Dataset Information

Cone beam computed tomography-guided thin/ultrathin bronchoscopy for diagnosis of peripheral lung nodules: a prospective pilot study.

ABSTRACT: Background:Despite advances in bronchoscopy, its diagnostic yield for peripheral lung lesions continues to be suboptimal. Cone beam computed tomography (CBCT) could be utilized to corroborate the accuracy of our bronchoscopic navigation and hopefully increase its diagnostic yield. However, data on radiation exposure and feasibility of CBCT-guided bronchoscopy is scarce. Methods:Prospective pilot study of bronchoscopy for peripheral lung nodules under general anesthesia with thin/ultrathin bronchoscope, radial-probe endobronchial ultrasound (RP-EBUS), and CBCT. Main objective was to estimate radiation dose and secondary objective was the additional value of CBCT in terms of navigational and diagnostic yield. Results:A total of 20 patients were enrolled. Median lesion size was 2.1 (range, 1.1-3) cm and distance from pleura was 2.1 (range, 0-2.8) cm. "Bronchus sign" was present in 12 (60%) of the lesions. Totally, 12 lesions (60%) were invisible on fluoroscopy. CBCT identified atelectasis obscuring the target in 4 cases (20%). Eleven patients (55%) underwent 1 CBCT scan and 9 patients (45%) 2. The mean estimated effective dose (E) to patients resulting from CBCT ranged between 8.6 and 23 mSv, depending on utilized conversion factors. Both pre-CBCT navigation and diagnostic yield were 50%. Additional post-CBCT maneuvers increased navigation yield to 75% (P=0.02) and diagnostic yield to 70% (P=0.04). One patient developed a pneumothorax. Conclusions:CBCT-guided bronchoscopy is associated with an acceptable radiation dose. CBCT may potentially increase both navigation and diagnostic yield of thin/ultrathin bronchoscopy for peripheral lung nodules. The above findings as well as the incidental but relevant finding of intra-procedural atelectasis need to be confirmed in larger prospective studies. Trial registration:This study is registered in ClinicalTrials.gov as number NCT02978170.

SUBMITTER: Casal RF

PROVIDER: S-EPMC6344689 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Cone beam computed tomography-guided thin/ultrathin bronchoscopy for diagnosis of peripheral lung nodules: a prospective pilot study.

Casal Roberto F RF Sarkiss Mona M Jones Aaron K AK Stewart John J Tam Alda A Grosu Horiana B HB Ost David E DE Jimenez Carlos A CA Eapen George A GA

Journal of thoracic disease 20181201 12

<h4>Background</h4>Despite advances in bronchoscopy, its diagnostic yield for peripheral lung lesions continues to be suboptimal. Cone beam computed tomography (CBCT) could be utilized to corroborate the accuracy of our bronchoscopic navigation and hopefully increase its diagnostic yield. However, data on radiation exposure and feasibility of CBCT-guided bronchoscopy is scarce.<h4>Methods</h4>Prospective pilot study of bronchoscopy for peripheral lung nodules under general anesthesia with thin/u ...[more]

PMID: 30746241

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Project description: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, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community.As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall 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. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that 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.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.Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 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.

Project description:BackgroundThe imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed.MethodsIncluded in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions.ResultsThe phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains.ConclusionsThe proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

Dataset Information

Cone beam computed tomography-guided thin/ultrathin bronchoscopy for diagnosis of peripheral lung nodules: a prospective pilot study.

Publications

Cone beam computed tomography-guided thin/ultrathin bronchoscopy for diagnosis of peripheral lung nodules: a prospective pilot study.

Similar Datasets

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