A longitudinal four‐dimensional computed tomography and cone beam computed tomography dataset for image‐guided radiation therapy research in lung cancer

Abstract

Purpose To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients we have collected at our institution and shared publicly with the research community. Acquisition and Validation Methods As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally-advanced non-small cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Daily 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. Data Format and Usage Notes 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. Discussion Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, over-sampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

4D robust optimization including uncertainties in time structures can reduce the interplay effect in proton pencil beam scanning radiation therapy

“…Three NSCLC patients with different tumor sizes and motion amplitudes are selected for the study from The Cancer Imaging Archive (TCIA) 4D Lung database. 38,39 The patient dataset includes high-quality 4DCT images with targets and organs at risk (OARs) delineated by a single radiation oncologist. For treatment planning, a CT-to-density table based on table IV in Hugo et al 39 was used.…”

“…38,39 The patient dataset includes high-quality 4DCT images with targets and organs at risk (OARs) delineated by a single radiation oncologist. For treatment planning, a CT-to-density table based on table IV in Hugo et al 39 was used. Audio-visual biofeedback was used for all patients to provide steady breathing patterns.…”

Evaluation of video compression methods for cone‐beam computerized tomography

“…Cone‐beam computed tomography (CBCT) is a routine imaging procedure for verification of patient position in radiotherapy . As designed for daily use, many sets of CBCT images are resulted during patient setup of multi‐faction IMRT . Due to low soft tissue contrast, CBCT is usually used for patient setup and rarely for treatment planning purpose.…”

Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐CBCT) using groupwise deformable registration

“…There are several known issues and limitations associated with the data and methods proposed in this study. First, in addition to the issues identified by Hugo et al . for the dataset utilized in this study, the clinically representative downsampled dataset was produced artificially rather than through a standard clinical acquisition.…”

“…CBCT projection datasets used in this study were from a larger dataset of 4D-CBCT scans from 20 subjects undergoing concurrent radiochemotherapy for locally advanced nonsmall cell lung cancer. 20,21 This dataset, collected under an institutional review board approved protocol, is described in more detail by Hugo et al 22 To demonstrate proof-of-concept in this study, one projection set was selected for each subject from those acquired during the first 2 weeks of treatment for which a reasonable respiratory signal could be extracted. Each of the 4D-CBCT datasets was acquired on a commercial CBCT scanner (On-Board Imager TM , Varian Medical Systems, Inc., Palo Alto, CA).…”