DTI Brain Template Construction Based on Gaussian Averaging

doi:10.11938/cjmr20212957

Abstract

Abstract:

The tensor data of subjects are usually averaged linearly over multiple channels to obtain the tensor template. However, linear averaging ignores the vector information in the tensor. Additionally, it will render the interface between the gray matter and white matter too smooth, resulting in resolution reduction. To address the above problems, this paper introduced quaternion and Gaussian weighted average to construct a Gaussian diffusion tensor imaging (DTI) brain template. First, the DTI data of 55 healthy subjects were preprocessed to minimize data artifacts. The obtained data were then subjected to preliminary spatial standardization. Then, the tensor was decomposed to acquire eigenvectors and eigenvalues. Finally, the eigenvalues and quaternion converted from the eigenvectors were followed by Gaussian weighted average to gain the averaged eigenvectors and eigenvalues. The tensor template was obtained by reconstructing the averaged eigenvectors and eigenvalues. The experimental results show that compared with the linear DTI template, the Gaussian DTI template performs better on the DTED, COH, DVED, OVL, and corr_FA evaluation indicators but poorer on the IA indicator. The Gaussian DTI template proposed in this paper has certain advantage on the overall information retention, but is to be further improved on the orientation information.

Key words: diffusion magnetic resonance imaging, brain template, DTI registration, Gaussian average

CLC Number:

O482.53

Lan DENG,Yuan-jun WANG. DTI Brain Template Construction Based on Gaussian Averaging[J]. Chinese Journal of Magnetic Resonance, 2022, 39(4): 413-427.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Table 2

