Purpose: To evaluate on-board digital tomosynthesis (DTS) for patient positioning vs. two-dimensional (2D) radiography and three-dimensional cone beam (CBCT).
Methods And Materials: A total of 92 image sessions from 9 prostate cancer patients were analyzed. An on-board image set was registered to a corresponding reference image set. Four pairs of image sets were used: digitally reconstructed radiographs vs. on-board orthogonal paired radiographs for the 2D method, coronal-reference DTS vs. on-board coronal DTS for the coronal-DTS method, sagittal-reference DTS vs. on-board sagittal DTS for the sagittal-DTS method, and planning CT vs. CBCT for the CBCT method. The registration results were compared.
Results: The systematic errors in all methods were <1 mm/1 degrees . When registering the bony anatomy, the mean vector difference was 0.21 +/- 0.11 cm between 2D and CBCT, 0.11 +/- 0.08 cm between CBCT and coronal DTS, and 0.14 +/- 0.07 cm between CBCT and sagittal DTS. The correlation between CBCT to DTS was stronger (coefficient = 0.92-0.95) than the correlation between 2D and CBCT or DTS (coefficient = 0.81-0.83). When registering the soft tissue, the mean vector difference was 0.18 +/- 0.11 cm between CBCT and coronal DTS and 0.29 +/- 0.17 cm between CBCT and sagittal DTS. The correlation coefficient of CBCT to sagittal DTS and to coronal DTS was 0.84 and 0.92, respectively.
Conclusion: DTS could provide equivalent results to CBCT when the bony anatomy is used as landmarks for prostate image-guided radiotherapy. For soft tissue-based positioning verification, coronal DTS produced equivalent results to CBCT, but sagittal DTS alone was insufficient. DTS could allow for comparable soft tissue-based target localization with faster scanning time and a lower imaging dose compared with CBCT.
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http://dx.doi.org/10.1016/j.ijrobp.2008.09.006 | DOI Listing |
Med Image Anal
January 2025
Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland. Electronic address:
Eur Radiol
January 2025
Radboud University Medical Center, IQ Health science department, Nijmegen, The Netherlands.
Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.
Materials And Methods: The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50-72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
J Med Imaging (Bellingham)
January 2025
University of Houston, Department of Biomedical Engineering, Houston, Texas, United States.
Purpose: Digital phantoms are one of the key components of virtual imaging trials (VITs) that aim to assess and optimize new medical imaging systems and algorithms. However, these phantoms vary in their voxel resolution, appearance, and structural details. We investigate whether and how variations between digital phantoms influence system optimization with digital breast tomosynthesis (DBT) as a chosen modality.
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