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Multi-model Analysis of Diffusion-weighted Imaging of Normal Testes at 3.0 T

Rationale and Objectives

This study aimed to establish diffusion quantitative parameters (apparent diffusion coefficient [ADC], DDC, α, D app , and K app ) in normal testes at 3.0 T.

Materials and methods

Sixty-four healthy volunteers in two age groups (A: 10–39 years; B: ≥ 40 years) underwent diffusion-weighted imaging scanning at 3.0 T. ADC 1000 , ADC 2000 , ADC 3000 , DDC, α, D app , and K app were calculated using the mono-exponential, stretched-exponential, and kurtosis models. The correlations between parameters and the age were analyzed. The parameters were compared between the age groups and between the right and the left testes.

Results

The average ADC 1000 , ADC 2000 , ADC 3000 , DDC, α, D app , and K app values did not significantly differ between the right and the left testes ( P > .05 for all). The following significant correlations were found: positive correlations between age and testicular ADC 1000 , ADC 2000 , ADC 3000 , DDC, and D app ( r = 0.516, 0.518, 0.518, 0.521, and 0.516, respectively; P < .01 for all) and negative correlations between age and testicular α and K app ( r = −0.363, −0.427, respectively; P < .01 for both). Compared to group B, in group A, ADC 1000 , ADC 2000 , ADC 3000 , DDC, and D app were significantly lower ( P < .05 for all), but α and K app were significantly higher ( P < .05 for both).

Conclusions

Our study demonstrated the applicability of the testicular mono-exponential, stretched-exponential, and kurtosis models. Our results can help establish a baseline for the normal testicular parameters in these diffusion models. The contralateral normal testis can serve as a suitable reference for evaluating the abnormalities of the other side. The effect of age on these parameters requires further attention.

Introduction

Ultrasound is the initial imaging method for testicular evaluation. However, magnetic resonance imaging (MRI), which provides anatomic information, is a powerful alternative imaging modality for testicular disease, especially ultrasound equivocal cases . Diffusion-weighted imaging (DWI), which assesses the random movement of water molecules within a tissue, is one of the primary components of MRI. DWI is a highly accurate cancer detection strategy and has been recommended as a biomarker for cancer identification .

The mono-exponential model, based on the assumption of free Brownian movement of molecular water, has been adopted for DWI analysis in most clinical studies. However, water molecules do not move freely in biological tissues because of many forms of hindrance, such as membranes and intracellular organelles, which influence diffusion . Furthermore, only one parameter can be obtained from the mono-exponential model, which limits the amount of information acquired from the DWI dataset. Therefore, new diffusion models have been developed to describe the complicated behavior of water diffusion; these models include the stretched-exponential and kurtosis models, among others . Previous studies have suggested that those new models might provide more useful information for disease detection or classification .

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Patients

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MRI Protocol

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MRI Data Assessment

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Figure 1, Results from a 44-year-old volunteer. (a) Transverse T2-weighted anatomic image. One observer placed regions of interest (ROIs) on the largest testis slice on the image of b-value = 50 s/mm 2 of the right and left testes (b) . The ROI was copied from images of b-value = 50 s/mm 2 to the same location of other b-value images. The mean signal intensity value of each b-value was recorded for each ROI (c) – (i) . We performed nonlinear least-square fitting based on the Levenberg-Marquardt algorithm using MATLAB to the mean signal of the right testis to obtain apparent diffusion coefficient (ADC) 1000 , ADC 2000 , ADC 3000 , DDC, α, D app , and K app (j) . The ADC values were calculated using maximum b-values of 1000, 2000, and 3000 s/mm 2 .

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Modeling

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S(b)/S50=exp{−[(b−50)⋅DDC]α} S

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S(b)/S50=exp[−(b−50)⋅Dapp+(b−50)2⋅Dapp2⋅Kapp/6] S

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Statistical Analysis

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Results

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TABLE 1

Comparison of the Parameter Values of the Right and the Left Testes

Parameter Right Testes ( n = 64) Left Testes ( n = 64)T__P ADC 1000 0.990 ± 0.138 0.992 ± 0.142 −0.294 .77 ADC 2000 0.821 ± 0.101 0.825 ± 0.104 −0.675 .502 ADC 3000 0.699 ± 0.078 0.702 ± 0.079 −0.696 .489 DDC 0.883 ± 0.174 0.890 ± 0.197 −0.604 .548 α 0.683 ± 0.044 0.683 ± 0.040 −0.291 .772 D app 1.097 ± 0.160 1.101 ± 0.163 −0.437 .664 K app 0.817 ± 0.068 0.815 ± 0.069 0.512 .611

The data are presented as mean ± standard deviation.

Values of ADC 1000 , ADC 2000 , ADC 3000 , DDC, and D app are reported in units of ×10 −3 mm 2 /s; α and K app are both nondimensional parameters.

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TABLE 2

Pearson Correlation Coefficient Between Parameters and Age

Parameter r_P__N_ ADC 1000 0.516 <.001 128 ADC 2000 0.518 <.001 128 ADC 3000 0.518 <.001 128 DDC 0.521 <.001 128 α −0.363 <.001 128 D app 0.516 <.001 128 K app −0.427 <.001 128

r = Pearson correlation coefficient.

Figure 2, Scatterplots of the parameter values correlated with age for normal testes. The correlations between the apparent diffusion coefficient (ADC) 1000 , ADC 2000 , ADC 3000 , DDC, and D app of testes and age were significantly positive ( r = 0.516, 518, 0.518, 0.521, and 0.516, respectively; P < .01 for all), whereas the correlations between the α and K app of the testes and age were significantly negative ( r = −0.363, −0.427, respectively; P < .01 for both). r = Pearson correlation coefficient.

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TABLE 3

Comparison of Parameters Values Between Groups A and B

Parameter Group A ( n = 72) Group B ( n = 56)T__P ADC 1000 0.941 ± 0.112 1.055 ± 0.145 −4.983 <.001 ADC 2000 0.786 ± 0.080 0.871 ± 0.108 −5.139 <.001 ADC 3000 0.672 ± 0.057 0.738 ± 0.086 −5.210 <.001 DDC 0.823 ± 0.120 0.968 ± 0.220 −4.452 <.001 α 0.692 ± 0.043 0.672 ± 0.038 2.732 =.007 D app 1.042 ± 0.133 1.172 ± 0.165 −4.953 <.001 K app 0.838 ± 0.049 0.788 ± 0.079 4.145 <.001

The data are presented as mean ± standard deviation.

Values of ADC 1000 , ADC 2000 , ADC 3000 , DDC, and D app , which are reported in units of ×10 −3 mm 2 /s; α and K app are both nondimensional parameters.

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Figure 3, Variations in the apparent diffusion coefficient (ADC) value depending on the b-value for group A and group B. The ADC value of both group A (a) and group B (b) significantly decreased with increasing b-value. The values of ADC 3000 for both group A (a) and group B (b) were significantly lower than the ADC 1000 and ADC 2000 values ( P < .01 for all). The values of ADC 2000 for both group A (a) and group B (b) were significantly lower than the ADC 1000 value ( P < .01 for both).

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Discussion

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Conclusion

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Acknowledgment

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