-
Notifications
You must be signed in to change notification settings - Fork 0
/
AIM-Template.py
222 lines (207 loc) · 11.5 KB
/
AIM-Template.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
#!/usr/local/bin/python
__author__ = 'Nolan Nichols'
#module to parse xml
from lxml import etree
#module to work with NIfTi images
import nibabel as nb
#module to open rdf at QueryService url
from urllib2 import urlopen
#module to work with rdf mapping between AAL and FMAIDs
from rdflib import ConjunctiveGraph as Graph
#open the aal atlas as a single analyze volume in MNI152 space with integers from 1 - 116 for anatomy labels
def readAAL():
aal = nb.load('/Users/nolan/Documents/UW/GrantApps/Neuroimaging-Ontology/AIM-NIfTI/aal2mni152.nii.gz')
aalArray = aal.get_data()
return aalArray
#open a fMRI zScore map for a single participant that is already in MNI space
def readFMRI(fmriFile):
fmri = nb.load(fmriFile) #'/Users/nolan/Documents/UW/GrantApps/Neuroimaging-Ontology/AIM-NIfTI/000640456777_visit2_zstat1.nii'
fmriArray = fmri.get_data()
return fmriArray
#return a list of inputs to configure aimInstance based on anatomical entity (i.e., aalID)
def voxelsByLabel(aalArray, aalID, fmriArray):
'''atlas is a NiBabel 3D array in MNI space. aalID is an integer 1-116 corresponding to a brain structure'''
xyzLabels = [] #list that stores 5 items... (x,y,z coordinates, aalID, and zScore)
for x in range(aalArray.shape[0]):
for y in range(aalArray.shape[1]):
for z in range(aalArray.shape[2]):
if aalArray[x,y,z] == aalID: #only append coordinates for specific aalIDs
xyzLabels.append((x, y, z, aalArray[x,y,z], fmriArray[x,y,z]))
return xyzLabels
#return an AIM instance based on the required coordinates, labels, and statistics
def aimInstance(x, y, z, fmaid, fmaLabel, zScore, cagridId):
''' '''
#Global variables for AIM template
XSI_NS = 'http://www.w3.org/2001/XMLSchema-instance'
XSI_TYPE = '{%s}type' % XSI_NS
#create an AIM ImageAnnotation template, ImageAnnotation sub-tree w/attributes
ImageAnnotation = etree.Element('ImageAnnotation')
ImageAnnotation.attrib['aimVersion'] = '3.0'
ImageAnnotation.attrib['cagridId'] = cagridId
ImageAnnotation.attrib['codeMeaning'] = 'fMRI brain data'
ImageAnnotation.attrib['codeValue'] = 'PIXEL DATA'
ImageAnnotation.attrib['codingSchemeDesignator'] = 'PIXELDATA'
ImageAnnotation.attrib['dateTime'] = ''
ImageAnnotation.attrib['name'] = 'Example AIM file'
ImageAnnotation.attrib['uniqueIdentifier'] = ''
#configure namespaces
schemaLocation = '{%s}schemaLocation' % XSI_NS
ImageAnnotation.attrib[schemaLocation] = 'gme://caCORE.caCORE/3.2/edu.northwestern.radiology.AIM AIM_v3_rv11_XML.xsd'
#create an AIM ImageAnnotation template - calculationCollection sub-tree w/attributes
calculationCollection = etree.SubElement(ImageAnnotation, 'calculationCollection')
Calculation = etree.SubElement(calculationCollection, 'Calculation')
Calculation.attrib['cagridId'] = cagridId
Calculation.attrib['codeMeaning'] = 'Z Score'
Calculation.attrib['codeValue'] = 'ZSCORE'
Calculation.attrib['codingSchemeDesignator'] = 'FMRI'
Calculation.attrib['description'] = 'Z Score'
Calculation.attrib['uid'] = ''
#create an AIM ImageAnnotation template - referencedCalculationCollection *UNUSED*
referencedCalculationCollection = etree.SubElement(Calculation, 'referencedCalculationCollection')
#create an AIM ImageAnnotation template - calculationResultCollection sub-tree w/attributes
calculationResultCollection = etree.SubElement(Calculation, 'calculationResultCollection')
CalculationResult = etree.SubElement(calculationResultCollection, 'CalculationResult')
CalculationResult.attrib['cagridId'] = cagridId
CalculationResult.attrib['numberOfDimensions'] = '1'