DTI Brain template parameter evaluation

被试编号	DTED/(×10⁴)		COH		DVED/(×10⁵)		OVL		corr_FA/(×10^?1)
被试编号	Gauss	linear	Gauss	linear	Gauss	linear		linear	Gauss	linear
1	0.141±0.428	3.83±10.2	0.029±0.055	0.026±0.061	2.61±7.93	44.6±126	0.152±2.80	0.148±4.38	6.31	6.30
2	0.128±0.396	3.81±10.1	0.084±0.024	0.028±0.053	2.65±8.01	44.6±126	0.156±2.65	0.151±8.91	7.14	7.13
3	0.131±0.397	3.82±10.2	0.026±0.056	0.091±0.036	2.70±8.15	44.7±130	0.145±2.30	0.142±2.87	6.70	6.69
4	0.128±0.391	3.82±10.2	0.045±0.058	0.028±0.031	2.65±7.98	44.7±126	0.150±4.36	0.118±43.3	6.98	6.97
5	0.130±0.397	3.83±10.2	0.037±0.036	0.031±0.022	2.67±8.05	44.7±126	0.155±1.24	0.158±2.78	6.84	6.83
6	0.128±0.393	3.83±10.2	0.054±0.023	0.041±0.041	2.84±8.62	44.8±127	0.140±1.34	0.139±2.67	6.61	6.60
7	0.123±0.380	3.84±10.2	0.023±0.010	0.139±0.025	2.74±8.29	44.7±126	0.148±1.19	0.147±0.95	7.11	7.10
8	0.131±0.400	3.83±10.2	0.050±0.063	0.080±0.036	2.70±8.17	44.7±126	0.141±6.34	0.155±15.1	6.74	6.73
9	0.133±0.407	3.82±10.2	0.047±0.050	0.033±0.058	2.65±8.00	44.7±126	0.148±5.28	0.151±0.24	6.83	6.82
10	0.123±0.380	3.82±10.1	0.142±0.008	0.010±0.026	2.77±8.34	44.8±127	0.148±1.18	0.147±0.72	7.09	7.08
11	0.101±0.342	3.86±10.1	0.138±0.025	0.101±0.062	2.71±8.91	44.7±127	0.164±3.53	0.100±7.95	6.58	6.35
12	0.129±0.337	3.88±10.1	0.096±0.036	0.051±0.051	2.83±7.03	44.5±127	0.143±4.53	0.126±8.53	6.29	5.65
13	0.156±0.382	3.81±10.2	0.122±0.008	0.045±0.039	2.62±8.63	44.7±126	0.167±5.52	0.143±7.85	6.42	5.07
14	0.140±0.392	3.90±10.1	0.060±0.028	0.052±0.065	2.79±8.12	44.5±127	0.154±3.09	0.123±5.32	5.84	5.48
15	0.144±0.309	3.84±10.1	0.093±0.021	0.060±0.013	2.59±7.47	44.6±127	0.140±2.57	0.130±0.24	7.32	6.91
16	0.109±0.307	3.87±10.2	0.064±0.025	0.039±0.034	2.82±8.25	44.6±127	0.180±5.44	0.160±1.38	8.88	6.20
17	0.129±0.348	3.85±10.2	0.147±0.024	0.116±0.058	2.56±8.17	44.8±127	0.167±5.32	0.150±10.9	8.22	7.53
18	0.127±0.336	3.71±10.1	0.105±0.043	0.124±0.069	2.84±7.26	44.8±127	0.158±5.42	0.155±8.95	7.25	6.31
19	0.151±0.325	3.83±10.2	0.192±0.041	0.131±0.014	2.50±7.34	44.7±127	0.162±4.44	0.122±6.68	7.36	5.01
20	0.109±0.388	3.74±10.2	0.102±0.042	0.093±0.065	2.42±8.71	44.7±127	0.144±6.24	0.142±2.03	8.16	5.01
21	0.105±0.331	3.77±10.2	0.092±0.030	0.100±0.024	2.89±8.48	44.7±126	0.164±5.94	0.129±8.58	8.69	7.94
22	0.146±0.377	3.89±10.2	0.119±0.009	0.040±0.060	2.80±8.77	44.6±127	0.140±4.00	0.112±8.26	6.93	6.56
23	0.103±0.327	3.80±10.2	0.128±0.015	0.039±0.036	2.55±7.38	44.8±127	0.145±4.77	0.124±4.26	8.26	5.26
24	0.156±0.334	3.71±10.1	0.119±0.058	0.090±0.043	2.59±7.85	44.7±126	0.143±7.00	0.112±9.71	8.61	6.49
25	0.153±0.353	3.71±10.2	0.115±0.014	0.048±0.029	2.44±7.40	44.5±126	0.156±5.54	0.121±6.75	7.07	5.42
26	0.131±0.308	3.87±10.1	0.120±0.068	0.019±0.031	2.73±8.47	44.8±126	0.129±1.49	0.112±0.01	7.42	6.84
27	0.145±0.334	3.70±10.1	0.108±0.029	0.044±0.014	2.43±7.54	44.8±127	0.144±4.53	0.123±14.9	7.18	5.40
