PET Brain Scan Sonification to Augment the Diagnosis of Alzheimer's Disease

80 Pages·2014·5.78 MB·English

by  Hariharan Mohanraj

PET Brain Scan Sonification
to Augment the Diagnosis of
Alzheimer’s Disease

Hariharan Mohanraj

Submitted in partial fulfillment of the requirements for the
Master of Music in Music Technology
in the Department of Music and Performing Arts Professions
in the Steinhardt School
New York University
Advisor: Dr. Agnieszka Roginska
2013/12/06
Acknowledgements
The work presented in this thesis is not only the product of my effort, but also a product of the
support of countless individuals around me. I would like to thank my advisor, Dr. Agnieszka
Roginska, for welcoming me aboard her research project, a portion of which constitutes this
thesis. For being a teacher and a guide through the entirety of my graduate education. For
pushing me to my full potential. I would like to thank Dr. Kent P. Friedman, who was an anchor
of medical expertise through this project. I would like to thank James Paul Keary, who has had
just as much a hand in this work as I have. His efforts were key to taking this project from theory
and prototypes to fully functional tools.
I would like to thank Braxton Boren, Areti Andreopoulou, Andrew Madden, and Justin Mathew
for being constant role models in the field of research. I would like to thank the sixth floor
“gang”, especially Alex Marse, John Turner, Amar Lal, and Jeet Paul, for their invaluable
insight, conversation, and unwavering willingness to grab a cup of coffee.
I would like to thank my parents for their unconditional support. For never doubting me, even
when my faith in myself wavered. I would like to thank my sister, who could always lend a
listening ear when work got the better of me. Lastly, to the scores of unnamed friends and
colleagues who supported me along the way, though this page may not have room for everyone,
you are all remembered in my heart and you have my gratitude.

This research is supported in part by the National Center for Advancing Translational Sciences,
National Institutes of Health.
Table of Contents
Glossary .......................................................................................................................................... 8

1.
  Introduction ............................................................................................................................ 9

1.1.
  Sonification ....................................................................................................................... 9

1.2.
  Diagnosis of Alzheimer’s Disease ................................................................................. 10

2.
  Literature Review ................................................................................................................. 12

2.1.
  Sonification ..................................................................................................................... 12

2.1.1.
  Discrete Sonification ................................................................................................. 13

2.1.2.
  Continuous Sonification ............................................................................................ 14

2.2.
  Applications of Sonification in Medicine ..................................................................... 16

2.3.
  Medical Background ...................................................................................................... 18

2.3.1.
  Multi-dimensional datasets ....................................................................................... 18

2.3.2.
  Positron Emission Tomography ................................................................................ 18

2.3.3.
  Alzheimer’s Disease .................................................................................................. 20

2.4.
  Preliminary Work .......................................................................................................... 22

2.4.1.
  Modular sonification approach ................................................................................. 23

2.4.2.
  Preliminary Sonification Techniques ........................................................................ 30

2.4.3.
  Evaluation of Initial Techniques ............................................................................... 32

3.
  Methodology .......................................................................................................................... 34

3.1.
  Triple-Tone Sonification Technique ............................................................................ 34

3.2.
  Data ................................................................................................................................. 35

3.3.
  Lobe Segmentation ........................................................................................................ 37

3.4.
  Averaging and Masking ................................................................................................ 39

3.5.
  Frequency Mapping ....................................................................................................... 39

3.6.
  Positive Compression ..................................................................................................... 41

3.7.
  Data Subsets ................................................................................................................... 42

3.8.
  Implementation .............................................................................................................. 43

3.9.
  Data Analysis .................................................................................................................. 46

4.
  Evaluation ............................................................................................................................. 51
4.1.
  Goal of Evaluation ......................................................................................................... 51

4.2.
  Subjective Test ............................................................................................................... 51

4.2.1.
  Training ..................................................................................................................... 52

4.2.2.
  Coarse Categorization ............................................................................................... 53

4.2.3.
  Fine Categorization ................................................................................................... 53

