Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

Registration of medical images for applications in minimally invasive procedures

Il punto di partenza di questa tesi \ue8 l'analisi dei metodi allo stato dell'arte di registrazione delle immagini mediche per verificare se sono adatti ad essere utilizzati per assistere il medico durante una procedura minimamente invasiva , ad esempio una procedura percutanea eseguita manualmente o un intervento teleoperato eseguito per mezzo di un robot . La prima conclusione \ue8 che, anche se ci sono tanti lavori dedicati allo sviluppo di algoritmi di registrazione da applicare nel contesto medico, la maggior parte di essi non sono stati progettati per essere utilizzati nello scenario della sala operatoria (OR) anche perch\ue9, rispetto ad altre applicazioni , OR richiede anche la validazione, prestazioni in tempo reale e la presenza di altri strumenti . Gli algoritmi allo stato dell'arte sono basati su un iterazione in tre fasi : ottimizzazione - trasformazione - valutazione della somiglianza delle immagini registrate. In questa tesi, studiamo la fattibilit\ue0 dell'approccio in tre fasi per applicazioni OR, mostrando i limiti che tale approccio incontra nelle applicazioni che stiamo considerando. Verr\ue0 dimostrato come un metodo semplice si potrebbe utilizzare nella OR. Abbiamo poi sviluppato una teoria che \ue8 adatta a registrare grandi insiemi di dati non strutturati estratti da immagini mediche, tenendo conto dei vincoli della OR . Vista l'impossibilit\ue0 di lavorare con dati medici di tipo DICOM, verr\ue0 impiegato un metodo per registrare dataset composti da insiemi di punti non strutturati. Gli algoritmi proposti sono progettati per trovare la corrispondenza spaziale in forma chiusa tenendo conto del tipo di dati, il vincolo del tempo e la presenza di rumore e /o piccole deformazioni. La teoria e gli algoritmi che abbiamo sviluppato sono derivati dalla teoria delle forme proposta da Kendall (Kendall's shapes) e utilizza un descrittore globale della forma per calcolare le corrispondenze e la distanza tra le strutture coinvolte . Poich\ue9 la registrazione \ue8 solo una componente nelle applicazioni mediche, l' ultima parte della tesi \ue8 dedicata ad alcune applicazioni pratiche in OR che possono beneficiare della procedura di registrazione .The registration of medical images is necessary to establish spatial correspondences across two or more images. Registration is rarely the end-goal, but instead, the results of image registration are used in other tasks. The starting point of this thesis is to analyze which methods at the state of the art of image registration are suitable to be used in assisting a physician during a minimally invasive procedure, such as a percutaneous procedure performed manually or a teleoperated intervention performed by the means of a robot. The first conclusion is that, even if much previous work has been devoted to develop registration algorithms to be applied in the medical context, most of them are not designed to be used in the operating room scenario (OR) because, compared to other applications, the OR requires also a strong validation, real-time performance and the presence of other instruments. Almost all of these algorithms are based on a three phase iteration: optimize-transform-evaluate similarity. In this thesis, we study the feasibility of this three steps approach in the OR, showing the limits that such approach encounter in the applications we are considering. We investigate how could a simple method be realizable and what are the assumptions for such a method to work. We then develop a theory that is suitable to register large sets of unstructured data extracted from medical images keeping into account the constraints of the OR. The use of the whole radiologic information is not feasible in the OR context, therefore the method we are introducing registers processed dataset extracted from the original medical images. The framework we propose is designed to find the spatial correspondence in closed form keeping into account the type of the data, the real-time constraint and the presence of noise and/or small deformations. The theory and algorithms we have developed are in the framework of the shape theory proposed by Kendall (Kendall's shapes) and uses a global descriptor of the shape to compute the correspondences and the distance between shapes. Since the registration is only a component of a medical application, the last part of the thesis is dedicated to some practical applications in the OR that can benefit from the registration procedure

