4-D Tomographic Inference: Application to SPECT and MR-driven PET

Emission tomographic imaging is framed in the Bayesian and information theoretic framework. The first part of the thesis is inspired by the new possibilities offered by PET-MR systems, formulating models and algorithms for 4-D tomography and for the integration of information from multiple imaging modalities. The second part of the thesis extends the models described in the first part, focusing on the imaging hardware. Three key aspects for the design of new imaging systems are investigated: criteria and efficient algorithms for the optimisation and real-time adaptation of the parameters of the imaging hardware; learning the characteristics of the imaging hardware; exploiting the rich information provided by depthof- interaction (DOI) and energy resolving devices. The document concludes with the description of the NiftyRec software toolkit, developed to enable 4-D multi-modal tomographic inference

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)

Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, the dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach: We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended cardiac torso (XCAT) phantom to simulate different lung motion/anatomy scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results: STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting based neural representation method. STINR tracks the lung tumor to an averaged center-of-mass error of <2 mm, with corresponding relative errors of reconstructed dynamic CBCTs <10%

Improving quantification in non-TOF 3D PET/MR by incorporating photon energy information

Hybrid PET/MR systems combine functional information obtained from positron emission tomography (PET) and anatomical information from magnetic resonance (MR) imaging. In spite of the advantages that such systems can offer, PET attenuation correction still represents one of the biggest challenges for imaging in the thorax. This is due to the fact that the MR signal is not directly correlated to gamma-photon attenuation. In current practice, pre-defined population-based attenuation values are used. However, this approach is prone to errors in tissues such as the lung where a variability of attenuation values can be found both within and between patients. A way to overcome this limitation is to exploit the fact that stand-alone PET emission data contain information on both the distribution of the radiotracer and photon attenuation. However, attempts to estimate the attenuation map from emission data only have shown limited success unless time-of-flight PET data is available. Several groups have investigated the possibility of using scattered data as an additional source of information to overcome re- construction ambiguities. This thesis presents work to extend the previous methods by using PET emission data acquired at multiple energy windows and incorporating prior information derived from MR. This thesis is organised as follows. We first cover both the literature and mathematical theory relevant to the framework. Then, we present and discuss results on the case of attenu- ation estimation from scattered data only, when the activity distribution is known. We then give an overview of several candidates for joint reconstruction, which reconstruct both the activity and attenuation from scattered and unscattered data. We present extensive results using simulated data and compare the proposed methods to state-of-the-art MLAA from a single energy window acquisition. We conclude with suggestions for future work to bring the proposed method into clinical practice

Investigating the adjacent patient radiation dose received during a simulated ward chest X-ray examination

Introduction: A patient having a chest X-ray will inevitably be exposed to radiation from the primary beam. Using a light beam diaphragm (LBD) on the X-ray tube reduces scattered radiation at the X-ray tube through longitudinal and horizontal collimation. But not scattered secondary radiation resulting from interactions of the primary beam. This study aimed to investigate whether lead protection on simulated hospital ward inpatients (opposite and adjacent to a simulated chest X-ray examination) would change the secondary scattered radiation dose received. Method: Two rando phantoms (simulated patients) were positioned at different distances from the simulated patient receiving the chest X-ray. The phantoms were positioned one metre adjacent (either side of the phantom being X-rayed) and two metres opposite. The scattered radiation dose to radiosensitive organs (thyroid, breast, and gonads) was recorded using Thermoluminescent Dosimeters (TLDs). Six exposures were conducted, three with lead protection and three without. The mean radiation dose and standard deviation were compared using a paired two-sample t-test for statistical significance (p>0.05). Results: The lead protection reduced the radiation dose to the radiosensitive organs by 64%-100% (p=0.51-0.18) one metre adjacent and 65%-100% (p=0.65-0.18) two metres opposite. Noticeably the phantom two metres opposite had substantial individual organ dose reductions due to the distance from the primary beam. Conclusion: Lead aprons, thyroid collars, and distance reduced the radiation dose to the radiosensitive organs of the surrounding phantoms (simulated patients) from an adjacent chest X-ray examination and present opportunities for dose reduction techniques during ward chest X-ray examinations

Development of methods for time efficient scatter correction and improved attenuation correction in time-of-flight PET/MR

