SciCombinator

Discover the most talked about and latest scientific content & concepts.

Journal: Zeitschrift fur medizinische Physik

28

The earliest studies on ‘disability glare’ date from the early 20(th) century. The condition was defined as the negative effect on visual function of a bright light located at some distance in the visual field. It was found that for larger angles (>1degree) the functional effect corresponded precisely to the effect of a light with a luminosity equal to that of the light that is perceived spreading around such a bright source. This perceived spreading of light was called straylight and by international standard disability glare was defined as identical to straylight. The phenomenon was recognized in the ophthalmological community as an important aspect of the quality of vision and attempts were made to design instruments to measure it. This must not be confused with instruments that assess light spreading over small distances (<1 degree), as originating from (higher order) aberrations and defocus. In recent years a new instrument has gained acceptance (C-Quant) for objective and controllable assessment of straylight in the clinical setting. This overview provides a sketch of the historical development of straylight measurement, as well as the results of studies on the origins of straylight (or disability glare) in the normal eye, and on findings on cataract (surgery) and corneal conditions.

Concepts: Quantum mechanics, Evaluation, Eye, Vision, Visual perception, Visual system, Ophthalmology, Normal distribution

22

For dosimetry in radioligand therapy, the time-integrated activity coefficients (TIACs) for organs at risk and for tumour lesions have to be determined. The used sampling scheme affects the TIACs and therefore the calculated absorbed doses. The aim of this work was to develop a general and flexible method, which analyses numerous clinically applicable sampling schedules using true time-activity curves (TACs) of virtual patients.

22

Convolutional neural networks have begun to surpass classical statistical- and atlas based machine learning techniques in medical image segmentation in recent years, proving to be superior in performance and speed. However, a major challenge that the community faces are mismatch between variability within training and evaluation datasets and therefore a dependency on proper data pre-processing. Intensity normalization is a widely applied technique for reducing the variance of the data for which there are several methods available ranging from uniformity transformation to histogram equalization. The current study analyses the influence of intensity normalization on cerebellum segmentation performance of a convolutional neural network (CNN).

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The increasing frequency and complexity of medical radiation exposures to humans inevitably result in higher risks of harmful unintended or accidental radiation exposures. To ensure a high level of protection and its continuous improvement, the Directive 2013/59/Euratom thus requires to systematically record and analyze both events and near-miss events as well as, in the case of their significance, to disseminate information regarding lessons learned from these events promptly and nationwide to improve radiation protection in medicine. These requirements have been transposed into German legislation by the new radiation protection law and radiation protection ordinance that entered into force simultaneously on December 31th, 2018. The reporting and information system as provided by these regulations as well as the tasks, duties and powers of the parties involved are presented in the first part of this review article. In the second part, the established application-specified criteria for the significance - and thus the notification requirement - of (near-miss) events are itemized and explicated.

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What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

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This addendum provides correction factors for the recombination and the polarity effect for the new ionization chamber PTW PinPoint (type 31023). The measurements were made in filtered (WFF) and unfiltered (FFF) high-energy photon beams. It could be confirmed that both the initial and the general recombination effect of the chamber mainly depends on dose per pulse at the point of measurement and is independent of the filtration of the photon beam.

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Glioblastoma multiforme is the most frequent innate brain tumor and still yields an unfavorable prognosis of 15 months of survival after diagnosis. Animal models play an important role in the investigation of therapeutic strategies of brain tumors. Thereby, MRI is substantial to individual follow-up examination for therapeutic response. Contrast agent dosage at 1.5 and 3T MRI has been thoroughly tested, while there is hardly any data for 9.4T. Therefore, the aim of this study was to find the optimal contrast agent dosage at 9.4T for examination of T1 relaxation time and apparent tumor volume in an animal model.

