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Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy

Kadhim, Mustafa LU ; Persson, Emilia LU ; Haraldsson, André LU ; Gustafsson, Christian Jamtheim LU ; Nilsson, Mikael LU orcid ; Kügele, Malin LU orcid ; Bäck, Sven LU and Ceberg, Sofie LU (2025) In Scientific Reports 15(1).
Abstract

Precise patient positioning and daily anatomical verification are crucial in external beam radiotherapy to ensure accurate dose delivery and minimize harm to healthy tissues. However, Current image-guided radiotherapy techniques struggle to balance high-quality volumetric anatomical visualization and rapid low-dose imaging. Addressing this, reconstructing volumetric images from ultra-sparse X-ray projections holds promise for significantly reducing patient radiation exposure and potentially enabling real-time anatomy verification. Here, we present a novel DL-based framework that generates synthetic volumetric cone-beam CT in real-time from two orthogonal projection views and a reference planning CT for prostate cancer patients. Our... (More)

Precise patient positioning and daily anatomical verification are crucial in external beam radiotherapy to ensure accurate dose delivery and minimize harm to healthy tissues. However, Current image-guided radiotherapy techniques struggle to balance high-quality volumetric anatomical visualization and rapid low-dose imaging. Addressing this, reconstructing volumetric images from ultra-sparse X-ray projections holds promise for significantly reducing patient radiation exposure and potentially enabling real-time anatomy verification. Here, we present a novel DL-based framework that generates synthetic volumetric cone-beam CT in real-time from two orthogonal projection views and a reference planning CT for prostate cancer patients. Our model learns the mapping between 2D and 3D domains and generalizes across patients without retraining. We demonstrate that our framework produces high-fidelity volumetric reconstructions in real-time, potentially supporting clinical workflows without hardware modifications. This approach could reduce imaging dose and treatment time while preserving comprehensive anatomical information, offering a pathway for safer, more efficient prostate radiotherapy workflows.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
15
issue
1
article number
36106
publisher
Nature Publishing Group
external identifiers
  • pmid:41094004
  • scopus:105018836627
ISSN
2045-2322
DOI
10.1038/s41598-025-23781-7
language
English
LU publication?
yes
id
d76cea11-ae54-46f2-af23-98d4d973459c
date added to LUP
2025-12-11 14:23:32
date last changed
2025-12-12 03:07:56
@article{d76cea11-ae54-46f2-af23-98d4d973459c,
  abstract     = {{<p>Precise patient positioning and daily anatomical verification are crucial in external beam radiotherapy to ensure accurate dose delivery and minimize harm to healthy tissues. However, Current image-guided radiotherapy techniques struggle to balance high-quality volumetric anatomical visualization and rapid low-dose imaging. Addressing this, reconstructing volumetric images from ultra-sparse X-ray projections holds promise for significantly reducing patient radiation exposure and potentially enabling real-time anatomy verification. Here, we present a novel DL-based framework that generates synthetic volumetric cone-beam CT in real-time from two orthogonal projection views and a reference planning CT for prostate cancer patients. Our model learns the mapping between 2D and 3D domains and generalizes across patients without retraining. We demonstrate that our framework produces high-fidelity volumetric reconstructions in real-time, potentially supporting clinical workflows without hardware modifications. This approach could reduce imaging dose and treatment time while preserving comprehensive anatomical information, offering a pathway for safer, more efficient prostate radiotherapy workflows.</p>}},
  author       = {{Kadhim, Mustafa and Persson, Emilia and Haraldsson, André and Gustafsson, Christian Jamtheim and Nilsson, Mikael and Kügele, Malin and Bäck, Sven and Ceberg, Sofie}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Anatomically informed deep learning framework for generating fast, low-dose synthetic CBCT for prostate radiotherapy}},
  url          = {{http://dx.doi.org/10.1038/s41598-025-23781-7}},
  doi          = {{10.1038/s41598-025-23781-7}},
  volume       = {{15}},
  year         = {{2025}},
}