Abstract Book

S1173

ESTRO 37

Purpose or Objective To evaluate the impact of b-values on radiomic features of diffusion-weighted imaging (DWI) and to seek stable radiomic features which could be potentially used in quantitative clinical analysis of hepatic cirrhosis. Material and Methods The DWI data set of 35 patients who have been diagnosed with hepatic cirrhosis were randomly enrolled. DWI scanning was performed with ten b-values (0, 20, 50, 100, 200, 400, 800, 1000, 1200 and 1500 s/mm 2 , respectively) using a GE 3.0T Discovery scanner. Rigid registration was used to align ten b-value DWIs to T1WI image in MIM software. Using the anatomical information of T1WI as reference, we defined three VOIs (volume of interest, VOI) in parenchyma of liver segment II/III, V/VI and VII of b=0 s/mm 2 DWI images. Then, the VOIs in b=0 s/mm 2 were mapped to the other nine b-value DWI images. After that, 78 radiomic features were extracted by 3D Slicer Radiomics Extension from these VOIs, including 19 first order intensity histogram (IH) based features, 27 gray-level co-occurrence matrix (GLCM) based features, 16 gray-level run-length matrix (GLRLM) based features and 16 gray-level size zone matrix (GLSZM) based features. The percentage coefficient of variation (%COV) was used to determine the robust radiomic features against different b-values and the feature with %COV < 30 was considered as a stable one. The concordance correlation coefficient (CCC) was used to evaluate the reproducibility of different b-value DWI images and the image with CCC > 0.8 was considered to have higher reproducibility. Results 30 radiomic features were stable across ten b-value DWI images is presented in Table 1, including 4 IH-based features, 15 GLCM-based features, 7 GLRLM-based features and 4 GLSZM-based features. For different b- value DWI images, the higher reproducible radiomic features can be extracted from DWIs with nearby b- values. Especially, the most higher reproducible radiomic features were extracted from DWI image group b=0, 20, 50, 100, 200s/mm 2 and group b=800, 1000, 1200 s/mm 2 . Table 1 The stable radiomic features across ten b-value DWI images (%COV < 30)

group. The use of these stable radiomic features to analyze the reproducible DWI images of patients with hepatic cirrhosis can acquire more robust and accurate results. EP-2130 Automatic EPID image quality evaluation using a Standard Imaging QC-3 phantom. A. Prado 1 , M. Leonor 1 , A. Ferrando 2 1 Hospital Universitario 12 de Octubre, Radiofísica y Protección Radiológica, Madrid, Spain 2 Hospital Universitario 12 de Octubre, Servicio de Oncología Radioterápica. Sección de Radiofísica., Madrid, Spain Purpose or Objective To obtain a set of parameters in order to evaluate image quality of QC-3 phantom EPID images and to set tolerances for those parameters for QA purposes. Material and Methods Measurements were carried out on a 6 MV Varian Unique (Varian Medical Systems. Palo Alto, CA.) with an amorphous silicon Portal Vision unit. The phantom used was a Standard Imaging QC-3 phantom with spatial frequencies of 0.1, 0.201, 0.25, 0.435 and 0.766 lp/mm for the bar pattern sectors. It also has six sectors with different thicknesses so as to achieve different contrasts. All images were taken using 6 MU. In order to evaluate parameter consistency 20 images were obtained in consecutive days. The phantom was aligned with the lasers and was located on the EPID surface, which was set at SSD=160 cm. To analyze the images an Image J macro was developed. This macro automatically creates all needed ROIs, obtains the relevant data and performs the calculations. First, standard deviations of black and white sectors were obtained in order to calculate the noise as σ 2 n =0.5·(σ 2 b + σ 2 w ). For each bar pattern sector its standard deviation was obtained (σ i ). After that, a noise subtraction was performed just subtracting σ 2 n to each of the σ i 2 and applying the square root. These results were normalized to the σ obtained in a ROI with half black and half white regions. The corresponding 0 frequency value was force to be 1. A Rodbard function was fitted to data with R 2 >0.98, which is reasonable for obtaining a CTF estimation. The area under the curve was calculated utilizing the Simpson rule. Five parameters were evaluated, namely the area under the curve up to the highest frequency in the pattern and the frequencies for which the CTF values were 0.9, 0.5, 0.4 and 0.3, respectively. Results Results obtained are depicted in figure 1. Time evolution of all parameters studied during the considered period are shown. Standard deviations of all 20 images for each parameter were obtained (table 1). As a consequence, it was considered as reasonable to set a 3σ tolerance for every parameter (table 1) in order to compare future results when performing EPID QA at our institution.

IH

GLCM GLCM- IDMN

GLRLM GLSZM

GLRLM- SRE GLRLM- R.Perce nt GLRLM- RLNUN GLRLM- R.Entro py GLRLM- RLNU GLRLM- LRE GLRLM- GLNU

GLSZM- SAE GLSZM- Z.Entro py GLSZM- GLNU

GLCM- InverseVar

IH-Kurtosis

GLCM- IMC2 GLCM- Homo2 GLCM- IDMN

IH-Entropy

GLCM-IDN

IH- RobustAbsDe v IH- InterQuarRan ge

GLCM- SumEntro py

GLCM- Entropy

GLSZM- SZNUN

GLCM- Homo1

GLCM- SumAver

GLCM-ID GLCM-

AverInten

GLCM- DiffEntrop y

GLCM- DiffAver

GLCM- Dissimilari ty

Conclusion Textural features are less affected by different b-values, especially the GLCM-based features. Besides, b=0, 20, 50, 100, 200s/mm 2 is the highest reproducible DWI image

Figure 1: Time evolution of evaluated parameters.