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- Magnander, Tobias, et al.
(författare)
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A fast GPU code for full Monte Carlo based SPECT reconstruction
- 2016
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer. - 1619-7070 .- 1619-7089.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- To improve image quality in SPECT/CT reconstructions, various approximate recovery resolution techniques have been developed and implemented in clinical practice. However, optimal image reconstruction requires accounting for all physical interactions of the emitted photons in the individual patient. The objectives for this study were to develop a novel Monte Carlo (MC) code for fast simulation of individual image projections, and to implement these projections in ordered subset expectation maximum (OSEM) reconstructions of SPECT/CTimages.Method: The MC code was written in Compute Unified Device Architecture language for a computer with four graphic processing units (GeForce GTX Titan X, Nvidia, USA). This enables simulations of parallel photon emission from the voxels matrix (1283 or 2563). Each CT number was converted to attenuation coefficients for photo absorption, coherent scattering and incoherent scattering. The type of interaction was determined by the ratio of attenuation coefficients in the CT voxels. For photon scattering the deflection angle was determined by the differential scattering cross sections. The accepted angle for photon interaction with the crystal was determined from the diameter and height of the collimator hole. Predefined energy and spatial resolution kernels for the crystal were used. TheMCcode was implemented intoOSEMreconstruction of 177Lu, 111In and 99mTc SPECT/CT images. The National Electrical Manufacturers Association (NEMA) image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with clinical standard OSEM reconstructions and clinical state-of-the-art OSEM reconstructions with recovery resolution corrections. Results and conclusion: The performance of the MC code was 500 millions photons/s. The required number of photons emitted per voxel for obtaining low noise in the simulated image was 400 for a 1283 voxel matrix. With this number of emitted photons/voxel the MC-based OSEM reconstruction with 10 subsets was performed within 60 s/iteration. The images converged after 2-4 iterations, depending on the sphere sizes in the NEMAphantom. Thereby, the reconstruction time was <4 minutes. The contrast-to-noise level was slightly improved with increased number of emitted photons/voxel, and the reconstruction time was linearly depending on the number of emitted photons/voxel. The signal-to-background for the spheres in the NEMA phantom was clearly improved with MC-based OSEM reconstruction: e.g. for 177Lu the improvement was 37% compared to standard OSEM and 20 % compared to state-of-the-art OSEM. Furthermore, visual inspection of clinical investigations revealed clearly improved resolution and contrast with MC-based reconstruction.
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- Steffen, A.C, et al.
(författare)
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Affibody mediated tumor targeting of HER-2 expressing xenografts in mice
- 2006
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 33:6, s. 631-638
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Targeted delivery of radionuclides for diagnostic and therapeutic applications has until recently largely been limited to receptor ligands, antibodies and antibody-derived molecules. Here, we present a new type of molecule, a 15-kDa bivalent affibody called (Z(HER2:4))(2), with potential for such applications. The (Z(HER2:4))(2) affibody showed high apparent affinity (K (D)=3 nM) towards the oncogene product HER-2 (also called p185/neu or c-erbB-2), which is often overexpressed in breast and ovarian cancers. The purpose of this study was to investigate the in vivo properties of the new targeting agent. METHODS: The biodistribution and tumour uptake of the radioiodinated (Z(HER2:4))(2) affibody was studied in nude mice carrying tumours from xenografted HER-2 overexpressing SKOV-3 cells. RESULTS: The radioiodinated (Z(HER2:4))(2) affibody was primarily excreted through the kidneys, and significant amounts of radioactivity were specifically targeted to the tumours. The blood-borne radioactivity was, at all times, mainly in the macromolecular fraction. A tumour-to-blood ratio of about 10:1 was obtained 8 h post injection, and the tumours could be easily visualised with a gamma camera at this time point. CONCLUSION: The results indicate that the (Z(HER2:4))(2) affibody is an interesting candidate for applications in nuclear medicine, such as radionuclide-based tumour imaging and therapy.
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| 5. |
- Tran, Thuy A., 1980-, et al.
(författare)
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Design, synthesis and biological evaluation of a multifunctional HER2-specific Affibody molecule for molecular imaging
- 2009
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 36:11, s. 1864-1873
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this study was to design and evaluate a novel platform for labelling of Affibody molecules, enabling for both recombinant and synthetic production and for site-specific labelling with 99mTc or trivalent radiometals. Methods: The HER2-specific Affibody molecule PEP05352 was made by peptide synthesis. The chelator sequence SECG (serine-glutamic acid-cysteine-glycine) was anchored on the C-terminal to allow 99mTc-labelling. The cysteine can alternatively serve as a conjugation site of the chelator DOTA for indium-labelling. The resulting 99mTc- and 111In-labelled Affibody molecules were evaluated both in vitro and in vivo. Results: Both conjugates retained their capacity to bind to HER2 receptors in vitro and in vivo. The tumour-to-blood ratio in LS174T xenografts was 30 at 4 h p.i. for both conjugates. Biodistribution data showed that 99mTc-labelled Affibody molecule had 4-fold lower kidney accumulation compared with 111In-labelled Affibody molecule while the accumulation in other organs was similar. Gamma-camera imaging of the conjugates could clearly visualise the tumours 4 h after injection. Conclusions: Incorporation of C-terminal SECG sequence in Affibody molecules provides a general multifunctional platform for site-specific labelling with different nuclides (technetium, indium, gallium, cobalt, or yttrium) and for a flexible production (chemical synthesis or recombinant).
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- Etminani, Kobra, 1984-, et al.
(författare)
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A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
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