28	0.139±0.317	3.72±10.1	0.071±0.020	0.057±0.020	2.83±7.56	44.5±126	0.141±4.94	0.124±2.40	7.06	5.92
29	0.148±0.358	3.88±10.1	0.071±0.031	0.037±0.049	2.42±7.47	44.8±126	0.168±3.14	0.133±9.15	8.99	7.98
30	0.143±0.373	3.70±10.0	0.078±0.052	0.023±0.068	2.67±8.10	44.9±127	0.157±1.20	0.147±3.91	7.71	6.79
31	0.135±0.363	3.77±10.2	0.047±0.056	0.046±0.012	2.77±7.37	44.8±126	0.164±2.98	0.126±13.1	8.22	5.29
32	0.155±0.348	3.79±10.1	0.065±0.031	0.012±0.019	2.41±7.28	44.8±126	0.129±3.88	0.131±12.0	7.93	5.66
33	0.154±0.386	3.87±10.1	0.078±0.045	0.053±0.019	2.43±7.95	44.6±126	0.167±3.67	0.145±0.51	6.71	5.64
34	0.140±0.350	3.76±10.1	0.078±0.037	0.025±0.048	2.81±8.49	44.8±126	0.133±2.14	0.110±4.14	7.72	7.36
35	0.119±0.334	3.73±10.1	0.060±0.023	0.028±0.059	2.67±7.63	44.8±126	0.147±6.51	0.103±13.5	7.15	5.59
36	0.134±0.346	3.79±10.2	0.116±0.057	0.042±0.043	2.82±8.31	44.6±127	0.156±2.83	0.155±7.21	7.95	6.31
37	0.111±0.317	3.77±10.1	0.065±0.029	0.063±0.067	2.45±8.75	44.9±126	0.147±4.15	0.128±8.15	6.49	5.36
38	0.119±0.330	3.76±10.2	0.112±0.045	0.053±0.034	2.45±7.38	44.5±127	0.157±2.48	0.157±12.9	8.24	7.68
39	0.148±0.362	3.72±10.1	0.082±0.019	0.021±0.016	2.72±8.93	44.6±127	0.131±2.08	0.115±14.8	8.91	6.90
40	0.117±0.341	3.82±10.1	0.116±0.007	0.036±0.054	2.89±8.79	44.5±126	0.138±2.21	0.136±3.82	7.82	5.62
41	0.120±0.385	3.81±10.1	0.106±0.009	0.102±0.048	2.62±7.47	44.7±126	0.154±5.13	0.141±7.91	6.20	6.12
42	0.107±0.315	3.73±10.1	0.057±0.056	0.039±0.027	2.44±8.07	44.5±126	0.147±1.55	0.128±3.60	6.36	6.22
43	0.134±0.302	3.85±10.2	0.100±0.053	0.064±0.063	2.50±8.16	44.7±127	0.125±3.48	0.105±13.1	7.86	5.95
44	0.149±0.323	3.70±10.1	0.136±0.034	0.093±0.031	2.41±7.63	44.6±127	0.150±3.10	0.129±9.21	7.52	6.17
45	0.123±0.353	3.79±10.2	0.088±0.011	0.052±0.018	2.45±8.37	44.8±127	0.168±2.52	0.147±5.73	6.88	6.40
46	0.142±0.389	3.86±10.1	0.128±0.045	0.034±0.061	2.67±8.76	44.7±126	0.144±3.27	0.127±11.2	8.25	7.54
47	0.143±0.339	3.76±10.1	0.114±0.018	0.102±0.063	2.84±8.38	44.7±126	0.142±4.46	0.117±6.08	7.17	6.00
48	0.153±0.335	3.74±10.1	0.091±0.045	0.085±0.061	2.70±7.17	44.9±127	0.158±6.75	0.111±9.43	8.02	5.59
49	0.103±0.327	3.77±10.1	0.115±0.018	0.057±0.058	2.82±8.32	44.7±126	0.168±3.25	0.146±4.33	6.38	5.59
50	0.151±0.385	3.80±10.2	0.081±0.054	0.034±0.038	2.89±7.81	44.8±127	0.180±6.90	0.170±11.6	8.62	8.54
51	0.104±0.367	3.89±10.2	0.081±0.047	0.047±0.037	2.82±7.80	44.9±127	0.166±2.62	0.107±10.7	7.80	7.74
52	0.146±0.363	3.84±10.2	0.024±0.011	0.017±0.023	2.54±8.21	44.6±127	0.167±2.14	0.124±9.07	6.27	5.14
53	0.138±0.345	3.87±10.1	0.110±0.021	0.020±0.043	2.83±8.20	44.9±126	0.157±4.31	0.153±6.69	7.50	5.07
54	0.121±0.344	3.73±10.1	0.087±0.053	0.073±0.067	2.42±7.17	44.7±127	0.166±3.10	0.162±11.6	8.26	5.00
55	0.112±0.331	3.80±10.1	0.108±0.024	0.033±0.043	2.49±8.26	44.6±127	0.128±6.65	0.116±10.7	6.77	6.65
平均值±标准差	0.132±0.016	3.80±0.058	0.089±0.035	0.055±0.032	2.65±0.156	44.7±0.116	0.152±0.013	0.133±0.017	7.37±0.802	6.32±0.880

