Background And Purpose: In radiotherapy, the image quality of four-dimensional computed tomography (4DCT) is often degraded by artifacts resulting from breathing irregularities. Quality assurance mostly employ simplistic phantoms, not fully representing complexities and dynamics in patients. 3D-printing allows for design of highly customized phantoms. This study aims to validate the proof-of-concept of a realistic dynamic thorax phantom and its 4DCT application.
Materials And Methods: Using 3D-printing, a realistic thorax phantom was produced with tissue-equivalent materials for soft tissue, bone, and compressible lungs, including bronchi and tumors. Lung compression was facilitated by motors simulating customized breathing curves with an added platform for application of monitoring systems. The phantom contained three tumors which were assessed in terms of tumor motion amplitude. Three 4DCT sequences and repeated static images for different lung compression levels were acquired to evaluate the reproducibility. Moreover, more complex patient-specific breathing patterns with irregularities were simulated.
Results: The phantom showed a reproducibility of ±0.2 mm and ±0.4 mm in all directions for static 3DCT images and 4DCT images, respectively. Furthermore, the tumor close to the diaphragm showed higher amplitudes in the inferior/superior direction (13.9 mm) than lesions higher in the lungs (8.1 mm) as observed in patients. The more complex breathing patterns demonstrated commonly seen 4DCT artifacts.
Conclusion: This study developed a dynamic 3D-printed thorax phantom, which simulated customized breathing patterns. The phantom represented a realistic anatomy and 4DCT scanning of it could create realistic artifacts, making it beneficial for 4DCT quality assurance or protocol optimization.
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http://dx.doi.org/10.1016/j.phro.2024.100656 | DOI Listing |
Eur Radiol
December 2024
University of Crete, Medical School, Department of Medical Physics, 71003, Heraklion, Crete, Greece.
Objectives: To compare the radiation exposure from single-energy CT (SECT) against rapid kV-switching dual-energy CT (DECT) imaging in both adults and children when resulting image data offer equivalent lesion identification power.
Materials And Methods: Lesions in an adult and a 10-year-old-child body phantom were imitated using iodine solutions of different concentrations. Phantoms were subjected to several SECT and DECT thoracic and abdominal scans using a rapid kV-switching DECT scanner.
Diagnostics (Basel)
November 2024
Department of Physics, University Koblenz, 56070 Koblenz, Germany.
Background: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in combination with histopathological workup for patients with non-small-cell lung cancer.
Methods: A total of 20 patients with biopsy-proven lung cancer who underwent [F]fluorodeoxyglucose positron emission/computed tomography (FDG-PET/CT) examination before tumor resection were included.
Nihon Hoshasen Gijutsu Gakkai Zasshi
December 2024
Department of Radiology, Division of Medical Technology, Kyushu University Hospital.
Purpose: The deep learning time-of-flight (DL-ToF) aims to replicate the ToF effects through post-processing, applying deep learning-based enhancement to PET images. This study evaluates the effectiveness of DL-ToF using a chest-abdomen phantom that simulates human anatomical structures.
Methods: The 3 DL-ToF intensities (Low-DL-ToF: LDL, Middle-DL-ToF: MDL, High-DL-ToF: HDL) were adopted for the PET image of the chest-abdomen phantom.
Bioengineering (Basel)
October 2024
Department of Biomedical Engineering, Guangdong Medical University, Xincheng, Dongguan 523808, China.
Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
October 2024
Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Background And Purpose: In radiotherapy, the image quality of four-dimensional computed tomography (4DCT) is often degraded by artifacts resulting from breathing irregularities. Quality assurance mostly employ simplistic phantoms, not fully representing complexities and dynamics in patients. 3D-printing allows for design of highly customized phantoms.
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