Validation of nonrigid registration in pretreatment and follow‐up PET/CT scans for quantification of tumor residue in lung cancer patients

Abstract

Nonrigid registrations of pre‐ and postradiotherapy (RT) PET/CT scans of NSCLC patients were performed with different algorithms and validated tracking internal landmarks. Dice overlap ratios (DR) of high FDG‐uptake areas in registered PET/CT scans were then calculated to study patterns of relapse. For 22 patients, pre‐ and post‐RT PET/CT scans were registered first rigidly and then nonrigidly. For three patients, two types (based on Demons or Morphons) of nonrigid registration algorithms each with four different parameter settings were applied and assessed using landmark validation. The two best performing methods were tested on all patients, who were then classified into three groups: large (Group 1), minor (Group 2) or insufficient improvement (Group 3) of registration accuracy. For Group 1 and 2, DRs between high FDG‐uptake areas in pre‐ and post‐RT PET scans were determined. Distances between corresponding landmarks on deformed pre‐RT and post‐RT scans decreased for all registration methods. Differences between Demons and Morphons methods were smaller than 1 mm. For Group 1, landmark distance decreased from 9.5 ± 2.1 mm to 3.8 ± 1.2 mm (mean ± 1 SD, p < 0.001), and for Group 3 from 13.6 ± 3.2 mm to 8.0 ± 2.2 mm (p.15em=.15em0.02). No significant change was observed for Group 2 where distances decreased from 5.6 ± 1.3 mm to 4.5 ± 1.1 mm (p.15em=.15em0.02). DRs of high FDG‐uptake areas improved significantly after nonrigid registration for most patients in Group 1. Landmark validation of nonrigid registration methods for follow‐up CT imaging in NSCLC is necessary. Nonrigid registration significantly improves matching between pre‐ and post‐RT CT scans for a subset of patients, although not in all patients. Hence, the quality of the registration needs to be assessed for each patient individually. Successful nonrigid registration increased the overlap between pre‐ and post‐RT high FDG‐uptake regions.PACS number: 87.57.Q‐, 87.57.C‐, 87.57.N‐, 87.57.‐s, 87.55.‐x, 87.55.D‐, 87.55.dh, 87.57.uk, 87.57.nj

Quantification of CT-assessed radiation-induced lung damage in lung cancer patients treated with or without chemotherapy and cetuximab

“…Non-rigid registration was based on the Morphons algorithm using eight resolution steps of 10 iterations with a Gaussian smoothing filters of 1.5 times the voxel dimensions [23]. The algorithm has been investigated for accuracy of tumor residue by Spijkerman et al [24]. For lung tissue registration this method had accuracy well below 5 mm resampling in which we have used in this study.…”

In Vivo Quantification of Hypoxic and Metabolic Status of NSCLC Tumors Using [18F]HX4 and [18F]FDG-PET/CT Imaging

“…Aerts et al showed that the residual tumor volume after RT is mainly located within the pre-RT high FDG-uptake volume. However, 30% of the residual volume did not correspond to the high FDG volume (11, 13). This may be caused by tumor regrowth in pre-RT hypoxic tumor subvolumes located outside the high FDG volume.…”

A multichannel feature-based approach for longitudinal lung CT registration in the presence of radiation induced lung damage

“…On the other hand, we tried to focus on landmarks around the tumour and areas of RILD, as, although these are the regions that are hardest to align, they are also the ones of most interest. In Spijkerman et al (2014), the best performing, medium, and worst performing groups had errors of 3.8 mm, 4.5 mm and 8.0 mm respectively. Once again, the temporal distance between scans was only about 4 months.…”