Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

Figures & data

Table I. Image parameters.

Image #	Acq. Mode	Grid-Size	Recon. Alg	Iter. number	Post-filter width (mm)	Legend
1	2D	128×128	OSEM	2	3	2D-128-OSEM2-3mm
2	2D	128×128	OSEM	2	5	2D-128-OSEM2-5mm
3	2D	128×128	OSEM	4	5	2D-128-OSEM4-5mm
4	2D	256×256	OSEM	2	3	2D-256-OSEM2-3mm
5	2D	256×256	OSEM	2	5	2D-256-OSEM2-5mm
6	3D	128×128	ITER	2	3	3D-128-ITER2-3mm
7	3D	128×128	ITER	2	6	3D-128-ITER2-6mm
8	3D	128×128	ITER	4	6	3D-128-ITER4-6mm
9	3D	256×256	ITER	2	3	3D-256-ITER2-3mm
10	3D	256×256	ITER	2	6	3D-256-ITER2-6mm

Acq. Mode = acquisition mode; Recon. Alg = reconstruction algorithm; Iter = iteration.

Figure 1. Diagram for Feature Extraction. From left to right: Step 1: Identification of the axial slices showing the tumor. Step 2: Cropping to tumor region and location of the SUV_max. Step 3: Tumor delineation using 40% of the SUV_max. Step 4: Definition of the patch 7×7 voxels in size. Step 5: Feature extraction.

Figure 2. Range of variation of the textural features. Top: shows 8 first and 23 second order features. Bottom: shows high order features: 11-GLRL, 5-NGL, 3-NGTD. The gray and black shade represent the minimum and maximum deviations from the mean.