# The Role of a DirectDensity(r) CT Reconstruction in a Radiotherapy Workflow: A Phantom Study
## 1 Introduction  
In radiation therapy, computed tomography (CT) images of the patients are commonly used to both identify anatomical structures of clinical interest (targets and organs at risk, or OAR) and to provide a density map of the patient, through which the dose calculation using treatment planning systems (TPSs) is performed. To derive this density map from the CT scan, it is necessary to implement in the TPS a conversion curve between Hounsfield units (HU) and relative electron density (RED) or mass density (MD) in the form of a calibration curve [1, 2]. However, this curve depends on the kVp set during acquisition [3, 4]. This is due to the beam energy dependence of the linear attenuation coefficients defining the HUs. Images acquired at different kVp values would therefore need different calibration curves in the TPS [2, 5]. However, keeping all of these curves properly controlled and calibrated is a time and energy consuming procedure that can become problematic in an often-demanding clinical routine. Therefore, most clinical centers prefer to acquire CTsfor radiation therapy purposes always at a single value or, at most, at two different values of kVp, one of which is dedicated to pediatric acquisitions. Typical values are 80 kVp for pediatric patients (to reduce the improper dose) and 120 kVp in all other cases [6].  
This choice allows one to implement a single (or double) calibration curve in the TPS, lightening the workload and reducing the risk factor associated with the right curve selection in the planning phase of the workflow. However, in some clinical cases, it might be convenient to acquire it at different kVp values (e.g., to increase image quality), thus allowing for a better contouring of the region of interest, or to reduce the absorbed dose [7; 8; 9].  
The novel reconstruction algorithm DirectDensity(r) (DD) (Siemens Healthcare, Erlangen, Germany) allows one to represent the CT scan in CT numbers directly related to the RED of the involved materials [10; 11; 12]. In contrast to the classical HU, related to the linear attenuation coefficient of the materials, these new units (indicated as HUDD) are independent of the beam energy used during the acquisition as the RED is an energy independent quantity. This allows one to obtain a single linear relationship between the CT numbers and material density, not influenced by the tube voltage value. Therefore, it is possible to implement a single calibration curve in the TPS, while keeping all the advantages of acquiring one at different kVp. Thus, the application of the DirectDensity(r) algorithm in the clinical practice may help make the radiotherapy workflow more robust, simple, and personalized.  
This work evaluates the applicability of the DirectDensity(r) algorithm in the treatment planning workflow in both the standard conditions and in the presence of high-density materials such as metal implants.  
The validity of the algorithm in standard conditions was investigated by comparing the calibration curves at different kVp, built from CT images of a phantom including materials in a density range characteristic of the human body.  
Consequences of introducing metallic materials in the phantom are explored to evaluate the applicability of DirectDensity(r) in clinical cases involving patients with metal implants. In particular, the dependence of calibration curves on kVp at such high densities was investigated and dose distributions calculated on CT scans of the phantom acquired with metal inserts were compared to the ones calculated on the CT scan of the same phantom without them, the latter having the same metal inserts densities, but only as an overwrite representing the ground truth.  
Finally, a combination of the DD algorithm with a metal reduction algorithm, the iterative metal artifact reduction (iMAR, Siemens Healthcare--Erlangen, Germany) [13; 14], was tested on both the calibration curves and dose distributions.