Automated analysis and visualization of preclinical whole-body microCT data

In this thesis, several strategies are presented that aim to facilitate the analysis and visualization of whole-body in vivo data of small animals. Based on the particular challenges for image processing, when dealing with whole-body follow-up data, we addressed several aspects in this thesis. The developed methods are tailored to handle data of subjects with significantly varying posture and address the large tissue heterogeneity of entire animals. In addition, we aim to compensate for lacking tissue contrast by relying on approximation of organs based on an animal atlas. Beyond that, we provide a solution to automate the combination of multimodality, multidimensional data.* Advanced School for Computing and Imaging (ASCI), Delft, NL * Bontius Stichting inz Doelfonds Beeldverwerking, Leiden, NL * Caliper Life Sciences, Hopkinton, USA * Foundation Imago, Oegstgeest, NLUBL - phd migration 201

Towards Robust and Accurate Image Registration by Incorporating Anatomical and Appearance Priors

Ph.DDOCTOR OF PHILOSOPH

3D fusion of histology to multi-parametric MRI for prostate cancer imaging evaluation and lesion-targeted treatment planning

Multi-parametric magnetic resonance imaging (mpMRI) of localized prostate cancer has the potential to support detection, staging and localization of tumors, as well as selection, delivery and monitoring of treatments. Delineating prostate cancer tumors on imaging could potentially further support the clinical workflow by enabling precise monitoring of tumor burden in active-surveillance patients, optimized targeting of image-guided biopsies, and targeted delivery of treatments to decrease morbidity and improve outcomes. Evaluating the performance of mpMRI for prostate cancer imaging and delineation ideally includes comparison to an accurately registered reference standard, such as prostatectomy histology, for the locations of tumor boundaries on mpMRI. There are key gaps in knowledge regarding how to accurately register histological reference standards to imaging, and consequently further gaps in knowledge regarding the suitability of mpMRI for tasks, such as tumor delineation, that require such reference standards for evaluation. To obtain an understanding of the magnitude of the mpMRI-histology registration problem, we quantified the position, orientation and deformation of whole-mount histology sections relative to the formalin-fixed tissue slices from which they were cut. We found that (1) modeling isotropic scaling accounted for the majority of the deformation with a further small but statistically significant improvement from modeling affine transformation, and (2) due to the depth (mean±standard deviation (SD) 1.1±0.4 mm) and orientation (mean±SD 1.5±0.9°) of the sectioning, the assumption that histology sections are cut from the front faces of tissue slices, common in previous approaches, introduced a mean error of 0.7 mm. To determine the potential consequences of seemingly small registration errors such as described above, we investigated the impact of registration accuracy on the statistical power of imaging validation studies using a co-registered spatial reference standard (e.g. histology images) by deriving novel statistical power formulae that incorporate registration error. We illustrated, through a case study modeled on a prostate cancer imaging trial at our centre, that submillimeter differences in registration error can have a substantial impact on the required sample sizes (and therefore also the study cost) for studies aiming to detect mpMRI signal differences due to 0.5 – 2.0 cm3 prostate tumors. With the aim of achieving highly accurate mpMRI-histology registrations without disrupting the clinical pathology workflow, we developed a three-stage method for accurately registering 2D whole-mount histology images to pre-prostatectomy mpMRI that allowed flexible placement of cuts during slicing for pathology and avoided the assumption that histology sections are cut from the front faces of tissue slices. The method comprised a 3D reconstruction of histology images, followed by 3D–3D ex vivo–in vivo and in vivo–in vivo image transformations. The 3D reconstruction method minimized fiducial registration error between cross-sections of non-disruptive histology- and ex-vivo-MRI-visible strand-shaped fiducials to reconstruct histology images into the coordinate system of an ex vivo MR image. We quantified the mean±standard deviation target registration error of the reconstruction to be 0.7±0.4 mm, based on the post-reconstruction misalignment of intrinsic landmark pairs. We also compared our fiducial-based reconstruction to an alternative reconstruction based on mutual-information-based registration, an established method for multi-modality registration. We found that the mean target registration error for the fiducial-based method (0.7 mm) was lower than that for the mutual-information-based method (1.2 mm), and that the mutual-information-based method was less robust to initialization error due to multiple sources of error, including the optimizer and the mutual information similarity metric. The second stage of the histology–mpMRI registration used interactively defined 3D–3D deformable thin-plate-spline transformations to align ex vivo to in vivo MR images to compensate for deformation due to endorectal MR coil positioning, surgical resection and formalin fixation. The third stage used interactively defined 3D–3D rigid or thin-plate-spline transformations to co-register in vivo mpMRI images to compensate for patient motion and image distortion. The combined mean registration error of the histology–mpMRI registration was quantified to be 2 mm using manually identified intrinsic landmark pairs. Our data set, comprising mpMRI, target volumes contoured by four observers and co-registered contoured and graded histology images, was used to quantify the positive predictive values and variability of observer scoring of lesions following the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, the variability of target volume contouring, and appropriate expansion margins from target volumes to achieve coverage of histologically defined cancer. The analysis of lesion scoring showed that a PI-RADS overall cancer likelihood of 5, denoting “highly likely cancer”, had a positive predictive value of 85% for Gleason 7 cancer (and 93% for lesions with volumes \u3e0.5 cm3 measured on mpMRI) and that PI-RADS scores were positively correlated with histological grade (ρ=0.6). However, the analysis also showed interobserver differences in PI-RADS score of 0.6 to 1.2 (on a 5-point scale) and an agreement kappa value of only 0.30. The analysis of target volume contouring showed that target volume contours with suitable margins can achieve near-complete histological coverage for detected lesions, despite the presence of high interobserver spatial variability in target volumes. Prostate cancer imaging and delineation have the potential to support multiple stages in the management of localized prostate cancer. Targeted biopsy procedures with optimized targeting based on tumor delineation may help distinguish patients who need treatment from those who need active surveillance. Ongoing monitoring of tumor burden based on delineation in patients undergoing active surveillance may help identify those who need to progress to therapy early while the cancer is still curable. Preferentially targeting therapies at delineated target volumes may lower the morbidity associated with aggressive cancer treatment and improve outcomes in low-intermediate-risk patients. Measurements of the accuracy and variability of lesion scoring and target volume contouring on mpMRI will clarify its value in supporting these roles

