ACCURATE INTELLIGENT & LIVE PREDICTIONS
BY THE APPLICATION OF NEURAL TOOL 6.1.2
TO HIGHLIGHT PATHOLOGICAL CHANGES IN
BRAIN WITHOUT THE USE OF INTRAVENOUS
CONTRAST INJECTION
TAPAN K BISWAS MD, D.M.R.D, F.R.C.R (1)
ACKNOWLEDGEMENT
FRE TRIAL VERSION OF
NEURAL TOOL-
CT SCANNER
BRAIN TUMOUR
CT IMAGE PLAIN
CONTRASTED
CONTRAST

Ionic or non ionic Iodinated contrast is the most
commonly used compounds for contrast
enhancement.
IODINE –has Atomic No.(Z-53)
Com plications




RADIATION HAZARD- May lead to Cancer
DRUG REACTION-Minor allergic reaction to life
thretning drug reaction
ONE DEATH OUT OF 200,000 CONTRAST INJECTION.
Contrast Mediated Nephrotoxicity (CMN) is another
complication- K idney Disease
CROSS SECTION OF CT SCANNER
WEIRED IDEA
ENHANCEMENT OF TUMOUR
W I THOUT THE I ODI NATED
CONTRAST I NJECTI ON
TIME MACHINE
TRAVEL TO FUTURE ??
TO RETRIEVE
ENHANCEMENT SIGNAL
DATA OF IMAGE
ACCURATELY
NEURAL NETWORK
WEIRED IDEA
CONTRAST ENHANCEMENT WITHOUT
IODINATED MEDICINE
LINEAR ATTENUATION
COEFFICIENT
INTERACTION OF XRAY WITH
BODY
TUMOR PHYSICS
Inter-action of X Ray with matter
X Ray-beam attenuated by tissue
In CT Scan main way is Compton Scattering
CT NUMBER
Explains Tissue Character

LINEAR ATTENUATION COEFFICIENT=µ

CT NUMBER =(Tissue µ− water µ) x 1000
water µ
HU=Hounsfield unit
IODINATED CONTRAST
IODINE-Atomic No.(Z-53)
1.IONIC  2.NON IONIC

SIGNAL SHADE IN IMAGE
CT NUMBER LOW
(-1000 TO 20 HU)


CT VALUE SHORT—
(26 TO 32 HU)

CT NUMBER HIGH

AFETR CONTRAST--(45 TO 140HU)
REQUIREMENTS
FOR SIMULATION TECHNIQUES
PRE CONTRAST CT VALUE OF TISSUES
 PRE CONTRAST SIGNAL SHADE
(INTENSITY) IN THE IMAGE
 POST CONTRAST CT VALUE OF TISSUE
 POST CONTRAST SIGNAL SHADE
IN THE IMAGE
NEURAL TOOL TO LIVE PREDICT THE
SHADES OF UNKNOWN TISSUE

TO DETERMINE CT NUMBER
SI=a (1-2e(-TI/T1) )-b).
POSTCONTRAST CT VALUE OF TISSUE
DECOMPOSITION OF PRE AND POST
CONTRAST IMAGES TO GET GRAY
SHADES
PRE AND POST CONTRAST CT NUMBER &
GRAY SHADES
TISSUE
CT NO1 GRAY SH GR SH
CT NO2
CSF
1
25
26
1
CSF
2
27
27
2
CSF
3
28
28
3
CSF
4
30
30
4
CSF
5
32
32
5
EDEMA
7
36
36
7
EDEMA
9
38
38
9
EDEMA
11
39
39
11
EDEMA
13
42
42
13
white mtr
16
44
46
18
white mtr
18
46
49
20
white mtr
19
48
51
22
white mtr
22
51
53
24
Graymattr
26
59
63
29
Graymattr
28
66
69
31
Graymattr
31
78
80
35
Graymattr
32
89
91
36
Graymattr
34
94
99
37
Graymattr
37
99
104
30
TUMOUR
42
108
124
44
TUMOUR
44
112
143
47
TUMOUR
46
120
147
50
TUMOUR
49
122
155
52
TUMOUR
54
126
159
58
RELATIONSHIP OF PRE & POST
CONTRAST CT VALUE
70
60
y = 1.061x + 0.050
50
40
Series1
Linear (Series1)
30
20
10
0
0
10
20
30
40
50
60
SEGMENTATION & K MEAN
CLUSTERING
By applying segmentation technique the grey
shade values (out of 256) of various parts
were determined and tabulated.
 K–mean clustering classified the data based
on attributes/features.
 Each attribute represented the CT value of
tissue before and after contrast and grey
shade value (out of 256 shades) as their
signal.

