Purpose: To provide a metric that reflects the dosimetric utility of the synthetic CT (sCT) and can be rapidly determined.
Methods: Retrospective CT and atlas-based sCT of 62 (53 IMRT and 9 VMAT) prostate cancer patients were used. For image similarity measurements, the sCT and reference CT (rCT) were aligned using clinical registration parameters. Conventional image similarity metrics including the mean absolute error (MAE) and mean error (ME) were calculated. The water equivalent depth (WED) was automatically determined for each patient on the rCT and sCT as the distance from the skin surface to the treatment plan isocentre at 36 equidistant gantry angles, and the mean WED difference (ΔWED¯) between the two scans was calculated. Doses were calculated on each scan pair for the clinical plan in the treatment planning system. The image similarity measurements and ΔWED¯ were then compared to the isocentre dose difference (ΔD) between the two scans.
Results: While no particular relationship to dose was observed for the other image similarity metrics, the ME results showed a linear trend against ΔD with R = 0.6, and the 95 % prediction interval for ΔD between -1.2 and 1 %. The ΔWED¯ results showed an improved linear trend (R = 0.8) with a narrower 95 % prediction interval from -0.8 % to 0.8 %.
Conclusion: ΔWED¯ highly correlates with ΔD for the reference and synthetic CT scans. This is easy to calculate automatically and does not require time-consuming dose calculations. Therefore, it can facilitate the process of developing and evaluating new sCT generation algorithms.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ejmp.2022.11.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Periodontal diseases in Down syndrome during childhood: a scoping review.
BMC Oral Health
January 2025
Université Paris Cité, Laboratory URP 2496 Orofacial Pathologies, Imaging, and Biotherapies, Faculty of odontology, Montrouge, France.
Background: Down syndrome (DS) is a genetic condition that involves the deregulation of immune function and is characterized by a proinflammatory phenotype leading to an impaired response to infections. Periodontitis is a highly prevalent chronic inflammatory disease. It has been shown that adults and teenagers with DS are more susceptible to this disease, but a similar correlation in DS children remains elusive.
View Article and Find Full Text PDFHierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.
Sci Rep
January 2025
Biotechnology Major, Sangmyung University, Seoul, 03016, South Korea.
Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning models typically perform better with more input data, the available wildlife data are ordinarily limited, specifically for rare or endangered species.
View Article and Find Full Text PDFImproved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.
Sci Rep
January 2025
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
The diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods. Although many efforts have been made in automatic lung nodule segmentation, most of them have not sufficiently addressed the challenges related to the type and size of nodules, such as juxta-pleural and juxta-vascular nodules. The current research introduces a Squeeze-Excitation Dilated Attention-based Residual U-Net (SEDARU-Net) with a robust intensity normalization technique to address the challenges related to different types and sizes of lung nodules and to achieve an improved lung nodule segmentation.
View Article and Find Full Text PDFA comprehensive evaluation of histopathology foundation models for ovarian cancer subtype classification.
NPJ Precis Oncol
January 2025
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian carcinoma morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge).
View Article and Find Full Text PDFNoninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a multicentre retrospective study.
Sci Rep
January 2025
Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, No. 324, Jinan, 250021, Shandong, China.
To develop and validate non-contrast computed tomography (NCCT)-based radiomics method combines machine learning (ML) to investigate invisible microscopic acute ischaemic stroke (AIS) lesions. We retrospectively analyzed 1122 patients from August 2015 to July 2022, whose were later confirmed AIS by diffusion-weighted imaging (DWI). However, receiving a negative result was reported by radiologists according to the NCCT images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!