5.
  Results .................................................................................................................................... 54

5.1.
  Coarse Categorization Responses ................................................................................. 54

5.2.
  Fine Categorization Responses ..................................................................................... 55

5.3.
  Response Accuracy ........................................................................................................ 55

5.4.
  Response Consistency .................................................................................................... 57

5.5.
  Effect of Fine Categorization on Accuracy .................................................................. 58

5.6.
  Discussion ....................................................................................................................... 60

6.
  Conclusion ............................................................................................................................. 62

6.1.
  Summary of Results ....................................................................................................... 62

6.2.
  Future Work ................................................................................................................... 62

6.2.1.
  Data Preparation ........................................................................................................ 62

6.2.2.
  Physician Testing ...................................................................................................... 62

6.2.3.
  Dynamic Detune Factor ............................................................................................ 63

6.2.4.
  Integrated Software Tool ........................................................................................... 63

6.3.
  Concluding Remarks ..................................................................................................... 64

References .................................................................................................................................... 65

Appendix ...................................................................................................................................... 68

Relative deviation and frequency values for all test cases ................................................... 68

Complete Set of Responses – Subjective Test ........................................................................ 69

Complete Set of Response Consistency .................................................................................. 75
Table of Figures
Figure 2.1 Classification of voxel ranges (Rossiter & Ng, 1996) ................................................. 16

Figure 2.2 Schematic Diagram of PET Scanner ............................................................................ 19

Figure 2.3 3-D DICOM dataset yields 2-D cross-sections and external perspective views of the
brain ....................................................................................................................................... 20

Figure 2.4 Figure illustrating the different lobes of the human brain ........................................... 21

Figure 2.5 Screenshot of the MATLAB sonification prototype tool ............................................. 24

Figure 2.6 Scan paths of initial sonification techniques ................................................................ 25

Figure 2.7 Frequency mapping curve ............................................................................................ 28

Figure 2.8 Stereo equal-energy amplitude panning curves ........................................................... 29

Figure 3.1 Figures illustrating the source of a) the lateral slice and b) the 3D slice ..................... 36

Figure 3.2 Lobe segmentation of lateral slice ............................................................................... 38

Figure 3.3 Lobe segmentation of 3D slice ..................................................................................... 38

Figure 3.4 Excerpt of text format file ............................................................................................ 44

Figure 3.5 Screenshot of SuperCollider interface for sonification playback and recording ......... 46

Figure 3.6 Average deviations of frontal and parietal lobes in lateral slices ................................. 47

Figure 3.7 Average deviations of frontal and parietal lobes in 3D slices ...................................... 47

Figure 3.8 Absolute deviations of lateral slices ............................................................................. 48

Figure 3.9 Absolute deviations of 3D slices .................................................................................. 48

Figure 3.10 Frequencies of frontal and parietal lobes - Lateral slices ........................................... 49

Figure 3.11 Frequencies of frontal and parietal lobes - 3D slices ................................................. 50
Figure 5.1 Means and standard errors of coarse categorization responses .................................... 54

Figure 5.2 Means and standard errors of fine categorization responses ........................................ 55
Table of Tables
Table 1 List of coordinates utilized in lobe segmentation ............................................................. 38

Table 2 List of detune factor values .............................................................................................. 41

Table 3 List of data subsets presented column-wise ..................................................................... 42

Table 4 Accuracy of subjects in coarse categorization .................................................................. 56

Table 5 Accuracy of subjects in fine categorization ...................................................................... 56

Table 6 Consistency of subjects in coarse categorization ............................................................. 57

Table 7 Consistency of subjects in fine categorization ................................................................. 57

Table 8 Subject-wise consistency percentage for coarse and fine categorization ......................... 58

Table 9 Side-by-side comparison of subject accuracy for coarse vs fine categorization .............. 58

