| 1. |
- Gear, Jonathan, et al.
(author)
-
EANM enabling guide : how to improve the accessibility of clinical dosimetry
- 2023
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer. - 1619-7070 .- 1619-7089. ; 50, s. 1861-1868
-
Journal article (peer-reviewed)abstract
- Dosimetry can be a useful tool for personalization of molecular radiotherapy (MRT) procedures, enabling the continuous development of theranostic concepts. However, the additional resource requirements are often seen as a barrier to implementation. This guide discusses the requirements for dosimetry and demonstrates how a dosimetry regimen can be tailored to the available facilities of a centre. The aim is to help centres wishing to initiate a dosimetry service but may not have the experience or resources of some of the more established therapy and dosimetry centres. The multidisciplinary approach and different personnel requirements are discussed and key equipment reviewed example protocols demonstrating these factors are given in the supplementary material for the main therapies carried out in nuclear medicine, including [I-131]-NaI for benign thyroid disorders, [Lu-177]-DOTATATE and I-131-mIBG for neuroendocrine tumours and [Y-90]-microspheres for unresectable hepatic carcinoma.
|
|
| 2. |
- Slart, Riemer H. J. A., et al.
(author)
-
Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT
- 2021
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer. - 1619-7070 .- 1619-7089. ; 48:5, s. 1399-1413
-
Journal article (peer-reviewed)abstract
- In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
|
|
