cine and 4D Flow MR images, mutual information (MI)and its normalized form, normalized mutual information (NMI), were the bestcandidates. Since NMI brings the MI values to a bounded range (0,1), it offered abetter comparison measure for this work.

For transformation, two options were available: rotation and translation. Many ofthe misalignments that can occur during a scan are caused by the patient breath-ing or moving around. These types of misalignments can usually be fixed byusing just rotation and translation. Furthermore, the size and shape of the heartwill not be affected and affine transformation is not needed. These two differenttransformations methods, together with the best combination of the remainingparameters were then used in all images and plots that were shown in the result.

While using transformations that include both rotation and translation alignmentmethod resulted in some improvements. However, the best results were obtained

5.3 Geometric accuracy 47

using only translation. That is because of the limiations of movement imposedinitially during generation of the ground truth and also because of the typicalpresentaion of breath-hold motion errors.

5.3 Geometric accuracy

When registration was applied, which can be seen in Figures 4.1 - 4.3 and in Fig-ures 4.7 - 4.9, the bSSFP cine MR image has been moved to a more correct place.That is illustrated by the difference between the red and the blue contours in thefigures. It can be seen that the different images have more or less misalignmentinitially by looking at the distance between the red and the green contours. If theimage was perfect from the beginning, the red and the green should be on top ofeach other and correction should not be needed. But that was not the case in anyof the datasets that were analysed.

When only using translation, Figures 4.1 - 4.3, the image was closer to the groundtruth for all the cases. This means that the correction has improved the resultingimages. For rigid transformation, Figures 4.7 - 4.9, the contours are in some casesfurther away than initially. The difference observed between these two transfor-mation types can depend on the way that the ground truth was created, since onlytranslation was allowed when manually aligning the bSSFP cine MR SA stack seg-mentation to the 4D Flow MR image. The contour was never rotated and thatcould be affecting the result.

In Figures 4.5 and 4.11, boxplots of the Dice index and the point-to-curve errorwere shown for all the datasets that were used. In Table 4.2 and 4.4 the maxi-mum and minimum numbers for the plots were shown. Here it can be seen thatthe data are very different between datasets. Consequently, it is hard for the reg-istration to fix all the problems. Some things that are misleading in this boxplotsare when the interpolated plane created from the 4D Flow MRI contains manyempty values, since the intersection between this image and the SA slice is small.For this slices, it was hard to decide where the heart was located. The contourwill then most likely be missed for that slice, which resulted in low Dice indexesand high point-to-curve errors.

From Figures 4.6 and 4.12, plots of the Dice index and the point-to-curve errorare shown for the different regions of the heart. In Table 4.3 and 4.5 the maxi-mum and minimum numbers for the plots are shown. Since the datasets havedifferent amounts of slices, different regions were used instead. It can be seen inthe plots that the groups do not have the same result. The registration did notwork optimally for the first group that corresponds to the apical part of the heart,but obtained better results for the other three. This is because the apex can notalways be seen in these images. Additionally, especially in these images, part ofthe SA slice is often only partially included in the 4D Flow MRI volume.

48 5 Discussion

A future work to solve this problem could be to apply different types of trans-formations on different parts of the heart. This could improve the correction,and parts of the heart that are more misaligned than others can benefit from amore adaptable method.

5.4 Evaluation of the results

Both intensity and geometric measures based methods can be to evaluate theresult. However, because of the large differences in the intensity distribution be-tween the images, intensity based evaluation did not reflect the true similaritiesbetween the compared images. Consequently, NCC was just used to reduce theamount of combinations and geometric methods were used for the evaluation ofthe registration results. A future work related to this is to find an intensity basedmethod that can also be used to further evaluate the final result.

5.5 Simulated misalignments

This evaluation method provided a better idea of the potential of the developedmethod. In Table 4.6 and Table 4.7, it can be seen that the registration works forboth large and small misalignments. The Dice index became higher than beforeand the point-to-curve errors became lower. In Figure 4.15, it can also be seenthat the alignment of the slices was improved. Additionally, improvements inthe smoothness of the contours can be seen in Figure 4.15a and 4.15b.

5.6 Implentation details

When the best parameter combination was selected, all the combinations wasvery similar and therefore the registrations that were less time consuming were se-lected. In this case, there was a difference between 345sec and 771sec in time be-tween the best and worse combination for a specific dataset. The system used forall experiments had following specifications: Intel Core i7-4770 CPU@ 3.4GHzand RAM 8 GB.

6Conclusions

In this thesis the feasibility of an automatic image registration method was testedto correct the misalignment in morphological cine MR images by using the 4DFlow MR as the reference image. The results from geometric evaluation on thedata from healthy volunteers and patients show that the proposed method is ableto mitigate the motion artefacts caused by both large and small respiratory move-ments during image morphological cine MRI acquisitions. As such it has thepotential to improve 4D flow MRI segmentation for a reliable assessment of car-diovascular blood flow.

49

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