TY - JOUR A1 - Tran-Gia, Johannes A1 - Wech, Tobias A1 - Bley, Thorsten A1 - Köstler, Herbert T1 - Model-Based Acceleration of Look-Locker T1 Mapping JF - PLoS One N2 - Mapping the longitudinal relaxation time \(T_1\) has widespread applications in clinical MRI as it promises a quantitative comparison of tissue properties across subjects and scanners. Due to the long scan times of conventional methods, however, the use of quantitative MRI in clinical routine is still very limited. In this work, an acceleration of Inversion-Recovery Look-Locker (IR-LL) \(T_1\) mapping is presented. A model-based algorithm is used to iteratively enforce an exponential relaxation model to a highly undersampled radially acquired IR-LL dataset obtained after the application of a single global inversion pulse. Using the proposed technique, a \(T_1\) map of a single slice with 1.6mm in-plane resolution and 4mm slice thickness can be reconstructed from data acquired in only 6s. A time-consuming segmented IR experiment was used as gold standard for \(T_1\) mapping in this work. In the subsequent validation study, the model-based reconstruction of a single-inversion IR-LL dataset exhibited a \(T_1\) difference of less than 2.6% compared to the segmented IR-LL reference in a phantom consisting of vials with \(T_1\) values between 200ms and 3000ms. In vivo, the \(T_1\) difference was smaller than 5.5% in WM and GM of seven healthy volunteers. Additionally, the \(T_1\) values are comparable to standard literature values. Despite the high acceleration, all model-based reconstructions were of a visual quality comparable to fully sampled references. Finally, the reproducibility of the \(T_1\) mapping method was demonstrated in repeated acquisitions. In conclusion, the presented approach represents a promising way for fast and accurate \(T_1\) mapping using radial IR-LL acquisitions without the need of any segmentation. KW - algorithms KW - cerebrospinal fluid KW - NMR relaxation KW - data acquisition KW - relaxation (physics) KW - relaxation time KW - central nervous system KW - magnetic resonance imaging Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-126436 VL - 10 IS - 4 ER - TY - JOUR A1 - Tran-Gia, Johannes A1 - Denis-Bacelar, Ana M. A1 - Ferreira, Kelley M. A1 - Robinson, Andrew P. A1 - Calvert, Nicholas A1 - Fenwick, Andrew J. A1 - Finocchiaro, Domenico A1 - Fioroni, Federica A1 - Grassi, Elisa A1 - Heetun, Warda A1 - Jewitt, Stephanie J. A1 - Kotzassarlidou, Maria A1 - Ljungberg, Michael A1 - McGowan, Daniel R. A1 - Scott, Nathaniel A1 - Scuffham, James A1 - Gleisner, Katarina Sjögreen A1 - Tipping, Jill A1 - Wevrett, Jill A1 - Lassmann, Michael T1 - A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project JF - EJNMMI Physics N2 - Purpose Patient-specific dosimetry is required to ensure the safety of molecular radiotherapy and to predict response. Dosimetry involves several steps, the first of which is the determination of the activity of the radiopharmaceutical taken up by an organ/lesion over time. As uncertainties propagate along each of the subsequent steps (integration of the time–activity curve, absorbed dose calculation), establishing a reliable activity quantification is essential. The MRTDosimetry project was a European initiative to bring together expertise in metrology and nuclear medicine research, with one main goal of standardizing quantitative \(^{177}\)Lu SPECT/CT imaging based on a calibration protocol developed and tested in a multicentre inter-comparison. This study presents the setup and results of this comparison exercise. Methods The inter-comparison included nine SPECT/CT systems. Each site performed a set of three measurements with the same setup (system, acquisition and reconstruction): (1) Determination of an image calibration for conversion from counts to activity concentration (large cylinder phantom), (2) determination of recovery coefficients for partial volume correction (IEC NEMA PET body phantom with sphere inserts), (3) validation of the established quantitative imaging setup using a 3D printed two-organ phantom (ICRP110-based kidney and spleen). In contrast to previous efforts, traceability of the activity measurement was required for each participant, and all participants were asked to calculate uncertainties for their SPECT-based activities. Results Similar combinations of imaging system and reconstruction lead to similar image calibration factors. The activity ratio results of the anthropomorphic phantom validation demonstrate significant harmonization of quantitative imaging performance between the sites with all sites falling within one standard deviation of the mean values for all inserts. Activity recovery was underestimated for total kidney, spleen, and kidney cortex, while it was overestimated