Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

Salimi, Yazdan; Shiri, Isaac; Akhavanallaf, Azadeh; Mansouri, Zahra; Sanaat, AmirHosein; Pakbin, Masoumeh; Ghasemian, Mohammadreza; Arabi, Hossein; Zaidi, Habib

doi:10.1016/j.ejrad.2022.110602

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…The external body contour was extracted from axial CT images through image processing algorithms developed and used in previous studies (34, 35). The CT images were cropped to a bounding box (BB) including the body contour in the lateral and AP directions to remove the background area.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

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Background: Automated organ segmentation from computed tomography (CT) images facilitates a number of clinical applications, including clinical diagnosis, monitoring of treatment response, quantification, radiation therapy treatment planning, and radiation dosimetry. Purpose: To develop a novel deep learning framework to generate multi-organ masks from CT images for 23 different body organs. Methods: A dataset consisting of 3106 CT images (649,398 axial 2D CT slices, 13,640 images/segment pairs) and ground-truth manual segmentation from various online available databases were collected. After cropping them to body contour, they were resized, normalized and used to train separate models for 23 organs. Data were split to train (80%) and test (20%) covering all the databases. A Res-UNET model was trained to generate segmentation masks from the input normalized CT images. The model output was converted back to the original dimensions and compared with ground-truth segmentation masks in terms of Dice and Jaccard coefficients. The information about organ positions was implemented during post-processing by providing six anchor organ segmentations as input. Our model was compared with the online available TotalSegmentator model through testing our model on their test datasets and their model on our test datasets. Results: The average Dice coefficient before and after post-processing was 84.28% and 83.26% respectively. The average Jaccard index was 76.17 and 70.60 before and after post-processing respectively. Dice coefficients over 90% were achieved for the liver, heart, bones, kidneys, spleen, femur heads, lungs, aorta, eyes, and brain segmentation masks. Post-processing improved the performance in only nine organs. Our model on the TotalSegmentator dataset was better than their models on our dataset in five organs out of 15 common organs and achieved almost similar performance for two organs. Conclusions: The availability of a fast and reliable multi-organ segmentation tool leverages implementation in clinical setting. In this study, we developed deep learning models to segment multiple body organs and compared the performance of our models with different algorithms. Our model was trained on images presenting with large variability emanating from different databases producing acceptable results even in cases with unusual anatomies and pathologies, such as splenomegaly. We recommend using these algorithms for organs providing good performance. One of the main merits of our proposed models is their lightweight nature with an average inference time of 1.67 seconds per case per organ for a total-body CT image, which facilitates their implementation on standard computers.

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Section: Methodsmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

Self Cite

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“…Knowledge of the patient's body attenuation or size characteristics prior to the diagnostic CT scan is helpful in tube voltage selection 22 and personalized chest CT protocol optimization. 23 In addition, body weight estimation from CT scout images is not affected by truncation because CT scout images are commonly acquired with an extended field of view compared to diagnostic scan images.…”

Section: Discussionmentioning

confidence: 99%

“…It allows radiologists and radiological technologists to obtain the patient's body weight prior to a diagnostic scan and validate that the proper scanner output is programmed based on the patient's size. Knowledge of the patient's body attenuation or size characteristics prior to the diagnostic CT scan is helpful in tube voltage selection 22 and personalized chest CT protocol optimization 23 . In addition, body weight estimation from CT scout images is not affected by truncation because CT scout images are commonly acquired with an extended field of view compared to diagnostic scan images.…”

Section: Discussionmentioning

confidence: 99%

Deep learning‐based body weight from scout images can be an alternative to actual body weight in CT radiation dose management

Ichikawa

Itadani

Sugimori

2023

J Applied Clin Med Phys

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Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning-based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management. Methods: Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients. A deep learning model was developed to predict body weight from scout images. The correlation between actual and predicted body weight was analyzed. To validate the use of predicted body weight in radiation dose management, the volume CT dose index (CTDI vol ) and the dose-length product (DLP) were compared between the body weight subgroups based on actual and predicted body weight. Surrogate size-specific dose estimates (SSDEs) acquired from actual and predicted body weight were compared to the reference standard. Results:The median actual and predicted body weight were 64.1 (interquartile range: 56.5-72.4) and 64.0 (56.3-72.2) kg, respectively. There was a strong correlation between actual and predicted body weight (ρ = 0.892, p < 0.001). The CTDI vol and DLP of the body weight subgroups were similar based on actual and predicted body weight (p < 0.001). Both surrogate SSDEs based on actual and predicted body weight were not significantly different from the reference standard (p = 0.447 and 0.410, respectively). Conclusion: Predicted body weight can be an alternative to actual body weight in managing dose metrics and simplifying SSDE calculation. Our proposed method can be useful for CT radiation dose management in adult patients with unknown body weight. K E Y W O R D S body weight, CT dose index, deep learning, diagnostic reference levels, dose-length product, size-specific dose estimates 1 INTRODUCTION Computed tomography (CT) scan plays an important role in the screening, diagnosis, and management of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The radiation dose to patients from CT scanning was evaluated through exposure factors of volumetric CT dose index (CTDI vol ) and dose-length product. The patient size was calculated by automatic extraction of body contour using an in-house developed code 36 in terms of water equivalent diameter and effective patient diameter. Size-specific dose estimate was calculated as described in the AAPM report #220 37 .…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence–Driven Single-Shot PET Image Artifact Detection and Disentanglement

Shiri,

Salimi,

Hervier

et al. 2023

Clin Nucl Med

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Purpose Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance. Methods The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions. Results Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUVmean, SUVmax, and SUVpeak, respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction. Conclusion We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.

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Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

Cited by 18 publications

References 44 publications

Deep learning-assisted multiple organ segmentation from whole-body CT images

Deep learning-assisted multiple organ segmentation from whole-body CT images

Deep learning‐based body weight from scout images can be an alternative to actual body weight in CT radiation dose management

Artificial Intelligence–Driven Single-Shot PET Image Artifact Detection and Disentanglement

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