In der vorliegenden Dissertation wurden zwei fortdauernde Probleme der Bildrekonstruktion in der time-of-flight (TOF) PET bearbeitet: Beschleunigung der TOF-Streukorrektur sowie Verbesserung der emissionsbasierten Schwächungskorrektur. Aufgrund der fehlenden Möglichkeit, die Photonenabschwächung direkt zu messen, ist eine Verbesserung der Schwächungskorrektur durch eine gemeinsame Rekonstruktion der Aktivitäts- und Schwächungskoeffizienten-Verteilung mittels der MLAA-Methode von besonderer Bedeutung für die PET/MRT, während eine Beschleunigung der TOF-Streukorrektur gleichermaßen auch für TOF-fähige PET/CT-Systeme relevant ist. Für das Erreichen dieser Ziele wurde in einem ersten Schritt die hochauflösende PET-Bildrekonstruktion THOR, die bereits zuvor in unserer Gruppe entwickelt wurde, angepasst, um die TOF-Information nutzen zu können, welche von allen modernen PET-Systemen zur Verfügung gestellt wird. Die Nutzung der TOF-Information in der Bildrekonstruktion führt zu reduziertem Bildrauschen und zu einer verbesserten Konvergenzgeschwindigkeit. Basierend auf diesen Anpassungen werden in der vorliegenden Arbeit neue Entwicklungen für eine Verbesserung der TOF-Streukorrektur und der MLAA-Rekonstruktion beschrieben. Es werden sodann Ergebnisse vorgestellt, welche mit den neuen Algorithmen am Philips Ingenuity PET/MRT-Gerät erzielt wurden, das gemeinsam vom Helmholtz-Zentrum Dresden-Rossendorf (HZDR) und dem Universitätsklinikum betrieben wird. Eine wesentliche Voraussetzung für eine quantitative TOF-Bildrekonstruktionen ist eine Streukorrektur, welche die TOF-Information mit einbezieht. Die derzeit übliche Referenzmethode hierfür ist eine TOF-Erweiterung des single scatter simulation Ansatzes (TOF-SSS). Diese Methode wurde im Rahmen der TOF-Erweiterung von THOR implementiert. Der größte Nachteil der TOF-SSS ist eine 3–7-fach erhöhte Rechenzeit für die Berechnung der Streuschätzung im Vergleich zur non-TOF-SSS, wodurch die Bildrekonstruktionsdauer deutlich erhöht wird. Um dieses Problem zu beheben, wurde eine neue, schnellere TOF-Streukorrektur (ISA) entwickelt und implementiert. Es konnte gezeigt werden, dass dieser neue Algorithmus eine brauchbare Alternative zur TOF-SSS darstellt, welche die Rechenzeit auf ein Fünftel reduziert, wobei mithilfe von ISA und TOF-SSS rekonstruierte Schnittbilder quantitativ ausgezeichnet übereinstimmen. Die Gesamtrekonstruktionszeit konnte mithilfe ISA bei Ganzkörperuntersuchungen insgesamt um den Faktor Zwei reduziert werden. Dies kann als maßgeblicher Fortschritt betrachtet werden, speziell im Hinblick auf die Nutzung fortgeschrittener Bildrekonstruktionsverfahren im klinischen Umfeld. Das zweite große Thema dieser Arbeit ist ein Beitrag zur verbesserten Schwächungskorrektur in der PET/MRT mittels MLAA-Rekonstruktion. Hierfür ist zunächst eine genaue Kenntnis der tatsächlichen Zeitauflösung in der betrachten PET-Aufnahme zwingend notwendig. Da die vom Hersteller zur Verfügung gestellten Zahlen nicht immer verlässlich sind und zudem die Zählratenabhängigkeit nicht berücksichtigen, wurde ein neuer Algorithmus entwickelt und implementiert, um die Zeitauflösung in Abhängigkeit von der Zählrate zu bestimmen. Dieser Algorithmus (MLRES) basiert auf dem maximum likelihood Prinzip und erlaubt es, die funktionale Abhängigkeit der Zeitauflösung des Philips Ingenuity PET/MRT von der Zählrate zu bestimmen. In der vorliegenden Arbeit konnte insbesondere gezeigt werden, dass sich die Zeitauflösung des Ingenuity PET/MRT im klinisch relevanten Zählratenbereich um mehr als 250 ps gegenüber der vom Hersteller genannten Auflösung von 550 ps verschlechtern kann, welche tatsächlich nur bei extrem niedrigen Zählraten erreicht wird. Basierend auf den oben beschrieben Entwicklungen konnte MLAA in THOR integriert werden. Die MLAA-Implementierung erlaubt die Generierung realistischer patientenspezifischer Schwächungsbilder. Es konnte insbesondere gezeigt werden, dass auch Knochen und Hohlräume korrekt identifiziert werden, was mittels MRT-basierter Schwächungskorrektur sehr schwierig oder sogar unmöglich ist. Zudem konnten wir bestätigen, dass es mit MLAA möglich ist, metallbedingte Artefakte zu reduzieren, die ansonsten in den MRT-basierten Schwächungsbildern immer zu finden sind. Eine detaillierte Analyse der Ergebnisse zeigte allerdings verbleibende Probleme bezüglich der globalen Skalierung