Signal Processing - May 2016 - 73
time and susceptibility to artifacts when using a large FOV,
which limits their application to only brain imaging [37]. A
number of studies have shown that ignoring bone might not
be adequate for quantification of osseous lesions with bias in
estimation in tracer uptake [standardized uptake value (SUV)],
varying between 5 and 15% in most cases but going up to 30%
in some cases [40]-[45].
The second category of approaches consists of using representative anatomical atlas registration, in which an MRI
template is registered to a patient's MRI, and prior knowledge
of the atlas attenuation properties, obtained by registration
to a corresponding CT template combined with a learning
algorithm based on the use of support vector machines, is
applied to derive a patient-specific attenuation map [46]. Reliable deformable registration algorithms play a pivotal role in
this approach, and failure of the registration process in the
case of large deformations will produce incorrect results [47].
The critical issue is the extent to which the global anatomy
depicted by an atlas will predict individual and patient-specific attenuation maps. For this and a few other reasons, most
techniques proposed so far that belong to this category were
developed specifically for brain imaging [48], [49]. Adaptation of these techniques for whole-body imaging applications
required few modifications to be made, consisting mainly
of generating a four-class segmentation of the MR images
to improve the registration process and optimal selection of
techniques, in which an aligned MR/CT atlas combined with
a learning strategy enables the prediction of the pseudo-CT
from an actual patient's MR image; and 3) emission-based and
transmission-based algorithms, in which the TOF emission or
transmission data are exploited to derive the attenuation map
(Figure 5) [37].
Segmentation-based methods are simple to implement and
usually require a single and fast MRI sequence. However, they
suffer from limited accuracy in the determination of attenuation coefficients owing to the limited number of segmented
clusters (usually three to five, including air, lungs, fat, soft
tissue, and fat/nonfat mixture) and the assignment of theoretical rather than actual patient-specific attenuation coefficients.
In these techniques, bones and air pockets are replaced by soft
tissue, and the variability of attenuation coefficients is ignored,
especially in the lungs. Tissues such as bone and lung and various pathological abnormalities with varying attenuations are
among the most challenging in whole-body imaging. With
the exception of the use of ultrashort echo time [38] and zero
echo time [39] pulse sequences, cortical bone has very low
signal intensity on conventional MRI sequences and is difficult to distinguish from air cavities and gas in the body. These
sequences were designed to portray tissues with low proton
density and short T2 relaxation time (e.g., cortical bone and
lungs) and, as such, to separate the bone signal from soft tissue.
The main drawback of these techniques is the long acquisition
MRI
Four-Class MRAC
Atlas-Based Registration
Emission-Based Technique (MLAA)
FIGuRE 5. Strategies for MRI-guided attenuation map generation, including the four-class segmentation-based method, atlas-based registration and machine
learning, and MRI-guided emission-based technique (MLAA). MRAC: MRI-based attenuation correction. (Figure adapted with permission from [37].)
IEEE Signal Processing Magazine
|
May 2016
|
73
Table of Contents for the Digital Edition of Signal Processing - May 2016
Signal Processing - May 2016 - Cover1
Signal Processing - May 2016 - Cover2
Signal Processing - May 2016 - 1
Signal Processing - May 2016 - 2
Signal Processing - May 2016 - 3
Signal Processing - May 2016 - 4
Signal Processing - May 2016 - 5
Signal Processing - May 2016 - 6
Signal Processing - May 2016 - 7
Signal Processing - May 2016 - 8
Signal Processing - May 2016 - 9
Signal Processing - May 2016 - 10
Signal Processing - May 2016 - 11
Signal Processing - May 2016 - 12
Signal Processing - May 2016 - 13
Signal Processing - May 2016 - 14
Signal Processing - May 2016 - 15
Signal Processing - May 2016 - 16
Signal Processing - May 2016 - 17
Signal Processing - May 2016 - 18
Signal Processing - May 2016 - 19
Signal Processing - May 2016 - 20
Signal Processing - May 2016 - 21
Signal Processing - May 2016 - 22
Signal Processing - May 2016 - 23
Signal Processing - May 2016 - 24
Signal Processing - May 2016 - 25
Signal Processing - May 2016 - 26
Signal Processing - May 2016 - 27
Signal Processing - May 2016 - 28
Signal Processing - May 2016 - 29
Signal Processing - May 2016 - 30
Signal Processing - May 2016 - 31
Signal Processing - May 2016 - 32
Signal Processing - May 2016 - 33
Signal Processing - May 2016 - 34
Signal Processing - May 2016 - 35
Signal Processing - May 2016 - 36
Signal Processing - May 2016 - 37
Signal Processing - May 2016 - 38
Signal Processing - May 2016 - 39
Signal Processing - May 2016 - 40
Signal Processing - May 2016 - 41
Signal Processing - May 2016 - 42
Signal Processing - May 2016 - 43
Signal Processing - May 2016 - 44
Signal Processing - May 2016 - 45
Signal Processing - May 2016 - 46
Signal Processing - May 2016 - 47
Signal Processing - May 2016 - 48