Table 2

Fig.6

Table 3

Comparison of IA parameters of tensors before and after spatial normalization by Gaussian DTI template and linear DTI templates, respectively

被试编号	Gauss	linear	linear2
1	0.146±0.785	0.115±0.794	0.126±0.778
2	0.146±0.806	0.118±0.810	0.126±0.821
3	0.158±0.869	0.125±0.871	0.137±0.887
4	0.153±0.912	0.125±0.919	0.135±0.915
5	0.149±0.894	0.117±0.899	0.128±0.899
6	0.162±0.871	0.130±0.877	0.140±0.880
7	0.154±0.953	0.123±0.957	0.130±0.955
8	0.155±0.794	0.124±0.799	0.130±0.798
9	0.148±0.844	0.117±0.848	0.127±0.850
10	0.146±0.915	0.116±0.918	0.128±0.911
11	0.151±0.649	0.135±0.792	0.109±0.704
12	0.142±0.603	0.120±0.724	0.114±0.719
13	0.146±0.689	0.119±0.606	0.129±0.618
14	0.170±0.826	0.131±0.853	0.129±0.910
15	0.135±0.761	0.113±0.743	0.120±0.916
16	0.155±0.907	0.122±0.714	0.117±0.897
17	0.140±0.927	0.126±0.938	0.117±0.609
18	0.137±0.730	0.102±0.838	0.115±0.903
19	0.151±0.833	0.114±0.739	0.128±0.663
20	0.148±0.776	0.134±0.645	0.140±0.716
21	0.167±0.668	0.121±0.897	0.105±0.765
22	0.155±0.640	0.135±0.887	0.126±0.803
23	0.155±0.793	0.113±0.872	0.140±0.806
24	0.150±0.627	0.103±0.939	0.109±0.700
25	0.139±0.864	0.117±0.967	0.109±0.606
26	0.166±0.739	0.139±0.721	0.109±0.983
27	0.166±0.914	0.137±0.757	0.122±0.783
28	0.160±0.879	0.114±0.914	0.136±0.880
29	0.142±0.995	0.131±0.608	0.137±0.736
30	0.150±0.773	0.129±0.723	0.110±0.911
31	0.162±0.847	0.117±0.740	0.131±0.880
32	0.157±0.787	0.116±0.672	0.109±0.633
33	0.136±0.691	0.128±0.606	0.113±0.655
34	0.155±0.947	0.101±0.698	0.108±0.820
35	0.142±0.695	0.110±0.994	0.124±0.608
36	0.133±0.911	0.131±0.805	0.110±0.758
37	0.136±0.840	0.114±0.771	0.106±0.854
38	0.156±0.801	0.101±0.797	0.129±0.872
39	0.160±0.762	0.113±0.987	0.104±0.605
40	0.151±0.892	0.120±0.624	0.129±0.999
41	0.143±0.773	0.136±0.931	0.122±0.722
42	0.152±0.884	0.108±0.635	0.113±0.620
43	0.154±0.752	0.109±0.855	0.131±0.842
44	0.142±0.681	0.133±0.757	0.118±0.770
45	0.148±0.853	0.107±0.989	0.104±0.702
46	0.144±0.708	0.108±0.656	0.102±0.899
47	0.154±0.807	0.111±0.947	0.119±0.670
48	0.147±0.771	0.118±0.978	0.113±0.957
49	0.156±0.653	0.101±0.948	0.116±0.654
50	0.145±0.662	0.112±0.829	0.130±0.909
51	0.153±0.998	0.122±0.890	0.124±0.722
52	0.136±0.685	0.123±0.894	0.118±0.772
53	0.132±0.646	0.124±0.782	0.126±0.636
54	0.154±0.638	0.108±0.653	0.117±0.708
55	0.143±0.677	0.121±0.895	0.121±0.678
平均值±标准差	0.150±0.009	0.119±0.010	0.121±0.010

Table 3

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