Deep Learning for Post-Contrast T1-Weighted Brain MRI Synthesis

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases...

Predicting the stability of distal radius fractures with a small dataset and machine learning

Purpose. Distal radius fractures are common fractures of the wrist. These fractures are often displaced and need reduction, after adequate reduction, the patients will have follow-up X-rays to check if the fracture stays stable. This is important because surgery might be required if the fracture becomes unstable. This can lead to delayed surgery...

Low Field Magnetic Resonance Imaging of the Eye: Inexpensive MRI for Ocular Conditions

Ultrasound imaging is an important modality in ocular oncology, allowing for fast examination of the eye by the ophthalmologist themselves. It is clinically used to measure tumour sizes for treatment planning. However, ocular ultrasound is limited to two-dimensional imaging, and suffers from poor contrast between tumour and sclera, which...

Towards Diffusion Weighted MR for Evaluation of Rectal Tumours on an MR-Linac

Purpose: Pre- and post-treatment MRI based Diffusion Weighted Imaging (DWI) has shown to be an effective predictor and indicator of treatment response in cancer. However, clinical applications of the Apparent Diffusion Coefficient (ADC) requires sufficient precision; this can be assessed with repeatability studies. Therefore, the purpose of this...

Application and Development of Quantitative Magnetic Resonance Imaging: High-Resolution QMRI in an Elderly Cohort and End-to-End Quantitative Susceptibility Mapping with Deep-Learning

We present application and developments of qMRI. We reconstructed data from the Prediva Ouderen Extentie (POE) database and calculated quantitative maps. The POE database consists of 29 7 Tesla (T) whole-brain MRI scans, including both male and female participants covering the age group between 80 and 91 years old. We used this data to extend...

Quantitative characteristics of brain lesions: a data visualization tool

One of the principal signs of disease progression in brain tumor patients is an increase in tumor size between time-separated medical image acquisitions. The current diagnosis of tumor progression is based on visual appraisal or manual measurement of largest diameters, neither of which is a fully quantitative measure. The RANO criteria dictate...

Dynamic B0 Inhomogeneity Correction at Low Field (50mT) using Interleaved B0 Mapping: Applied to Turbo Spin Echo and Magnetic Resonance Fingerprinting

Low-field MRI is susceptible to temperature changes in magnet temperature, resulting in time-dependent inhomogeneities. This causes time-dependent phase shifts in the measured signal, leading to image distortions. In this project, a method is proposed to interleave short magnetic field (B0) mapping sequences in the target image acquisition for...

Automated functional outcome prediction in stroke using combined imaging and clinical parameters

Predicting functional outcome after intra-arterial treatment (IAT) in acute ischemic stroke (AIS) patients is an important aspect of treatment decision making and prognostics. Standard methods for functional outcome prediction after stroke combine baseline clinical (and radiological) parameters. In this study, we investigated to what extent...

Conceptual probabilistic treatment planning approaches to deal with microscopic disease as an alternative to the Clinical Target Volume

Radiotherapy is one of the main treatment modalities available to treat cancer. Radiotherapy treatment plans are created based on CT scans of the patient. In such scans the macroscopic tumor is visible, but microscopic disease present in the surrounding tissue cannot be observed. To achieve an optimal clinical outcome, both the macroscopic and...

Recovering Quantitative Maps from Conventional Weighted MR Images using Deep Learning

Long acquisition times impede the routine clinical use of quantitative magnetic resonance imaging (qMRI). qMRI quantifies meaningful tissue parameters in T1-, T2-, and PD-maps, as opposed to conventional (qualitative) weighted MRI (wMRI), which only visualises contrast between tissues. Although methods exist that generate synthetic wMRI from...

Assessment of Cerebrovascular Reactivity inPatients with Brain Metastases

Introduction: Radiation is an effective treatment to increase overall mean survival of patients with metastatic brain tumours, however, damage to healthy tissue is inevitable. Radiation can cause dysfunction of the cerebrovasculature which is hypothesised to induce cognitive decline in patients after radiotherapy (RT). A new method, the...

Ultrafast ultrasound for super­resolution imaging: The effect of pulse inversion and harmonic­ or SVD­filtering on microbubble detection

Background/purpose: The mapping of microvasculature requires a high­resolution imaging method. Super­resolution ultrasound­imaging can provide such high resolution by detecting individual microbub­ bles (MBs) in a flow circuit. This method relies on the dynamic appearance of MBs, which appear as point scatterers on the US image, mixed with...

The increased risk of post-treatment contrast-enhancing brain lesion in IMPT of glioma, and the mitigation thereof in treatment planning

Proton radiotherapy has a dosimetric advantage over photon therapy to spare healthy tissue closely positioned to the tumor mainly due to the absent exit dose. In The Netherlands, the Proton therapy centers currently take a relative biological effectiveness (RBE) of 1.1 compared to photons to deliver an iso-effective treatment. However, initial...

Analyzing single molecule emission patterns using Deep Learning

The time taken to generate a super-resolution image and the quality of the final synthetic image depends on the performance of the localization algorithm which is used in the localization microscopy pipeline. The most precise and accurate algorithms are mostly iterative and they take a long time to generate the localization list while the faster...

Analyzing gray matter differences in age-related hearing loss using multivariable linear regression and deep learning

Objective: Recent studies have suggested an association between age-related hearing loss and cognitive decline. Yet, the underlying mechanism explaining this relation remains unknown. In this regard, several studies investigated gray matter (GM) differences in age-related hearing loss but presented inconsistent results regarding the association...

Ex vivo Validation of PET Imaging by 3D-printed Phantoms

Machine learning for knowledge-based dose-volume histogram prediction in prostate cancer

Introduction:  Despite the vast amount of optimization algorithms, radiotherapy treatment planning remains a manual, time-consuming and iterative process. To increase plan standardization, we clinically use Pinnacle's autoplanner for several disease sites. However, this introduces new challenges: first, the autoplanner is not perfect and still...

Development of a 3D-printed phantom for proton therapy

Proton therapy is a relatively new technique in the field of radiation oncology. The advantage of using protons can be illustrated by the depth-dose relation, which results in a more concentrated dose at a specific depth and thus potentially less dose in the surrounding healthy tissue compared to conventional photon therapy. However, this depth...