Table 10 Subject responses to Case Mild 7 in both coarse and fine categorizations .................... 59
Glossary
Some terms relating to aspects of medicine and the sonification of PET brain scans that appear
frequently in this thesis are defined below.
DICOM: Standardized file format for the storage of medical scans.
Voxel: A datapoint in a 3-dimensional DICOM dataset. The 3-dimensional analogue to a pixel
(volumetric pixel).
Lateral Slice: A cross-section of a brain scan along the plane that would considered horizontal if
the patient were standing.
3D Slice: A 2-dimensional screen-capture of an exterior-perspective view of the left and right
sides of the brain, used for sonification.
Hypometabolism: Decreased metabolic activity (in a region of the brain).
Lobe of Interest: Lobe of the brain that is generally affected by Alzheimer’s disease, where
hypometabolism is expected in diseased brains.
Reference Lobe: Lobe of the brain that is generally not affected by Alzheimer’s disease.
Relative Deviation: The fractional difference of one quantity from another. The relative
deviation of A with respect to B is (A-B)/B.

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1. Introduction
The common adage states that information is power. When talking about information, it is
important to separate the concept of data from the concept of representation of data. It is from the
appropriate representation of data that inferences can be drawn, thus contributing to information.
In today’s world, the visual domain is the leading mode of data representation. Popular data
handling packages and computing software libraries, such as Microsoft Excel and MATLAB, are
equipped with tools to represent data through 2-D or 3-D plots, bar graphs, pie charts,
histograms, box plots, etc. However, in recent times, several studies have begun to challenge the
notion that the visual domain is the best mode of data representation. One alternate mode of
representation that is being explored extensively today is the aural domain.
1.1. Sonification
Sonification can be defined concisely (Hermann T. , Taxonomy and Definitions for Sonification
and Auditory Display, 2008) as the conveyance of information through non-speech sound. This
definition of sonification encompasses sound information ranging from a simple beep signifying
the completion of an action on a handheld device to complex textures of sound representing
multi-dimensional datasets. Sonification uses the ears as the primary perceptive device, and aims
at leveraging the ears’ perceptual advantages over other perceptive devices (primarily the eyes).
Sonification is currently employed in a wide range of applications. Some of its applications
relate to day-to-day activities. However, sonification has also found uses in more scientific and
analytical contexts, such as the analysis of stock market data (Ciardi, 2004). Some applications
of sonification in the scientific field are also driven by aesthetic motivations. For example, the
data collected by the Large Hadron Collider’s ATLAS experiment to discover the Higgs boson
was converted to a musical score by composer, physicist, and engineer Domenico Vicinanza.
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1.2. Diagnosis of Alzheimer’s Disease
Diagnosis of medical conditions has been revolutionized with the advent of advanced medical
imaging techniques such as X-rays, magnetic resonance imaging (MRI), and positron emission
tomography (PET) imaging techniques. These techniques provide multi-dimensional and time-
varying datasets. The representation of these datasets to physicians for diagnosis is carried out
primarily in the visual domain. Current software tools (eg. MIM Software, OsiriX) allow
physicians to project these datasets in 3-dimensional views, or parse through the datasets one 2-
dimensional layer at a time. These visualization tools also allow the physician to tweak several
parameters of the data representation. For example, the physician can zoom into a particular sub-
section of a dataset, or adjust the color maps that govern the representation of a dataset.
Although medical imaging has greatly advanced diagnostic capabilities, it is not without
limitations. There still remains large inconsistency in the diagnosis of human brain conditions
among various physicians upon mere visual inspection of medical imaging datasets. Efforts have
been made in recent years to explore alternative methods of representation of medical imaging
data to improve the accuracy and consistency of diagnoses. One such technique involves
supplementing visual representation with quantitative analysis of the dataset (Piper, 2007)
(Nelson, Piper, Friedland, & Freeman, 2007). In the case of the human brain, based on a standard
database of healthy brains, a dataset can be spatially warped to fit to a standard human brain
model. The brain can then be divided into individual lobes. Additionally, statistical analysis tools
compare the brain to the standard database and the standard deviations of the data values of
various lobes of the brain are presented in a table of numbers to the physician. The physician can
then make a diagnosis based on both visual inspection and the statistical analysis.
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