Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations

Abstract

Purpose: Respiratory motion is one of the major challenges in radiotherapy. In this work, a comprehensive and clinically plausible set of 4D numerical phantoms, together with their corresponding "ground truths," have been developed and validated for 4D radiotherapy applications.

Methods: The phantoms are based on CTs providing density information and motion from multi-breathing-cycle 4D Magnetic Resonance imagings (MRIs). Deformable image registration (DIR) has been utilized to extract motion fields from 4DMRIs and to establish inter-subject correspondence by registering binary lung masks between Computer Tomography (CT) and MRI. The established correspondence is then used to warp the CT according to the 4DMRI motion. The resulting synthetic 4DCTs are called 4DCT(MRI)s. Validation of the 4DCT(MRI) workflow was conducted by directly comparing conventional 4DCTs to derived synthetic 4D images using the motion of the 4DCTs themselves (referred to as 4DCT(CT)s). Digitally reconstructed radiographs (DRRs) as well as 4D pencil beam scanned (PBS) proton dose calculations were used for validation.

Results: Based on the CT image appearance of 13 lung cancer patients and deformable motion of five volunteer 4DMRIs, synthetic 4DCT(MRI)s with a total of 871 different breathing cycles have been generated. The 4DCT(MRI)s exhibit an average superior-inferior tumor motion amplitude of 7 ± 5 mm (min: 0.5 mm, max: 22.7 mm). The relative change of the DRR image intensities of the conventional 4DCTs and the corresponding synthetic 4DCT(CT)s inside the body is smaller than 5% for at least 81% of the pixels for all studied cases. Comparison of 4D dose distributions calculated on 4DCTs and the synthetic 4DCT(CT)s using the same motion achieved similar dose distributions with an average 2%/2 mm gamma pass rate of 90.8% (min: 77.8%, max: 97.2%).

Conclusion: We developed a series of numerical 4D lung phantoms based on real imaging and motion data, which give realistic representations of both anatomy and motion scenarios and the accessible "ground truth" deformation vector fields of each 4DCT(MRI). The open-source code and motion data allow foreseen users to generate further 4D data by themselves. These numeric 4D phantoms can be used for the development of new 4D treatment strategies, 4D dose calculations, DIR algorithm validations, as well as simulations of motion mitigation and different online image guidance techniques for both proton and photon radiation therapy.

Keywords: 4D imaging; 4D numerical phantom; 4DMRI; intrafraction motion; proton therapy.

FIGURE 1

(a) Top: CT geometries from 13 patients. CT1–CT6 are end exhale (EE) reference CTs extracted from 4DCTs, whereas CT7–CT13 were acquired during deep‐inspiration breath‐hold (DIBH). Bottom: MRI lung geometries from five volunteers at EE reference phases from the 4DMRIs. (b) Volumes of both lung halves for CTs and MRIs. For the 4DMRIs the volumes for both the EE and the end inhalation (EI) reference state are shown

FIGURE 2

Workflow to generate a 4DCT(MRI) based on a reference CT and a 4DMRI

FIGURE 3

For each of the 4DMRI geometries, a point in the dome of each half of the lung was selected (see (a) for MRI1) and the superior–inferior (SI) motion is analyzed (left: red, right: green). Boxplots of amplitudes (SI) (b) and periods (c) over all breathing cycles of the 4DMRIs. SI displacements over time for MRI1–MRI5 (d–h). Selected motion patterns for 4DCT(MRI) generation are marked by gray shaded areas

FIGURE 4

Example coronal and sagittal 4DCT(MRI) slices at different breathing phases of CT2(MRI1) (a–d) and CT11(MRI2) (e–h). For CT2(MR1) the reference end exhale (EE) phase and an end inhalation (EI) phase are shown. For CT11(MRI2) the deep‐inspiration breath‐hold (DIBH) reference and an EE phase are displayed. The clinical target volume (CTV) is contoured on each image. The white lines have been inserted for visual reference

FIGURE 5

Overlay of end inhalation digitally reconstructed radiograph (DRR) from 4DCT (green) and 4DCT(CT) (pink) for CT1–CT6 (left) and relative change of the pixel values of the 4DCT(CT) DRR relative to the 4DCT DRR (right)

FIGURE 6

Overlay of sagittal slice of 4DCT (green) and 4DCT(CT) (pink) in (a–f) and patched 4DCT (pink) in (g–l) for CT1–CT6. All slices show the end inhalation phase and the tumor is visible in all slices

FIGURE 7

Dose distributions and dose differences for CT6 from 4D dose calculation on the original 4DCT (b), 4DCT(CT) (c), and patched 4DCT (d). The static dose distribution is shown in (a) and the chosen field directions are indicated with white arrows. Dose distributions and differences are shown as percentages of the prescribed dose and the same colormaps were used for all dose distributions and dose differences, respectively