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Inter-fractional variations of breathing pattern and patient anatomy introduce dose uncertainties in proton therapy. One approach to monitor these variations is to utilize the cone-beam computed tomography (CT, CBCT) scans routinely taken for patient positioning, reconstruct them as 4DCBCTs, and generate ‘virtual CTs’ (vCTs), combining the accurate CT numbers of the diagnostic 4DCT and the geometry of the daily 4DCBCT by using deformable image registration (DIR). In this study different algorithms for 4DCBCT reconstruction and DIR were evaluated. For this purpose, CBCT scans of a moving ex vivo porcine lung phantom with 663 and 2350 projections respectively were acquired, accompanied by an additional 4DCT as reference. The CBCT projections were sorted in 10 phase bins with the Amsterdam-shroud method and reconstructed phase-by-phase using first a FDK reconstruction from the Reconstruction Toolkit (RTK) and again an iterative reconstruction algorithm implemented in the Gadgetron Toolkit. The resulting 4DCBCTs were corrected by DIR of the corresponding 4DCT phases, using both a morphons algorithm from REGGUI and a b-spline deformation from Plastimatch. The resulting 4DvCTs were compared to the 4DCT by visual inspection and by calculating water equivalent thickness (WET) maps from the phantom’s surface to the distal edge of a target from various angles. The optimized procedure was successfully repeated with mismatched input phases and on a clinical patient dataset. Proton treatment plans were simulated on the 4DvCTs and the dose distributions compared to the reference based on the 4DCT via gamma pass rate analysis. A combination of iterative reconstruction and morphons DIR yielded the most accurate 4DvCTs, with median WET differences under 2mm and 3%/3mm gamma pass rates per phase between 89% and 99%. These results suggest that image correction of iteratively reconstructed 4DCBCTs with a morphons DIR of the planning CT may yield sufficiently accurate 4DvCTs for daily time resolved proton dose calculations.

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The current work investigates the performance of different multivariate supervised machine learning models to predict the presence or absence of multiple sclerosis (MS) based on features derived from quantitative MRI acquisitions. The performance of these models was evaluated for images which are significantly degraded due to subject motion, a problem which is often observed in clinical routine diagnostics. Finally, the difference between a true multivariate analysis and the corresponding univariate analysis based on single parameters alone was addressed.

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Proton radiotherapy (PT) requires accurate target alignment before each treatment fraction, ideally utilizing 3D in-room X-ray computed tomography (CT) imaging. Typically, the optimal patient position is determined based on anatomical landmarks or implanted markers. In the presence of non-rigid anatomical changes, however, the planning scenario cannot be exactly reproduced and positioning should rather aim at finding the optimal position in terms of the actually applied dose. In this work, dose-guided patient alignment, implemented as multicriterial optimization (MCO) problem, was investigated in the scope of intensity-modulated and double-scattered PT (IMPT and DSPT) for the first time. A method for automatically determining the optimal patient position with respect to pre-defined clinical goals was implemented. Linear dose interpolation was used to access a continuous space of potential patient shifts. Fourteen head and neck (H&N) and eight prostate cancer patients with up to five repeated CTs were included. Dose interpolation accuracy was evaluated and the potential dosimetric advantages of dose-guided over bony-anatomy-based patient alignment investigated by comparison of clinically relevant target and organ-at-risk (OAR) dose-volume histogram (DVH) parameters. Dose interpolation was found sufficiently accurate with average pass-rates of 90% and 99% for an exemplary H&N and prostate patient, respectively, using a 2% dose-difference criterion. Compared to bony-anatomy-based alignment, the main impact of automated MCO-based dose-guided positioning was a reduced dose to the serial OARs (spinal cord and brain stem) for the H&N cohort. For the prostate cohort, under-dosage of the target structures could be efficiently diminished. Limitations of dose-guided positioning were mainly found in reducing target over-dosage due to weight loss for H&N patients, which might require adaptation of the treatment plan. Since labor-intense online quality-assurance is not required for dose-guided patient positioning, it might, nevertheless, be considered an interesting alternative to full online re-planning for initially mitigating the effects of anatomical changes.