To treat lung tumours with particle therapy, different additional tasks and challenges in treatment planning and application have to be addressed thoroughly. One of these tasks is the quantification and consideration of the Bragg peak degradation due to lung tissue As lung is an heterogeneous tissue, the Bragg peak is broadened when particles traverse the microscopic alveoli. These are not fully resolved in clinical CT images and thus, the effect is not considered in the dose calculation. In this work, a correlation between the CT histograms of heterogeneous material and the impact on the Bragg peak curve is presented. Different inorganic materials were scanned with a conventional CT scanner and additionally, the Bragg peak degradation was measured in a proton beam and was then quantified. A model is proposed that allows an estimation of the modulation power by performing a histogram analysis on the CT scan. To validate the model for organic samples, a second measurement series was performed with frozen porcine lunge samples. This allows to investigate the possible limits of the proposed model in a set-up closer to clinical conditions. For lung substitutes, the agreement between model and measurement is within ±0.05 mm and for the organic lung samples, within ±0.15 mm. This work presents a novel, simple and efficient method to estimate if and how much a material or a distinct region (within the lung) is degrading the Bragg peak on the basis of a common clinical CT image. Up until now, only a direct in-beam measurement of the region or material of interest could answer this question.Purpose.To develop a method that enables computed tomography (CT) to magnetic resonance (MR) image registration of complex deformations typically encountered in rotating joints such as the knee joint.Methods.We propose a workflow, denoted quaternion interpolated registration (QIR), consisting of three steps, which makes use of prior knowledge of tissue properties to initialise deformable registration. In the first step, the rigid skeletal components were individually registered. Next, the deformation of soft tissue was estimated using a dual quaternion-based interpolation method. In the final step, the registration was fine-tuned with a rigidity-constrained deformable registration step. The method was applied to paired, unregistered CT and MR images of the knee of 92 patients. It was compared to registration using B-Splines (BS) and B-Splines with a rigidity penalty (BSRP). Registration accuracy was evaluated using mutual information, and by calculating Dice similarity coefficient (DSC), mean absolute surfaceration approach.Quasi-static ultrasound elastography is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In ultrasound elastography, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional Long-Short-Term Memory (convLSTM) decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open access in vivo database for quasi-static ultrasound elastography, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisation-based methods, in terms of elastographic signal-to-noise ratio (SNRe), strain consistency, and image similarity. Finally, our open source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU.Brachytherapy is a common treatment in cervical, uterine and vaginal cancer management. The technique is characterised by rapid developments in the fields of medical imaging, dosimetry planning and personalised medical device design. To reduce unnecessary burden on patients, assessments and training of these technologies should preferable be done using high-fidelity physical phantoms. In this study, anthropomorphic deformable phantoms of the vaginal wall and cavity were developed for image-guided adaptive brachytherapy, in which vaginal wall biomechanics were mimicked. Phantoms were produced from both silicone and polyvinyl alcohol materials. Material characterisations were performed with uniaxial tensile tests, via which Young's moduli and toughness were quantified. In addition, the contrast between adjacent phantom layers was quantified in magnetic resonance images. The results showed that stress-strain curves of the silicone phantoms were within the range of those found in healthy human vaginal wall tissues. Sample preconditioning had a large effect on Young's moduli, which ranged between 2.13 and 6.94 MPa in silicone. https://www.selleckchem.com/products/Roscovitine.html Toughness was a more robust and accurate metric for biomechanical matching, and ranged between 0.23 and 0.28 ·106J·m-3as a result of preconditioning. The polyvinyl alcohol phantoms were not stiff or tough enough, with a Young's modulus of 0.16 MPa and toughness of 0.02 ·106J·m-3. All materials used could be clearly delineated in magnetic resonance images, although the MRI sequence did affect layer contrast. In conclusion, we developed anthropomorphic deformable phantoms that mimic vaginal wall tissue and are well visible in magnetic resonance images. These phantoms will be used to evaluate the properties and to optimise the development and use of personalised brachytherapy applicators.