Reconstruction of the entire lumbar spine from partial information using Statistical Shape Models

Lumbar facet joint injections (LFJIs) are the standard of care in diagnosing the sources of and treating low back pain. The current gold standard for LFJIs is manually performed under fluoroscopic guidance. The main disadvantage is that it exposes both clinicians and patients to radiation hazards. A potential solution could be the development of an alternative, a real-time guidance system for LFJIs that operates without the need for radiation exposure. This could be realized through ultrasound imaging. However, LFJIs performed under ultrasound guidance have not yet been established in clinical routine due to the challenges in interpreting ultrasound images, where only limited 3D information could be extracted from ultrasound images. A more comprehensive and detailed understanding of the patient’s unique lumbar spine anatomy, which can be provided by a 3D lumbar spine model, could significantly enhance the procedure’s success rate. Therefore, developing a 3D lumbar spine model from the 2D ultrasound images could significantly enhance the capability of ultrasound images. The research proposes to solve a part of the challenges that reconstructing the entire lumbar spine model from limited shape information by the use of statistical shape models (SSMs). It is assumed that the shape information may be extracted from ultrasound images. To construct the SSMs of the entire lumbar spine and its components, the research introduces a registration framework to establish correspondences between the 3D shape models of the lumbar spines. The SSMs are tested rigorously using leave-one-out cross-validation (LOOCV) to determine their capability of capturing and representing anatomical variations in the population. The latter part of the thesis delves into the application of these SSMs for the reconstruction of the lumbar spine using Gaussian Process Regression (GPR). The study explores the impact of the number of data points on reconstruction accuracy and presents a detailed analysis of the minimum input information required for reconstruction within the clinically acceptable threshold. The findings from this research have potential applications in various areas related to lumbar spine reconstruction, diagnosis, and treatment. Notably, the reconstructed lumbar spine models can serve as a basis for developing real-time guidance systems for LFJIs, potentially leading to safer and more efficient procedures. Additionally, the ability to accurately reconstruct the lumbar spine based on partial information can be beneficial in surgical planning and simulation. This thesis serves as a stepping stone for further research in the field, enabling future investigations into real-time guidance systems for LFJIs using radiation-free imaging modalities such as ultrasound. The insights gained from this study can be leveraged to address the challenges associated with lower-resolution and artifact-prone data, ultimately advancing the development of safer and more efficient LFJIs procedures