| 1. |
- Benlloch, Jose M., et al.
(författare)
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The MINDVIEW project : First results
- 2018
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Ingår i: European psychiatry. - : Cambridge University Press (CUP). - 0924-9338 .- 1778-3585. ; 50, s. 21-27
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Tidskriftsartikel (refereegranskat)abstract
- We present the first results of the MINDVIEW project. An innovative imaging system for the human brain examination, allowing simultaneous acquisition of PET/MRI images, has been designed and constructed. It consists of a high sensitivity and high resolution PET scanner integrated in a novel, head-dedicated, radio frequency coil for a 3T MRI scanner. Preliminary measurements from the PET scanner show sensitivity 3 times higher than state-of-the-art PET systems that will allow safe repeated studies on the same patient. The achieved spatial resolution, close to 1 mm, will enable differentiation of relevant brain structures for schizophrenia. A cost-effective and simple method of radiopharmaceutical production from 11C-carbon monoxide and a mini-clean room has been demonstrated. It has been shown that 11C-raclopride has higher binding potential in a new VAAT null mutant mouse model of schizophrenia compared to wild type control animals. A significant reduction in TSPO binding has been found in gray matter in a small sample of drug-naïve, first episode psychosis patients, suggesting a reduced number or an altered function of immune cells in brain at early stage schizophrenia.
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| 2. |
- Hering, Alessa, et al.
(författare)
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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
- 2023
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 42:3, s. 697-712
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Tidskriftsartikel (refereegranskat)abstract
- Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
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| 3. |
- Koole, Michel, et al.
(författare)
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EANM guidelines for PET-CT and PET-MR routine quality control
- 2023
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Ingår i: Zeitschrift für Medizinische Physik. - : Elsevier. - 0939-3889 .- 1876-4436. ; 33:1, s. 103-113
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Tidskriftsartikel (refereegranskat)abstract
- We present guidelines by the European Association of Nuclear Medicine (EANM) for routine quality control (QC) of PET-CT and PET-MR systems. These guidelines are partially based on the current EANM guidelines for routine quality control of Nuclear Medicine instrumentation but focus more on the inherent multimodal aspect of the current, state-of-the-art PET-CT and PET-MR scanners. We briefly discuss the regulatory context put forward by the International Electrotechnical Commission (IEC) and European Commission (EC) and consider relevant guidelines and recommendations by other societies and professional organizations. As such, a comprehensive overview of recommended quality control procedures is provided to ensure the optimal operational status of a PET system, integrated with either a CT or MR system. In doing so, we also discuss the rationale of the different tests, advice on the frequency of each test and present the relevant MR and CT tests for an integrated system. In addition, we recommend a scheme of preventive actions to avoid QC tests from drifting out of the predefined range of acceptable performance values such that an optimal performance of the PET system is maintained for routine clinical use.
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| 4. |
- Slart, Riemer H. J. A., et al.
(författare)
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Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT
- 2021
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer. - 1619-7070 .- 1619-7089. ; 48:5, s. 1399-1413
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Tidskriftsartikel (refereegranskat)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.
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