Christian Gustafsson1,2, Juha Korhonen3,4,5, Emilia Persson1,2, Adalsteinn Gunnlaugsson1, Tufve Nyholm6,7, Lars E Olsson2. 1. Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, 221 85, Sweden. 2. Department of Medical Radiation Physics, Lund University, Malmö, 205 02, Sweden. 3. Department of Nuclear Medicine, Helsinki University Central Hospital, Helsinki, 00290, Finland. 4. Department of Radiology, Helsinki University Central Hospital, Helsinki, 00290, Finland. 5. Department of Radiation Therapy, Comprehensive Cancer Center, Helsinki University Central Hospital, Helsinki, 00290, Finland. 6. Department of Radiation Sciences, Umeå University, Umeå, 90187, Sweden. 7. Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, 95105, Sweden.
Abstract
PURPOSE: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration. METHODS: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images. RESULTS: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 ± 1.25 mm and 1.14 ± 1.06 mm, respectively. CONCLUSIONS: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.
PURPOSE: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration. METHODS: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancerpatients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images. RESULTS: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 ± 1.25 mm and 1.14 ± 1.06 mm, respectively. CONCLUSIONS: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.
Authors: Matteo Maspero; Peter R Seevinck; Nicole J W Willems; Gonda G Sikkes; Geja J de Kogel; Hans C J de Boer; Jochem R N van der Voort van Zyp; Cornelis A T van den Berg Journal: Radiat Oncol Date: 2018-06-05 Impact factor: 3.481
Authors: Adalsteinn Gunnlaugsson; Emilia Persson; Christian Gustafsson; Elisabeth Kjellén; Petra Ambolt; Silke Engelholm; Per Nilsson; Lars E Olsson Journal: Phys Imaging Radiat Oncol Date: 2019-03-20
Authors: Karin Goudschaal; F Beeksma; M Boon; M Bijveld; J Visser; K Hinnen; Z van Kesteren Journal: Radiat Oncol Date: 2021-02-19 Impact factor: 3.481