Articles

A Novel DOI Positioning Algorithm for Monolithic Scintillator Crystals in PET Based on Gradient Tree Boosting

Florian Müller , David Schug , Patrick Hallen, Jan Grahe, and Volkmar Schulz

PET Image Denoising Using a Deep Neural Network Through Fine Tuning

Kuang Gong , Jiahui Guan, Chih-Chieh Liu, and Jinyi Qi

Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Daniel Deidda, N. A. Karakatsanis, Philip M. Robson, Nikos Efthimiou, Zahi A. Fayad, Robert G. Aykroyd and Charalampos Tsoumpas

Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR

Kathleen M. Ropella-Panagis, Nicole Seiberlich and Vikas Gulani

Featured Articles

Machine (Deep) Learning Methods for Image Processing and Radiomics

Mathieu Hatt, Chintan Parmar, Jinyi Qi and Issam El Naqa

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A Novel DOI Positioning Algorithm for Monolithic Scintillator Crystals in PET Based on Gradient Tree Boosting

Florian Müller , David Schug , Patrick Hallen, Jan Grahe, and Volkmar Schulz

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Validation and Performance Assessment of a Preclinical SiPM-Based SPECT/MRI Insert

M. Carminati, F. M. Baratelli, M. Occhipinti, K. Erlandsson, K. Nagy, Z. Nyitrai, M. Czeller, A. Kühne, T. Niendorf, S. Valtorta, S. Belloli, R. M. Moresco, A. Savi, A. Iadanza, A. Falini, L. S. Politi, M. Cadioli, B. F. Hutton and C. Fiorini

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Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Daniel Deidda, N. A. Karakatsanis, Philip M. Robson, Nikos Efthimiou, Zahi A. Fayad, Robert G. Aykroyd and Charalampos Tsoumpas

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Review Articles

Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering—A Topical Review

Wolfgang Kainz, Esra Neufeld, Wesley E. Bolch, Christian G. Graff, Chan Hyeong Kim, Niels Kuster, Bryn Lloyd, Tina Morrison, Paul Segars, Yeon Soo Yeom, Maria Zankl, X. George Xu and Benjamin M. W. Tsui

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3-D Image-Based Dosimetry in Radionuclide Therapy

M. Ljungberg and K. Sjögreen Gleisner

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Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR

Kathleen M. Ropella-Panagis, Nicole Seiberlich and Vikas Gulani

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Organ-Dedicated Molecular Imaging Systems

Antonio J. González, Filomeno Sánchez, José M. Benlloch

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Pushing the Limits in Time-of-Flight PET Imaging

P. Lecoq

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Performance Study of a Large Monolithic LYSO PET Detector With Accurate Photon DOI Using Retroreflector Layers

Andrea González-Montoro, Albert Aguilar, Gabriel Cañizares, Pablo Conde, Liczandro Hernández, Luis F. Vidal, Matteo Galasso, Andrea Fabbri, Filomeno Sánchez, José M. Benlloch, and Antonio J. González

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Low Power and Small Area, 6.9 ps RMS Time-to-Digital Converter for 3-D Digital SiPM

Nicolas Roy, Frédéric Nolet, Frédérik Dubois, Marc-Olivier Mercier, Réjean Fontaine and Jean-François Pratte

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Mechanisms of Plasma Medicine: Coupling Plasma Physics, Biochemistry, and Biology

David B. Graves

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Abstract:
Methods from the field of machine (deep) learning have been successful in tackling a number of tasks in medical imaging, from image reconstruction or processing to predictive modeling, clinical planning and decision-aid systems. The ever growing availability of data and the improving ability of algorithms to learn from them has led to the rise of methods based on neural networks to address most of these tasks with higher efficiency and often superior performance than previous, “shallow” machine learning methods. The present editorial aims at contextualizing within this framework the recent developments of these techniques, including these described in the papers published in the present special issue on machine (deep) learning for image processing and radiomics in radiation-based medical sciences.

