Computed Tomography simulation Imaging, the foundation of Radiation Therapy

Within the radiation therapy workflow, the initial image is going to be compared to the positioning image at the treatment workstation for accurate patient positioning. By having a good starting point, the introduction of random errors can potentially be reduced.¹¹

SOMATOM go.Sim and SOMATOM go.Open Pro have been designed to provide clinicians the optimal starting point to support modern treatment approaches. It comes with tools that are necessary to manage the complexities of individual cases.

The CT planning image is the foundation for the whole radiation therapy workflow. It is key for the treatment pathway and is helping to tumor and organs-at-risk delineation, as well as the dose that the patient will receive.^3,4

Organs-at-risk contouring are a cost and time intensive task.⁵ In addition, inter-observer variability can make it difficult to achieve consistent contouring results.⁶ If we also consider that the quality of the image is influencing the OAR delination, the challenge of quality OAR-contours even increases.^7,8 

To meet these challenges, we introduced DirectORGANS: the first intelligent integrated OAR contouring. It is based on two core elements: Optimized reconstruction and deep-learning based contouring. This enables us to increase efficiency on this time-consuming task.

Imaging creates opportunities for improved therapeutic targeting of cancer. At the same time, conceptual thinking about radiotherapy target volume definition and dose prescription is not keeping up.³

With our solutions we want to enable clinicians with images that can help overcome daily hurdles when it comes to image quality and accurate planning target volume (PTV) visualization.

By using innovative iterative reconstructions, enabling the use of Dual Energy and reducing artifacts, we can help improving the tissue contrast. Therefore, we are also aiming to reduce of the variability in target delineation.⁹

In radiation therapy, the CT image also plays an important role when it comes to treatment planning. The image used for planning needs to reflect an accurate geometric model of the patient and enable dose calculation. At the same time, it needs to provide the physicians an optimal contrast to delineate the target volumes.

The streamlined solution of SOMATOM go.Sim and SOMATOM go.Open Pro aims to improve access to tailored treatment. This is done by enabling improved tissue contrast and intelligent real-time breathing adaptation¹⁰ – driving towards enhanced image quality for confident treatment planning. It has been developed aiming to reduce the likelihood of errors in a complex RT workflow and therefore potentially reduce time to treatment.

Within the radiation therapy workflow, the initial image is going to be compared to the positioning image at the treatment workstation for accurate patient positioning. By having a good starting point, the introduction of random errors can potentially be reduced.¹¹

SOMATOM go.Sim and SOMATOM go.Open Pro have been designed to provide clinicians the optimal starting point to support modern treatment approaches. It comes with tools that are necessary to manage the complexities of individual cases.