[en] Attenuation correction (AC) in brain PET/MR has recently emerged as one of the challenging tasks in the PET/MR field. It has been shown that to ignore the attenuation produced by bone can lead to errors ranging from 5-30% in regions close to bone structures. Since the information provided by the MR signal is not directly related to tissue attenuation, alternative methods have to be developed. Signal from bone tissue is difficult to measure given its short transverse relaxation time (T2). Ultrashort-echo time (UTE) pulse sequences were developed to measure signal from tissues with short T2. A combination of two consecutive UTE echoes has been used in several works to measure signal from bone tissue. The first echo is able to measure signal from bone tissue in addition to soft tissue, while the second echo contains most of the soft tissue contained in the first echo but not bone. In this work we extract the attenuation information from the difference between the logarithm of two images obtained after applying two consecutive UTE pulse sequences using the mMR scanner (Siemens Healthcare). Subsequently, image processing techniques are applied to reduce the noise and extract air cavities within the head. The resulting image is converted to linear attenuation coefficients, generating what is known as µ-map, to be used during reconstruction. For comparison purposes PET/CT scans of the same patients were acquired prior to the PET/MR scan. Additional µ-maps obtained for comparison were extracted from a Dixon sequence (used in clinical routine) and an additional µ-map calculated by the scanner based on UTE pulse sequences. Preliminary quantitative results measured in the cerebellum, using the value obtained with CT-based AC as reference, show differences of 34% without AC, 13% using the Dixon-based and UTE-based provided by the scanner, and 0.8% with the AC strategy presented here.

[en] The combination of clinical MRI and PET systems has received increased attention in recent years. In contrast to currently used PET/CT systems, PET/MRI offers not only improved soft-tissue contrast and reduced levels of ionizing radiation, but also a wealth of MRI-specific information such as functional, spectroscopic and diffusion tensor imaging. Combining PET and MRI, however, has proven to be very challenging, due to the detrimental cross-talk effects between the two systems. Significant progress has been made in the recent years to overcome these difficulties, with several groups reporting PET/MRI prototypes for animal imaging and a clinical insert for neurological applications being demonstrated at the 2007 Annual Meeting of the Society of Nuclear Medicine. In this paper we review different architectures for clinical PET/MRI systems, and their possibilities, limitations and technological obstacles. (orig.)

[en] Positron emission tomography (PET) is a nuclear medical imaging technique for quantitative measurement of physiologic parameters in vivo (an overview of principles and applications can be found in [P.E. Valk, et al., eds. Positron Emission Tomography. Basic Science and Clinical Practice. 2003, Springer: Heidelberg]), based on the detection of small amounts of positron-emitter-labelled biologic molecules. Various radiotracers are available for neurological, cardiological, and oncological applications in the clinic and in research protocols. This overview describes the basic principles, technology, and recent developments in PET, followed by a section on the development of a tomograph with avalanche photodiodes dedicated for small animal imaging as an example of efforts in the domain of high resolution tomographs

[en] MADPET-II is a high resolution positron emission tomograph aimed at radiopharmaceutical studies in small animals. The detector architecture is based on the individual readout of minute LSO scintillation crystals by Avalanche Photodiodes (APDs). A dual radial detector layer assures uniformity of the spatial resolution along the radial Field of View while maintaining the system's sensitivity. In total, the system consists of 1152 independent electronic channels. The tomograph performs list mode data acquisition, recording the energy and time stamp for every detected singles event. Coincident sorting and system calibration is done post acquisition in software. The measured mean energy resolution of the system is 22% and the system-wide time resolution is 9 nsec. The individual detector readout assures a maximum count rate of 10000 cps per channel. First phantom studies exhibit a spatial resolution of 1.25 mm along a central slice of the scanner. For the image reconstruction a 3D MLEM algorithm based on a Monte Carlo System Matrix is used. Results from performance evaluation measurements and the first animal studies are presented

[en] Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the detector. An incident positron may fire a number of successive pixels on the imaging plane. It is still impossible to capture the primary fired pixel along a particle trajectory by hardware or to perceive the pixel firing sequence by direct observation. Here, we propose a novel data-driven method to improve the spatial resolution by classifying the primary pixels within the detector using support vector machine. A classification model is constructed by learning the features of positron trajectories based on Monte-Carlo simulations using Geant4. Topological and energy features of pixels fired by "1"8F positrons were considered for the training and classification. After applying the classification model on measurements, the primary fired pixels of the positron tracks in the silicon detector were estimated. The method was tested and assessed for ["1"8F]FDG imaging of an absorbing edge protocol and a leaf sample. The proposed method improved the spatial resolution from 154.6   ±   4.2 µm (energy weighted centroid approximation) to 132.3   ±   3.5 µm in the absorbing edge measurements. For the positron imaging of a leaf sample, the proposed method achieved lower root mean square error relative to phosphor plate imaging, and higher similarity with the reference optical image. The improvements of the preliminary results support further investigation of the proposed algorithm for the enhancement of positron imaging in clinical and preclinical applications. (paper)

No abstract available

[en] This review will focus on the clinical potential of PET/CT for the characterization of cardiovascular diseases. We describe the technical challenges of combining instrumentation with very different imaging performance and discuss the clinical applications in the field of cardiology.

[en] Respiratory motion of lung lesions is a limiting factor of quantification of positron emission tomography (PET) data. As some important applications of PET such as therapy monitoring and radiation therapy treatment planning require precise quantification, it is necessary to correct PET data for motion artefacts. The method is based on list-mode data. First, the motion of the lesion was detected by a centre of mass approach. In the second step, data were sorted corresponding to the breathing state. A volume of interest (VOI) around the lesion was defined manually, and the motion of the lesion in this VOI was measured with reference to the end-expiration image. Then, all voxels in the VOI were shifted according to the measured lesion motion. After optimisation of parameters and verification of the method using a computer-controlled motion phantom, it was applied to nine patients with solitary lesions of the lung. Fifty percent difference in measured lesion volume and 26% in mean activity concentration were found comparing PET data before and after applying the correction algorithm when simulating a motion amplitude of 28 mm in phantom studies. For patients, maximum changes of 27% in volume and 13% in mean standardised uptake values (SUV) were found. As respiratory motion is affecting quantification of PET images, correction algorithms are essential for applications that require precise quantification. We described a method which improves the quantification of moving lesions by a local motion correction using list-mode data without increasing acquisition time or reduced signal-to-noise ratio of the images. (orig.)

No abstract available

[en] For the education of students a lab course on positron emission tomography (PET) is currently build. Students get a view into the physics of PET, detector concepts, electronics, signal processing, software, and reconstruction algorithms resulting in a reconstructed image of Na-22 sources with different shapes. The lab aims on students of physics and medicine. We use a former small animal detector prototype with LSO (lutetium-oxyorthosylicate) as scintillator and a single channel readout with 96 APDs (avalanche photodiodes). Data aquisition and signal processing is done by a sampling ADC system; data processing and image reconstruction via ROOT. We present the status of the project.