Abstract:
Monolithic crystals are examined as an alternative to segmented scintillator arrays in positron emission tomography (PET). Monoliths provide good energy, timing, and spatial resolution including intrinsic depth of interaction (DOI) encoding. DOI allows reducing parallax errors (radial astigmatism) at off-center positions within a PET ring. We present a novel DOI-estimation approach based on the supervised machine learning algorithm gradient tree boosting (GTB). GTB builds predictive regression models based on sequential binary comparisons (decision trees). GTB models have been shown to be implementable in FPGA if the memory requirement fits the available resources. We propose two optimization scenarios for the best possible positioning performance: One restricting the available memory to enable a future FPGA implementation and one without any restrictions. The positioning performance of the GTB models is compared with a DOI estimation method based on a single DOI observable (SO) comparable to other methods presented in literature. For a 12 mm high monolith, we achieve an averaged spatial resolution of 2.15 mm and 2.12 mm FWHM for SO and GTB models, respectively. In contrast to SO models, GTB models show a nearly uniform positioning performance over the whole crystal depth.

Abstract:
A preclinical insert for small animal simultaneous SPECT and MR imaging, in particular for imaging mouse brains, is presented. It consists of ten static magnetic resonance imaging (MRI)-compatible gamma cameras based on tiles of silicon photomultipliers readout by a multichannel ASIC and coupled to 5 cm × 5 cm CsI(Tl) scintillators and to an MRI-compatible multipinhole collimator. Calibration and image reconstruction algorithm are illustrated. Mutual compatibility is demonstrated along with imaging performance that is comparable with other non-MR micro-SPECT systems: 0.9 mm tomographic spatial resolution across a transverse field of view of 15.6 mm, 12% energy resolution (at 140 keV), and 1105 cps/MBq sensitivity. Experimental results with phantoms (glass capillaries of 290 μm diameter and a mini Derenzo) are presented.

Abstract:
Anatomically driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This paper examines the effects of spatial inconsistency between MR and PET images in hot and cold regions of PET images using the hybrid kernelized expectation maximization (HKEM) machine learning method. Our evaluation was conducted on Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the “spill-in” for cold regions and the “spill-out” for hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using kernelized expectation maximization, against the 37% and 8% obtained with HKEM. The findings of this paper suggest that including PET information in the kernel enhances the robustness of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.

Abstract:
Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semiautomated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. This paper is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.

Abstract:
Radionuclide therapy is the use of radioactive drugs for internal radiotherapy, mainly for the treatment of metastatic disease. As opposed to systemic cancer therapies in general, the use of radioactively labeled drugs results not only in a targeted therapy but also the possibility of imaging the distribution of the drug during therapy. From such images, the absorbed doses delivered to tumors and organs at risk can be calculated. Calculation of the absorbed dose from 3-D images such as single-photon emission computed tomography (SPECT)/CT, and in some cases positron emission tomography (PET)/CT, relies on image-based activity quantification. Quantification is accomplished by modeling the physics involved in the image-formation process, and applying image-processing methods. From a time-sequence of such quantitative images, the absorbed doses are then calculated. Although individual-patient dosimetry is a standard component of other forms of radiotherapy, it is still overlooked in the majority of radionuclide therapies. In this review, we summarize the physical and technical problems that need to be addressed in image-based dosimetry. The focus is on SPECT, since most of the radionuclides used are single-photon emitters, although the use of PET is also discussed. Practical issues of relevance for the practical implementation of personalized dosimetry in radionuclide therapy are also highlighted

Abstract:
Magnetic resonance imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for quantitative and functional evaluation, MRI is typically used qualitatively, in which the underlying tissue properties are not measured and, thus, the brightness of each pixel is not quantitatively meaningful. Positron emission tomography (PET) is an inherently quantitative imaging modality that interrogates functional activity within a tissue, probed by a molecule of interest coupled with an appropriate tracer. These modalities can complement one another to provide clinical information regarding both structure and function, but there are still technical and practical hurdles in the way of the integrated use of both modalities. Recent advances in MRI have moved the field in an increasingly quantitative direction, which is complementary to PET, and could also potentially help solve some of the challenges in PET/MR. Magnetic resonance fingerprinting (MRF) is a recently described MRI-based technique which can efficiently and simultaneously quantitatively map several tissue properties in a single exam. Here, the basic principles behind the quantitative approach of MRF are laid out, and the potential implications for combined PET/MR are discussed.

