Radiotherapy planning begins with CT, and every decision that follows, from target definition to dose calculation, depends on the quality and consistency of that first dataset. Siemens Healthineers is extending photon-counting CT into this part of the pathway with the NAEOTOM Alpha.Prime RT Pro Edition, bringing spectral information, high spatial resolution and radiotherapy workflow functions into a single simulation platform.
From diagnostic imaging to treatment planning
The system captures spectral information in every scan. That allows planning teams to reconstruct virtual monoenergetic images and quantitative datasets after acquisition, rather than choosing a single image contrast before the patient leaves the scanner. Siemens positions this as a way to support difficult delineation tasks while reducing the need for additional acquisitions.
The clinical argument is not simply sharper imaging. In radiotherapy, improved visualisation matters when it changes confidence around tumour boundaries, organs at risk or small structures close to the target. Siemens reports clinical examples in which small liver lesions of around 4 mm are visible on contrast-enhanced photon-counting CT, while spectral reconstructions can help distinguish tissues that may be difficult to separate on conventional images.
Automation moves contouring closer to acquisition
A second part of the proposition is workflow. DirectORGANS uses deep-learning algorithms to contour organs of interest directly at the CT scanner, with the aim of reducing manual delineation before treatment planning. The software is designed to pass the resulting contours into the radiotherapy workflow rather than requiring a separate manual step.
Siemens cites a 70% time saving in organ-at-risk delineation compared with manual contouring from a clinical evaluation at Universitätsklinikum Erlangen. That figure needs to be read carefully. The evaluation was performed with DirectORGANS on a SOMATOM go.Open Pro and syngo.via software, so it is evidence for the contouring workflow rather than a NAEOTOM-specific performance result. Even so, the operational issue is important. Contouring remains one of the most labour-intensive and variable parts of planning, and automation at the point of simulation could shift specialist time towards review and complex cases.
Independent evidence is encouraging, but still early
A prospective study from University Hospital Zurich provides independent clinical evidence for photon-counting CT in radiotherapy planning. Twelve patients with head-and-neck cancer underwent both conventional energy-integrating detector CT and photon-counting CT. Three physicians delineated clinical target volumes and organs at risk across the datasets.
The investigators found comparable overall delineation agreement, with significantly better delineation for the lips and oral cavity when 140 keV and 50 keV photon-counting reconstructions were combined. Contouring confidence was also higher with photon-counting CT. The ratio of mean CT dose index between conventional CT and photon-counting CT was 2.72 across the cohort, indicating a lower acquisition dose with photon-counting CT for most patients.
With twelve patients, the study cannot settle the comparison. It does, however, address a clinically relevant question with prospective patient data and supports the feasibility of photon-counting CT for treatment-planning delineation. The authors also disclose relevant industry relationships: the Zurich radiation oncology department has a research agreement with Siemens Healthineers, and Patrick Wohlfahrt is an employee in Siemens Healthineers' research and development team.
Where the value lies
For oncology leaders, the significance lies in whether one imaging platform can improve the quality of simulation while removing steps from the planning pathway. Features such as DirectORGANS, electron-density imaging and stopping-power-ratio calculation are intended to reduce hand-offs between acquisition, contouring and treatment planning.
The capital case will vary by institution. Shared use between diagnostic radiology and radiation oncology could improve utilisation, but only where local scheduling, governance and physical siting support it. Several workflow features are optional and market availability differs. The evidence base is also still developing.
Photon-counting CT therefore arrives in radiotherapy with its diagnostic case largely made and its planning case still open. The next test is whether the added spectral information and workflow automation translate into more reproducible planning, less manual work and clinically meaningful gains in treatment delivery.
Source & Image Credit: Siemens Healthineers
References:
Bertini N et al. First implementation of photon-counting detector computed tomography for optimizing segmentation in head-and-neck cancer radiotherapy. Physics and Imaging in Radiation Oncology. 2025;36:100864. Full text
