Enhancing interoperability and harmonisation of nuclear medicine image data and associated clinical data

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions

Long-range epidemic spreading with immunization

We study the phase transition between survival and extinction in an epidemic process with long-range interactions and immunization. This model can be viewed as the well-known general epidemic process (GEP) in which nearest-neighbor interactions are replaced by Levy flights over distances r which are distributed as P(r) ~ r^(-d-sigma). By extensive numerical simulations we confirm previous field-theoretical results obtained by Janssen et al. [Eur. Phys. J. B7, 137 (1999)].Comment: LaTeX, 14 pages, 4 eps figure

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions

Modellbasierte Rekonstruktionsmethoden für die MR-Relaxometrie

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.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

Model-Based Acceleration of Look-Locker T1 Mapping

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

Effect of kilovoltage and quality reference mAs on CT-based attenuation correction in 177Lu SPECT/CT imaging: a phantom study

Abstract Introduction CT-based attenuation correction (CT-AC) plays a major role in accurate activity quantification by SPECT/CT imaging. However, the effect of kilovoltage peak (kVp) and quality-reference mAs (QRM) on the attenuation coefficient image (μ-map) and volume CT dose index (CTDIvol) have not yet been systematically evaluated. Therefore, the aim of this study was to fill this gap and investigate the influence of kVp and QRM on CT-AC in 177Lu SPECT/CT imaging. Methods Seventy low-dose CT acquisitions of an Electron Density Phantom (seventeen inserts of nine tissue-equivalent materials) were acquired using various kVp and QRM combinations on a Siemens Symbia Intevo Bold SPECT/CT system. Using manufacturer reconstruction software, 177Lu μ-maps were generated for each CT image, and three low-dose CT related aspects were examined. First, the μ-map-based attenuation values (μ measured) were compared with theoretical values (μ theoretical). Second, changes in 177Lu activity expected due to changes in the μ-map were calculated using a modified Chang method. Third, the noise in the μ-map was assessed by measuring the coefficient of variation in a volume of interest in the homogeneous section of the Electron Density Phantom. Lastly, two phantoms were designed to simulate attenuation in four tissue-equivalent materials for two different source geometries (1-mL and 10-mL syringes). 177Lu SPECT/CT imaging was performed using three different reconstruction algorithms (xSPECT Quant, Flash3D, STIR), and the SPECT-based activities were compared against the nominal activities in the sources. Results The largest relative errors between μ measured and μ theoretical were observed in the lung inhale insert (range: 18%-36%), while it remained below 6% for all other inserts. The resulting changes in 177Lu activity quantification were -3.5% in the lung inhale insert and less than -2.3% in all other inserts. Coefficient of variation and CTDIvol ranged from 0.3% and 3.6 mGy (130 kVp, 35 mAs) to 0.4% and 0.9 mGy (80 kVp, 20 mAs), respectively. The SPECT-based activity quantification using xSPECT Quant reconstructions outperformed all other reconstruction algorithms. Conclusion This study shows that kVp and QRM values in low-dose CT imaging have a minimum effect on quantitative 177Lu SPECT/CT imaging, while the selection of low values of kVp and QRM reduce the CTDIvol