und des lokalen Übersprechens zwischen Aktivitäts- und Schwächungsschätzung auf. Daher werden zusätzliche Entwicklungen erforderlich sein, um auch diese Defizite zu beheben.The present work addresses two persistent issues of image reconstruction for time-of-flight (TOF) PET: acceleration of TOF scatter correction and improvement of emission-based attenuation correction. Due to the missing capability to measure photon attenuation directly, improving attenuation correction by joint reconstruction of the activity and attenuation coefficient distribution using the MLAA technique is of special relevance for PET/MR while accelerating TOF scatter correction is of equal importance for TOF-capable PET/CT systems as well. To achieve the stated goals, in a first step the high-resolution PET image reconstruction THOR, previously developed in our group, was adapted to take advantage of the TOF information delivered by state-of-the-art PET systems. TOF-aware image reconstruction reduces image noise and improves convergence rate both of which is highly desirable. Based on these adaptations, this thesis describes new developments for improvement of TOF scatter correction and MLAA reconstruction and reports results obtained with the new algorithms on the Philips Ingenuity PET/MR jointly operated by the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the University Hospital. A crucial requirement for quantitative TOF image reconstruction is TOF-aware scatter correction. The currently accepted reference method — the TOF extension of the single scatter simulation approach (TOF-SSS) — was implemented as part of the TOF-related modifications of THOR. The major drawback of TOF-SSS is a 3–7 fold increase in computation time required for the scatter estimation, compared to regular SSS, which in turn does lead to a considerable image reconstruction slowdown. This problem was addressed by development and implementation of a novel accelerated TOF scatter correction algorithm called ISA. This new algorithm proved to be a viable alternative to TOF-SSS and speeds up scatter correction by a factor of up to five in comparison to TOF-SSS. Images reconstructed using ISA are in excellent quantitative agreement with those obtained when using TOF-SSS while overall reconstruction time is reduced by a factor of two in whole-body investigations. This can be considered a major achievement especially with regard to the use of advanced image reconstruction in a clinical context. The second major topic of this thesis is contribution to improved attenuation correction in PET/MR by utilization of MLAA reconstruction. First of all, knowledge of the actual time resolution operational in the considered PET scan is mandatory for a viable MLAA implementation. Since vendor-provided figures regarding the time resolution are not necessarily reliable and do not cover count-rate dependent effects at all, a new algorithm was developed and implemented to determine the time resolution as a function of count rate. This algorithm (MLRES) is based on the maximum likelihood principle and allows to determine the functional dependency of the time resolution of the Philips Ingenuity PET/MR on the given count rate and to integrate this information into THOR. Notably, the present work proves that the time resolution of the Ingenuity PET/MR can degrade by more than 250 ps for the clinically relevant range of count rates in comparison to the vendor-provided figure of 550 ps which is only realized in the limit of extremely low count rates. Based on the previously described developments, MLAA could be integrated into THOR. The performed list-mode MLAA implementation is capable of deriving realistic, patient-specific attenuation maps. Especially, correct identification of osseous structures and air cavities could be demonstrated which is very difficult or even impossible with MR-based approaches to attenuation correction. Moreover, we have confirmed that MLAA is capable of reducing metal-induced artifacts which are otherwise present in MR-based attenuation maps. However, the detailed analysis of the obtained MLAA results revealed remaining problems regarding stability of global scaling as well as local cross-talk between activity and attenuation estimates. Therefore, further work beyond the scope of the present work will be necessary to address these remaining issues