Signal Processing - May 2016 - 49
Signal Processing - May 2016 - 50
Signal Processing - May 2016 - 51
Signal Processing - May 2016 - 52
Signal Processing - May 2016 - 53
Signal Processing - May 2016 - 54
Signal Processing - May 2016 - 55
Signal Processing - May 2016 - 56
Signal Processing - May 2016 - 57
Signal Processing - May 2016 - 58
Signal Processing - May 2016 - 59
Signal Processing - May 2016 - 60
Signal Processing - May 2016 - 61
Signal Processing - May 2016 - 62
Signal Processing - May 2016 - 63
Signal Processing - May 2016 - 64
Signal Processing - May 2016 - 65
Signal Processing - May 2016 - 66
Signal Processing - May 2016 - 67
Signal Processing - May 2016 - 68
Signal Processing - May 2016 - 69
Signal Processing - May 2016 - 70
Signal Processing - May 2016 - 71
Signal Processing - May 2016 - 72
Signal Processing - May 2016 - 73
Signal Processing - May 2016 - 74
Signal Processing - May 2016 - 75
Signal Processing - May 2016 - 76
Signal Processing - May 2016 - 77
Signal Processing - May 2016 - 78
Signal Processing - May 2016 - 79
Signal Processing - May 2016 - 80
Signal Processing - May 2016 - 81
Signal Processing - May 2016 - 82
Signal Processing - May 2016 - 83
Signal Processing - May 2016 - 84
Signal Processing - May 2016 - 85
Signal Processing - May 2016 - 86
Signal Processing - May 2016 - 87
Signal Processing - May 2016 - 88
Signal Processing - May 2016 - 89
Signal Processing - May 2016 - 90
Signal Processing - May 2016 - 91
Signal Processing - May 2016 - 92
Signal Processing - May 2016 - 93
Signal Processing - May 2016 - 94
Signal Processing - May 2016 - 95
Signal Processing - May 2016 - 96
Signal Processing - May 2016 - 97
Signal Processing - May 2016 - 98
Signal Processing - May 2016 - 99
Signal Processing - May 2016 - 100
Signal Processing - May 2016 - 101
Signal Processing - May 2016 - 102
Signal Processing - May 2016 - 103
Signal Processing - May 2016 - 104
Signal Processing - May 2016 - 105
Signal Processing - May 2016 - 106
Signal Processing - May 2016 - 107
Signal Processing - May 2016 - 108
Signal Processing - May 2016 - 109
Signal Processing - May 2016 - 110
Signal Processing - May 2016 - 111
Signal Processing - May 2016 - 112
Signal Processing - May 2016 - 113
Signal Processing - May 2016 - 114
Signal Processing - May 2016 - 115
Signal Processing - May 2016 - 116
Signal Processing - May 2016 - 117
Signal Processing - May 2016 - 118
Signal Processing - May 2016 - 119
Signal Processing - May 2016 - 120
Signal Processing - May 2016 - Cover3
Signal Processing - May 2016 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201809
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201807
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201805
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201803
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201801
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0917
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0717
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0517
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0317
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0916
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0716
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0516
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0316
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0915
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0715
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0515
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0315
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0914
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0714
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0514
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0314
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0913
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0713
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0513
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0313
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0912
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0712
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0512
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0312
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0911
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0711
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0511
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0311
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0910
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0710
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0510
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0310
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0909
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0709
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0509
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0309
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1108
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0908
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0708
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0508
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0308
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0108
https://www.nxtbookmedia.com