Synopsis

We compared the potential of various diffusion parameters obtained from Gaussian and non-Gaussian diffusion models in differentiating focal lesions of malignant spinal tumors. Mean signal intensity was calculated by placing ROIs within focal lesions for 9 b-values in each subject. ADC, frac<1, frac>3, and PG(D) differed significantly between focal lesions of myeloma and LCA. Frac>3 and PG(D) was significantly different between focal lesions of myeloma and MMK. Non-Gaussian diffusion parameters may provide additional information and improve the differentiation of malignant focal lesions compared with conventional diffusion parameters, which would be helpful in improving therapy strategies.

Purpose

Materials and Methods

Theoretical Background

ADC values were calculated using the following monoexponential model:

S(b)/S(0) = exp(-b·ADC)

where S(b) represents the signal intensity in the presence of diffusion sensitization, b, and S(0) represents the signal intensity in the absence of diffusion sensitization.

The three parameters of perfusion fraction (f), pseudo-ADC (ADC_fast), and true ADC (ADC_slow) were calculated using biexponential intravoxel incoherent motion analysis:

S(b)/S(0)= [f·exp (-b·ADC_fast)] +[(1 - f )·exp(-b·ADC_slow)]

The gamma distribution model (GDM) assumes a continuous distribution of diffusion coefficients, P (D). The DW signal can be written as follows:

$$S(b)=\int_{}^{} P(D)exp(-bD)dD$$ (1)

The gamma distribution function, PG (D) has been suggested recently as²:

$$P_{G}(D,\kappa,\theta)=D^{\kappa-1}\frac{exp(-D/\theta)}{Γ（\kappa）\theta^{\kappa}}$$ (2)

where Γ is the gamma function, θ is the scale parameter of the same dimensionality as diffusivity, and κ is the shape parameter. Replacing P(D) in Eq. (1) with PG (D) from Eq. (2), gives the following expression for DW signal attenuation:

$$S(b)=(1+b\theta)^{-\kappa}$$ (3)

MRI examination

DW images of the lumbar spines of 63 subjects were categorized as follows: patients with focal lesion of multiple myeloma (MM, n=23); patients with metastatic bone tumor from breast cancer (MMK, n=21); and patients with metastatic bone tumor from lung cancer (LCA, n=19).

MRI was performed on a 3-T system (Ingenia; Philips Healthcare) equipped with an anterior coil and integrated posterior coil. Single-shot DW EPI with 9 values (0, 40, 80, 140, 200, 500, 1000, 1500, 2000) on 3 orthogonal axes was performed with the following acquisition parameters: TR/TE =8000/84 ms; FOV= 35×35 cm²; matrix size, 192×192; in-plane voxel size, 1.8×1.8 mm²; slice thickness, 4 mm; number of slices, 11; slice gap, 1 mm; factor of 3 SENSE in the phase direction, and 1 average.

Image data analysis

Mean signal intensity was calculated by placing operator-determined regions of interest within the largest focal lesions for each b-value in each subject. For each subject, ADC, f, ADC_fast, ADC_slow, scale parameter θ, scale parameter κ, area fraction of D < 1.0mm²/s (frac<1), area fraction of D > 3.0 mm²/s (frac>3), and PG (D) were measured using Equations 1-3.

Statistical analysis

Parameters of the 3 groups were compared using the Kruskal-Wallis test. Multiple regression analysis was performed to identify potential associations of diffusion model parameters for differentiating focal lesions. Receiver operating characteristic analyses were then performed.

Results

Table 1 summarizes MR parameters for the 3 groups. ADC, frac<1, frac>3, and PG (D) differed significantly between focal lesions of myeloma and LCA. Frac>3 and PG (D) were significantly different between focal lesions of myeloma and MMK. Three parameters from biexponential models were not significantly different among the three groups. No parameters differentiated between focal lesions of LCA and MMK.

Multiple regression analysis demonstrated that frac>3 (P=0.03) differentiated focal lesions of MM from MMK. Areas under the curve (AUCs) were 0.821 for frac>3, 0.726 for PG (D), and 0.625 for ADC for differentiation between focal lesions of MM from LCA and 0.698 for frac>3, 0.670 for PG (D), and 0.597 for ADC for differentiation between focal lesions of MM from MMK (Fig. 1).

Discussion

Conclusion

Acknowledgements

References

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2. Oshio K, Shinmoto H, Mulkern RV. Interpretation of diffusion MR imaging data using a gamma distribution model. Magn Reson Med Sci. 2014;13:191-195.

3. Marlow R, Honeth G, Lombardi S, et al. A Novel Model of Dormancy for Bone Metastatic Breast Cancer Cells. Cancer Res. 2013;73(23); 6886–6899.