CalculationResult.attrib['type'] = 'Scalar'
CalculationResult.attrib['unitOfMeasure'] = 'Zscore'
calculationDataCollection = etree.SubElement(CalculationResult, 'calculationDataCollection')
CalculationData = etree.SubElement(calculationDataCollection, 'CalculationData')
CalculationData.attrib['cagridId'] = cagridId
CalculationData.attrib['value'] = zScore.__str__() # fMRI z-score goes for a single coordinate
#create an AIM ImageAnnotation template - coordinateCollection sub-tree w/attributes
coordinateCollection = etree.SubElement(CalculationData,'coordinateCollection')
Coordinate = etree.SubElement(coordinateCollection, 'Coordinate')
Coordinate.attrib['cagridId'] = cagridId
Coordinate.attrib['dimensionIndex'] = '0'
Coordinate.attrib['position'] = '0'
#create an AIM ImageAnnotation template - dimensionCollection sub-tree w/attributes
dimensionCollection = etree.SubElement(CalculationResult,'dimensionCollection')
Dimension = etree.SubElement(dimensionCollection, 'Dimension')
Dimension.attrib['cagridId'] = cagridId
Dimension.attrib['index'] = '0'
Dimension.attrib['label'] = 'Value'
Dimension.attrib['size'] = '1'
#create an AIM ImageAnnotation template - user sub-tree w/attributes
user = etree.SubElement(ImageAnnotation, 'user')
User = etree.SubElement(user, 'User')
User.attrib['cagridId'] = cagridId
User.attrib['loginName'] = 'TEST'
User.attrib['name'] = 'TEST'
User.attrib['numberWithinRoleOfClinicalTrial'] = '2'
User.attrib['roleInTrial'] = 'N/A'
#create an AIM ImageAnnotation template - equipment sub-tree w/attributes
equipment = etree.SubElement(ImageAnnotation, 'equipment')
Equipment = etree.SubElement(equipment, 'Equipment')
Equipment.attrib['cagridId'] = cagridId
Equipment.attrib['manufacturerModelName'] = 'N/A'
Equipment.attrib['manufacturerName'] = 'University'
Equipment.attrib['softwareVersion'] = '3.0.0.0'
#create an AIM ImageAnnotation template - anatomicEntityCollection sub-tree w/attributes
anatomicEntityCollection = etree.SubElement(ImageAnnotation, 'anatomicEntityCollection')
AnatomicEntity = etree.SubElement(anatomicEntityCollection, 'AnatomicEntity')
AnatomicEntity.attrib['annotatorConfidence'] = '1'
AnatomicEntity.attrib['cagridId'] = cagridId
AnatomicEntity.attrib['codeMeaning'] = fmaLabel #this info exists outside of atlas
AnatomicEntity.attrib['codeValue'] = fmaid #changed from RID
AnatomicEntity.attrib['codingSchemeDesignator'] = 'FMAID' #change to AAL or FMA
AnatomicEntity.attrib['isPresent'] = 'True'
AnatomicEntity.attrib['label'] = 'Pixel in %s' % fmaLabel #should be 'Pixel in '+ %s codeMeaning
#create an AIM ImageAnnotation template - imageReferenceCollection sub-tree w/attributes
imageReferenceCollection = etree.SubElement(ImageAnnotation, 'imageReferenceCollection')
ImageReference = etree.SubElement(imageReferenceCollection,'ImageReference')
ImageReference.attrib['cagridId'] = cagridId
ImageReference.attrib[XSI_TYPE] = 'DICOMImageReference'
#create an AIM ImageAnnotation template - imageStudy sub-tree w/attributes
imageStudy = etree.SubElement(ImageReference, 'imageStudy')
ImageStudy = etree.SubElement(imageStudy, 'ImageStudy')
ImageStudy.attrib['cagridId'] = cagridId
ImageStudy.attrib['instanceUID'] = ''
ImageStudy.attrib['startDate'] = ''
ImageStudy.attrib['startTime'] = ''
#create an AIM ImageAnnotation template - imageSeries sub-tree w/attributes
imageSeries = etree.SubElement(ImageStudy,'imageSeries')
ImageSeries = etree.SubElement(imageSeries, 'ImageSeries')
ImageSeries.attrib['cagridId'] = cagridId
ImageSeries.attrib['instanceUID'] = ''
#create an AIM ImageAnnotation template - imageCollection sub-tree w/attributes
imageCollection = etree.SubElement(ImageSeries, 'imageCollection')