PRE & POST CON CT NUMBER AND
VALUES(K MEAN CLUSTERING)
SL
NEURAL NETWORK
Mathematical Program
SELECTION OF THE TIME
MACHINE
PALISADE DECISION TOOL
NEURAL TOOLS 6.1.2
BRAIN AND BRAIN TUMOUR
TRAINING OF DATA BY P ALI SADE
NEURAL TOOL 6.1.2
CSF
CSF
CSF
EDEMA
EDEMA
EDEMA
EDEMA
white mtr
white mtr
white mtr
white mtr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
TUMOUR
TUMOUR
TUMOUR
TUMOUR
TUMOUR
3
4
5
7
9
11
13
16
18
19
22
26
28
31
32
34
37
42
44
46
49
54
28
30
32
36
38
39
42
44
46
48
51
59
66
78
89
94
99
108
112
120
122
126
28
30
32
36
38
39
42
46
49
51
53
63
69
80
91
99
104
124
143
147
155
159
3
4
5
7
9
11
13
18
20
22
24
29
31
35
36
37
30
44
47
50
52
58
DATA SET MANAGER
TRAINED AND TESTED VALUES
CSF
CSF
CSF
EDEMA
EDEMA
EDEMA
EDEMA
white mtr
white mtr
white mtr
white mtr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
TUMOUR
TUMOUR
TUMOUR
TUMOUR
TUMOUR
3
4
5
7
9
11
13
16
18
19
22
26
28
31
32
34
37
42
44
46
49
54
28
30
32
36
38
39
42
44
46
48
51
59
66
78
89
94
99
108
112
120
122
126
28
30
32
36
38
39
42
46
49
51
53
63
69
80
91
99
104
124
143
147
155
159
3
4
5
7
9
11
13
18
20
22
24
29
31
35
36
37
30
44
47
50
52
58
test
train
train
train
train
train
train
train
train
train
train
test
test
train
train
test
train
train
train
train
train
test
3.15 Good
-0.15
35.97 Good
35.64 Good
-6.97
-4.64
32.77 Good
4.23
55.91 Good
2.09
TRAINING DATA
TESTING
Testing Report: "Net Trained on Data Set #16"
TISSUE
CSF
CSF
CSF
CSF
CSF
EDEMA
EDEMA
EDEMA
EDEMA
white mtr
white mtr
white mtr
white mtr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
TUMOUR
TUMOUR
TUMOUR
TUMOUR
TUMOUR
CT NO1
GRAY SH
1
2
3
4
5
7
9
11
13
16
18
19
22
26
28
31
32
34
37
42
44
46
49
54
CT NO2
25
27
28
30
32
36
38
39
42
44
46
48
51
59
66
78
89
94
99
108
112
120
122
126
GR SH
1
2
3
4
5
7
9
11
13
18
20
22
24
29
31
35
36
37
30
44
47
50
52
58
Tag Used
26
27
28
30
32
36
38
39
42
46
49
51
53
63
69
80
91
99
104
124
143
147
155
159
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
Prediction
Good/Bad
23.50Good
26.29Good
28.27Good
31.07Good
33.86Good
36.00Good
39.96Good
43.11Good
47.88Good
43.82Good
47.78Good
50.89Good
56.51Good
61.23Good
69.27Good
82.86Good
92.99Good
99.07Good
103.44Good
130.19Good
136.10Good
145.27Good
150.07Good
159.48Good
Residual
2.50
0.71
-0.27
-1.07
-1.86
0.00
-1.96
-4.11
-5.88
2.18
1.22
0.11
-3.51
1.77
-0.27
-2.86
-1.99
-0.07
0.56
-6.19
6.90
1.73
4.93
-0.48
LIVE PREDICTION OF UNKNOWN
INPUTS (PRECON CT)
CSF
CSF
CSF
CSF
EDEMA
EDEMA
EDEMA
EDEMA
white mtr
white mtr
white mtr
white mtr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
Graymattr
TUMOUR
TUMOUR
TUMOUR
TUMOUR
TUMOUR
2
3
4
5
7
9
11
13
16
18
19
22
26
28
31
32
34
37
42
44
46
49
54
27
28
30
32
36
38
39
42
44
46
48
51
59
66
78
89
94
99
108
112
120
122
126
2
3
4
5
7
9
11
13
18
20
22
24
29
31
35
36
37
30
44
47
50
52
58
27
28
30
32
36
38
39
42
46
49
51
53
63
69
80
91
99
104
predict
predict
predict
predict
predict
132.39
138.04
147.66
151.66
159.33
MAPPING OR SIMULATION OF
ENHANCEMENT
MAPPING OR CONTRAST
SIMULATION
PLAIN
CONTRAST
CONTRAST
SIMULATION
CONTRAST EFFECT OF THE
TUMOUR
PREDICTED VS ACTUAL
CT NUMBER(TRAINING)
Predicted vs. Actual (Training)
180
160
140
120
80
60
40
180
160
140
Actual
120
80
60
40
20
0
100
20
0
Predicted
100
R-Square (Training), Root mean Sq
error(training), Root mean Sq (Testing)
Linear Predictor vs. Neural Net
Linear Predictor
Neural Net
R-Square
(Training)
0.6228
—
Root Mean Sq. Error
(Training & prediction)
130 .30
0.0001502
ENHANCEMENT WITHOUT IV
CONTRAST
Determination pre-contrast CT VALUE
 Determination post contrast CT VALUE
 Segmentation to get signal shades of
images
 Grouping or K mean clustering of values
 Neural network to predict post contrast CT
VALUE or signal shades
 Mapping or simulation

ADVANTAGE OF VIRTUAL
CONTRAST LIKE EFFECT
1.No injection –so no hazard of drug
reaction or death
 No Kidney toxicity
 No extra cost of the Medicine or injection
 2.No re scan—so no extra radiation dose
and minimizing the chance of cancer.

? WEIRED IDEAS –THANKS TO
PALISADE NEURAL TOOL6.1.2