Wat we zien, wat we niet zien, en wat we daarmee doen

Oratie uitgesproken door Prof.dr. U.A. van der Heide bij de aanvaarding van het ambt van bijzonder hoogleraar in de Radiotherapie, in het bijzonder Beeldvormende Technieken in de Radiologie aan de Universiteit Leiden vanwege het Nederlands Kanker InstituutOratie uitgesproken door Prof.dr. U.A. van der Heide bij de aanvaarding van het ambt van bijzonder hoogleraar in de Radiotherapie, in het bijzonder Beeldvormende Technieken in de Radiologie aan de Universiteit Leiden vanwege het Nederlands Kanker Instituut op vrijdag 10 juni 201

Longitudinal correlations between intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI during radiotherapy in prostate cancer patients

Purpose: Intravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment.Materials and Methods: 20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T2-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D* and the product fD* were calculated. The Tofts model was used to calculate the DCE parameters Ktrans, kep and ve. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI.Results: When averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD*, significant longitudinal correlations with all DCE parameters were found.Conclusions: The increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.</p

Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer

Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in the context of adaptive image-guided radiotherapy for prostate cancer, where planning and follow-up CT images as well as their corresponding contours are available for training. At testing time the contours of the follow-up scans are not available, which is a common scenario in adaptive radiotherapy. The study involves two datasets from different manufacturers and institutes. The first dataset was divided into training (12 patients) and validation (6 patients), and was used to optimize and validate the methodology, while the second dataset (14 patients) was used as an independent test set. We carried out an extensive quantitative comparison between the quality of the automatically generated contours from different network architectures as well as loss weighting methods. Moreover, we evaluated the quality of the generated deformation vector field (DVF). We show that MTL algorithms outperform their Single-Task Learning (STL) counterparts and achieve better generalization on the independent test set. The best algorithm achieved a mean surface distance of 1.06 +/- 0.3 mm, 1.27 +/- 0.4 mm, 0.91 +/- 0.4 mm, and 1.76 +/- 0.8 mm on the validation set for the prostate, seminal vesicles, bladder, and rectum, respectively. The high accuracy of the proposed method combined with the fast inference speed, makes it a promising method for automatic re-contouring of follow-up scans for adaptive radiotherapy, potentially reducing treatment related complications and therefore improving patients quality-of-life after treatment. The source code is available at https://github.com/moelmahdy/JRS-MTL.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Feasibility of Gold Fiducial Markers as a Surrogate for Gross Tumor Volume Position in Image-Guided Radiation Therapy of Rectal Cancer

Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

Daily Intravoxel Incoherent Motion (IVIM) in prostate cancer patients during MR-guided radiotherapy: a multicenter study

Biological, physical and clinical aspects of cancer treatment with ionising radiatio