Zeitschrift Fur Medizinische Physik 

About: Zeitschrift Fur Medizinische Physik is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Imaging phantom & Dosimetry. It has an ISSN identifier of 0939-3889. Over the lifetime, 1067 publications have been published receiving 10636 citations. The journal is also known as: Medizinische Physik (Jena) & Journal of medical physics.

An overview of deep learning in medical imaging focusing on MRI

Abstract: 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.

A gentle introduction to deep learning in medical image processing

Abstract: This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

Optical coherence tomography - development, principles, applications.

Abstract: This paper presents a review of the development of optical coherence tomography (OCT), its principles and important applications. Basic OCT systems are described and the physical foundations of OCT signal properties and signal recording systems are reviewed. Recent examples of OCT applications in ophthalmology, cardiology, gastroenterology and dermatology outline the relevance of this advanced imaging modality in the medical field.

The PSI gantry 2: a second generation proton scanning gantry

Abstract: PSI is still the only location in which proton therapy is applied using a dynamic beam scanning technique on a very compact gantry. Recently, this system is also being used for the application of intensity-modulated proton therapy (IMPT). This novel technical development and the success of the proton therapy project altogether have led PSI in Year 2000 to further expand the activities in this field by launching the project PROSCAN. The first step is the installation of a dedicated commercial superconducting cyclotron of a novel type. The second step is the development of a new gantry, Gantry 2. For Gantry 2 we have chosen an iso-centric compact gantry layout. The diameter of the gantry is limited to 7,5 m, less than in other gantry systems (∼10–12 m). The space in the treatment room is comfortably large, and the access on a fixed floor is possible any time around the patient table. Through the availability of a faster scanning system, it will be possible to treat the target volume repeatedly in the same session. For this purpose, the dynamic control of the beam intensity at the ion source and the dynamic variation of the beam energy will be used directly for the shaping of the dose.

A Triple Gaussian Pencil beam Model for Photon beam Treatment Planning

Abstract: The transverse profiles of pencil beams are often represented by Gaussian functions in order to speed up electron beam treatment planning algorithms, because convolutions of Gaussions with most beam fluence profiles can be performed analytically. We extend this approach to high-energy photon radiations. Monte-Carlo generated transverse profiles of photon pencil beams are adequately represented by a sum of three Gaussian functions, whose coefficients and parameters have been optimized using Fourier transform methods. The axial profile of the pencil beam is determined by the depth-dependent surface integral of the dose in the transverse plane. As a first application, the triple Gaussian pencil beam algorithm is used to demonstrate the field size dependence of the photon beam depth dose curve. Photon beams modified by wege filters or shielding blocks will be treated in a second communication.