for the medulla. Conclusion This international comparison exercise demonstrates that harmonization of quantitative SPECT/CT is feasible when following very specific instructions of a dedicated calibration protocol, as developed within the MRTDosimetry project. While quantitative imaging performance demonstrates significant harmonization, an over- and underestimation of the activity recovery highlights the limitations of any partial volume correction in the presence of spill-in and spill-out between two adjacent volumes of interests. KW - quantitative SPECT/CT KW - 177Lu SPECT/CT imaging KW - standardization of SPECT/CT imaging KW - harmonization of SPECT/CT imaging KW - international multicenter comparison exercise KW - traceability of SPECT/CT imaging KW - molecular radiotherapy (MRT) KW - 3D printing KW - phantom Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270380 VL - 8 ER - TY - THES A1 - Tran-Gia, Johannes T1 - Model-Based Reconstruction Methods for MR Relaxometry T1 - Modellbasierte Rekonstruktionsmethoden für die MR-Relaxometrie N2 - In this work, a model-based acceleration of parameter mapping (MAP) for the determination of the tissue parameter T1 using magnetic resonance imaging (MRI) is introduced. The iterative reconstruction uses prior knowledge about the relaxation behavior of the longitudinal magnetization after a suitable magnetization preparation to generate a series of fully sampled k-spaces from a strongly undersampled acquisition. A Fourier transform results in a spatially resolved time course of the longitudinal relaxation process, or equivalently, a spatially resolved map of the longitudinal relaxation time T1. In its fastest implementation, the MAP algorithm enables the reconstruction of a T1 map from a radial gradient echo dataset acquired within only a few seconds after magnetization preparation, while the acquisition time of conventional T1 mapping techniques typically lies in the range of a few minutes. After validation of the MAP algorithm for two different types of magnetization preparation (saturation recovery & inversion recovery), the developed algorithm was applied in different areas of preclinical and clinical MRI and possible advantages and disadvantages were evaluated. N2 - Im Rahmen dieser Arbeit wurde ein modellbasiertes Verfahren namens MAP (engl. Model-based Acceleration of Parameter mapping) für die Bestimmung des T1-Gewebeparameters mittels Magnetresonanztomographie (MRT) entwickelt. Dieser iterative Algorithmus verwendet das Vorwissen über den nach einer Magnetisierungspräparation zu erwartenden Signalverlauf, um aus einer im Anschluss an eine initiale Präparation aufgenommene zeitliche Serie stark unterabgetasteter k-Räume eine Serie voll abgetasteter k-Räume zu generieren.Eine Fourier-Transformation dieser Serie in den Bildraum zeigt den örtlich aufgelösten zeitlichen Verlauf der longitudinalen Relaxation, was eine Kartierung des Gewebeparameters T1 ermöglicht. In seiner schnellsten Form ermöglicht dieses Verfahren die Rekonstruktion einer T1-Karte aus einem innerhalb weniger Sekunden nach einer passenden Magnetisierungspräparation aufgenommenen radialen Gradienten-Echo-Datensatz, während die Messdauer herkömmlich verwendeter T1-Bestimmungstechniken üblicherweise im Bereich von einigen Minuten liegt. Nach der Validierung des MAP-Algorithmus für zwei unterschiedliche Arten der Magnetisierungspräparation (Sättigungspräparation, Inversion) wurde die entwickelte Technik im Rahmen dieser Arbeit in verschiedenen Bereichen der präklinischen und klinischen MRT angewendet und mögliche Vor- und Nachteile untersucht. KW - Kernspintomographie KW - Radiologische Diagnostik KW - Bildgebendes Verfahren KW - Magnetic Resonance Relaxometry KW - Magnetresonanz-Relaxometrie KW - Model Based Reconstruction Algorithms in Magnetic Resonance Imaging KW - Modellbasierte-Rekonstruktionsalgorithmen in der Magnetresonanztomografie KW - Relaxation Parameter Mapping in Magnetic Resonance Imaging KW - Bestimmung des Relaxations-Parameters in der Magnetresonanztomografie Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-109774 ER - TY - JOUR A1 - Leube, Julian A1 - Gustafsson, Johan A1 - Lassmann, Michael A1 - Salas-Ramirez, Maikol A1 - Tran-Gia, Johannes T1 - Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset JF - EJNMMI Physics N2 - Background In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations. Methods A database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system. Results No statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system. Conclusion Our study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net. KW - 177Lu KW - Monte Carlo KW - SPECT KW - Deep learning KW - Denoising Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300697 SN - 2197-7364 VL - 9 ER -