Quality assurance of dose management systems

Clinical benchmarking of a commercial software for skin dose estimation in cardiac, abdominal, and neurology interventional procedures

“…Although RDM is used for patient dosimetry, a quality assurance program must be implemented to ensure robustness and quality of patient dose data. 21 This is the first prospective clinical study evaluating the estimated skin dose distribution by RDM software with GafChromic films in routine cardiac, abdominal and neurology IR procedures. A previous study 10 that involved a benchmarking of RDM solution on phantom configurations demonstrated an acceptable accuracy in calculating PSD values and computing 2D dose maps, considering measurement uncertainties associated with GafChromic films.…”

“…This solution is essential particularly when the equipment does not display directly the skin dose map. Although RDM is used for patient dosimetry, a quality assurance program must be implemented to ensure robustness and quality of patient dose data 21 . This is the first prospective clinical study evaluating the estimated skin dose distribution by RDM software with GafChromic films in routine cardiac, abdominal and neurology IR procedures.…”

CTContour: An open-source Python pipeline for automatic contouring and calculation of mean SSDE along the abdomino-pelvic region for CT images; validation on fifteen systems

“…Dose monitoring platforms capable of evaluating the SSDE from topogram images are also available. It is nonetheless important to make an independent check of the SSDE provided by these systems using own software as part of the Medical Physics quality assurance programme [27], more so if commercial calculations are based on non-linear machine learning techniques [28,29]. Commercial tools are essentially black-boxes that will not usually provide insight into the individual steps performed and may not necessarily provide a visual of each contoured slice to facilitate inspection.…”

Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy

“…Due to the variability and differences among the patients' anatomies, an imaging protocol for a different body region may be used by the clinical personnel in order to obtain better image quality. While the header can be adjusted following the CT acquisition, this is not commonly done in the clinic, which may lead to a wrong body region label [10]. Additionally, this 'Body Part Examined' tag may be completely absent in the CBCT DICOM headers, as is the case at our institution, highlighting the need for an automatic region-labeling algorithm to recognize the global patient anatomy and treatment region.…”