A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive

Abstract

Rationale and Objectives Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Materials and Methods 10 BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at 1/4th dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. Results 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. Conclusions The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both 4D CT and COPDgene patient cohorts.

Isotropic Total Variation Regularization of Displacements in Parametric Image Registration

“…In contrast to the two previous databases that were acquired with respiratory-correlated 4D reconstruction imaging, the COPDgene study [41] provides 3D breath-hold CT lung images. It consists of 10 cases, each case including a pair of images at end-inhale and end-exhale, together with 300 publicly-available landmarks per image.…”

Benchmarking of five commercial deformable image registration algorithms for head and neck patients

“…In an effort to provide consistent datasets for algorithm validation, several researchers have made their ground‐truth models publicly available. Examples include the extended cardiac–torso (XCAT) phantom, (3) the point‐validated pixel‐based breathing thorax model (POPI model), (4) the DIR‐Lab Thoracic 4D CT model, 5 , 6 and those provided as part of the EMPIRE 10 challenge (7) . In an effort to improve the correlation of computer‐based phantoms with the actual anatomical changes seen over a course of radiation therapy, the current authors previously developed synthetic datasets derived from clinical images of real head and neck patients (8) .…”

A hybrid optimization strategy for registering images with large local deformations and intensity variations

“…Notice that DM-ALOST gave a higher correlation than the other registration methods, although none of the differences were significant. It showed that DM-ALOST gave best registration results among all six methods [32].…”