Image = etree.SubElement(imageCollection, 'Image')
Image.attrib['cagridId'] = cagridId
Image.attrib['sopClassUID'] = ''
Image.attrib['sopInstanceUID'] = ''
#create an AIM ImageAnnotation template - geometricShapeCollection sub-tree w/attributes
geometricShapeCollection = etree.SubElement(ImageAnnotation, 'geometricShapeCollection')
GeometricShape = etree.SubElement(geometricShapeCollection, 'GeometricShape')
GeometricShape.attrib['cagridId'] = cagridId
GeometricShape.attrib['includeFlag'] = 'true'
GeometricShape.attrib['shapeIdentifier'] = '0'
GeometricShape.attrib[XSI_TYPE] = 'Point'
#create an AIM ImageAnnotation template - spatialCoordinateCollection sub-tree w/attributes
spatialCoordinateCollection = etree.SubElement(GeometricShape, 'spatialCoordinateCollection')
SpatialCoordinate = etree.SubElement(spatialCoordinateCollection, 'SpatialCoordinate')
SpatialCoordinate.attrib['cagridId'] = cagridId
SpatialCoordinate.attrib['coordinateIndex'] = '0'
SpatialCoordinate.attrib['imageReferenceUID'] = ''
SpatialCoordinate.attrib['referencedFrameNumber'] = '1'
SpatialCoordinate.attrib['x'] = x.__str__()
SpatialCoordinate.attrib['y'] = y.__str__()
SpatialCoordinate.attrib['z'] = z.__str__()
SpatialCoordinate.attrib[XSI_TYPE] = 'ThreeDimensionSpatialCoordinate' #updated for 3D
#create an AIM ImageAnnotation template - person sub-tree w/attributes
person = etree.SubElement(ImageAnnotation,'person')
Person = etree.SubElement(person, 'Person')
Person.attrib['cagridId'] = cagridId
Person.attrib['id'] = 'NIF'
Person.attrib['name'] = ''
return ImageAnnotation
#read in rdf graph that maps AAL IDs and FMAIDs
#fmaURL = urlopen('http://xiphoid.biostr.washington.edu:8080/ValueSetService/ValueSet?qid=101')
fmaGraph = Graph()
fmaGraph.parse('/Users/nolan/Documents/UW/GrantApps/Neuroimaging-Ontology/AIM-NIfTI/AAL-FMA-Mapping.rdf')
#parse rdf into list
def fma2List(fmaGraph):
fmaList = list()
for s,p,o in fmaGraph:
fmaList.append((str(s),str(p),str(o)))
return sorted(fmaList) #sorting ensures correct indexing of aalIDs, fmaids, labels, etc in aalDict.
#organize fmaList into nested sets
def list2Nested(fmaList):
aalList = list()
while fmaList:
tempList = list()
for i in range(4):
tempList.append(fmaList.pop())
aalList.append((tempList[0][2],tempList[1][2],tempList[2][2],tempList[3][2]))
return aalList
#index the aal2Fma mapping as a dict keyed on the aalIDs for searching by aalID
def aalMap(aalList):
aalDict = dict()
for i in range(len(aalList)):
aalTemp = aalList.pop()
aalDict[aalTemp[3]] = list((aalTemp[0],aalTemp[1],aalTemp[2]))
return aalDict
#iterate through voxelsByLabel using the lookup indices as input to configure AIM instance
def aimGen (xyzLabels):
AIM_ROOT = etree.Element('AIM-ROOT') #create a root AIM element for ImageAnnotations returned by aimInstance
aalDict = aalMap(list2Nested(fma2List(fmaGraph))) #dict to look up FMAID by aalID
while xyzLabels:
record = xyzLabels.pop()
x = record[0]
y = record[1]
z = record[2]
aalID = record[3]
fmaid = aalDict[str(aalID)][2] #mapping between aalID and fmaid
fmaLabel = aalDict[str(aalID)][0]
zScore = record[4]
cagridId = '%s-%s-%s' % (x,y,z)
ImageAnnotation = aimInstance(x, y, z, fmaid, fmaLabel, zScore, cagridId)
AIM_ROOT.append(ImageAnnotation)
aimTree = etree.ElementTree(AIM_ROOT) #create an element tree from AIM_ROOT returned from aimInstance
return aimTree
def writeAIM (aalID,fmriArray):
aimTree = aimGen(voxelsByLabel(readAAL, aalID, readFMRI(fmriArray)))
aimTree.write('%s/AIM-%s.xml' % ('/Users/nolan/Documents/UW/GrantApps/Neuroimaging-Ontology/AIM-NIfTI/scratch', aalID)) #write out the aim file
return
#directory to write AIM file to
aimFile = '/Users/nolan/Documents/UW/GrantApps/Neuroimaging-Ontology/AIM-NIfTI/scratch'