Abstract:
In this review, we will cover both clinical and technical aspects of the advantages and disadvantages of organ specific (dedicated) molecular imaging (MI) systems, namely positron emission tomography (PET) and single photon emission computed tomography, including gamma cameras. This review will start with the introduction to the organ-dedicated MI systems. Thereafter, we will describe the differences and their advantages/disadvantages when compared with the standard large size scanners. We will review time evolution of dedicated systems, from first attempts to current scanners, and the ones that ended in clinical use. We will review later the state of the art of these systems for different organs, namely: breast, brain, heart, and prostate. We will also present the advantages offered by these systems as a function of the special application or field, such as in surgery, therapy assistance and assessment, etc. Their technological evolution will be introduced for each organ-based imager. Some of the advantages of dedicated devices are: higher sensitivity by placing the detectors closer to the organ, improved spatial resolution, better image contrast recovery (by reducing the noise from other organs), and also lower cost. Designing a complete ring-shaped dedicated PET scanner is sometimes difficult and limited angle tomography systems are preferable as they have more flexibility in placing the detectors around the body/organ. Examples of these geometries will be presented for breast, prostate and heart imaging. Recently achievable excellent time of flight capabilities below 300-ps full width at half of the maximum reduce significantly the impact of missing angles on the reconstructed images.

Abstract:
There is an increasing demand for high sensitivity multiparametric medical imaging approaches. High precision time-of-flight positron emission tomography (TOFPET) scanners have a very high potential in this context, providing an improvement in the signal-to-noise ratio of the reconstructed image and the possibility to further increase the already very high sensitivity (at the pico-molar level) of PET scanners. If the present state-of-the art coincidence time resolution of about 500 ps can be improved, it will open the way in particular to a significant reduction of the dose injected to the patient, and consequently, to the possibility to extend the use of PET scans to new categories of patients. This paper will describe the systematic approach followed by a number of researchers worldwide to push the limits of TOFPET imaging to the sub-100 ps level. It will be shown that the possibility to reach 10 ps, although extremely challenging, is not limited by physical barriers and that a number of disruptive technologies are presently being investigated at the level of all the components of the detection chain to gain at least a factor of 10 as compared to the present state-of-the-art.

Abstract:
Clinical and organ-dedicated PET systems typically require a high efficiency imposing the use of thick scintillators, normally through crystal arrays. To provide depth of interaction (DOI) information, two or more layers are sometimes mounted in the staggered or phoswich approach. In this paper, we are proposing an alternative using thick and large monolithic crystals. We have tested two surface treatments for a 50 mm × 50 mm × 20 mm LYSO block. We provide data in this paper as close as 5 mm to the lateral walls. We left those walls black painted and the exit face coupled to the photosensor (12 × 12 SiPM array) polished. The entrance face was: 1) black painted or 2) coupled to a retroreflector (RR) layer. These configurations keep a good DOI linearity and, on average, reached 4 mm DOI resolution, measured as the full width at half of the maximum. Approaches using RR layers return constant and good energy resolutions nearing 12%, compared to a range of 15%-16% in the case of totally black painted blocks. The best result concerning the detector spatial resolution was obtained when one of the smallest RR was used (120 um corner cube size), being 1.7 mm at the entrance crystal layer and 0.7 mm in the layer closest to the photosensor. These values worsen at least 30% for the black treatment case.

Abstract:
Time-of-flight measurements are becoming essential to the advancement of several fields, such as preclinical positron emission tomography and high energy physics. Recent developments in single photon avalanche diode (SPAD)-based detectors have spawned a great interest in digital silicon photomultipliers (dSiPMs). To overcome the tradeoff between the photosensitive area and the processing capabilities in current 2-D dSiPM, we propose a novel 3-D digital SiPM, where the SPAD, designed for maximal photosensitive area, will be stacked in 3-D over the electronic circuits, designed in a CMOS node technology. All readout circuits will be implemented directly under the SPAD real estate, including quenching circuit, time-to-digital converter (TDC) and digital readout electronics. This paper focusses on the TDC element of this system, designed in TSMC CMOS 65 nm. This ring oscillator-based Vernier TDC requires only 25 × 50 μm 2 and 160 μW, and achieves 6.9 ps rms timing accuracy.

Abstract:
Low temperature plasma (LTP) has emerged in the last decade as a novel and promising therapy for wound and skin decontamination, promotion of wound healing, cancer remission, control of wound-resident multidrug resistant bacteria, and dental and cosmetic applications, among others. Progress has been rapid in developing clinically useful devices and many studies are underway worldwide. Mechanisms of plasma therapeutics are beginning to be understood but much remains to be explored. This review focuses on mechanisms coupling the physics and chemistry of LTPs to medically relevant biochemistry and biology.