Exploring TOF Capabilities of PET Detector Blocks Based on Large Monolithic Crystals and Analog SiPMs

[EN] Monolithic scintillators are more frequently used in PET instrumentation due to their advantages in terms of accurate position estimation of the impinging gamma rays both planar and depth of interaction, their increased efficiency, and expected timing capabilities. Such timing performance has been studied when those blocks are coupled to digital photosensors showing an excellent timing resolution. In this work we study the timing behaviour of detectors composed by monolithic crystals and analog SiPMs read out by an ASIC. The scintillation light spreads across the crystal towards the photosensors, resulting in a high number of SiPMs and ASIC channels fired. This has been studied in relation with the Coincidence Timing Resolution (CTR). We have used LYSO monolithic blocks with dimensions of 50 x 50 x 15 mm(3) coupled to SiPM arrays (8 x 8 elements with 6 x 6 mm(2) area) which compose detectors suitable for clinical applications. While a CTR as good as 186 ps FWHM was achieved for a pair of 3 x 3 x 5 mm(3) LYSO crystals, when using the monolithic block and the SiPM arrays, a raw CTR over 1 ns was observed. An optimal timestamp assignment was studied as well as compensation methods for the time-skew and time-walk errors. This work describes all steps followed to improve the CTR. Eventually, an average detector time resolution of 497 ps FWHM was measured for the whole thick monolithic block. This improves to 380 ps FWHM for a central volume of interest near the photosensors. The timing dependency with the photon depth of interaction and planar position are also included.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 695536). It has also been supported by the Spanish Ministerio de Economia, Industria y Competitividad under Grant TEC2016-79884-C2-1-R.Lamprou, E.; González Martínez, AJ.; Sánchez Martínez, F.; Benlloch Baviera, JM. (2020). Exploring TOF Capabilities of PET Detector Blocks Based on Large Monolithic Crystals and Analog SiPMs. Physica Medica. 70:10-18. https://doi.org/10.1016/j.ejmp.2019.12.004101870Surti, S. (2014). Update on Time-of-Flight PET Imaging. Journal of Nuclear Medicine, 56(1), 98-105. doi:10.2967/jnumed.114.145029Spanoudaki, V. C., & Levin, C. S. (2010). Photo-Detectors for Time of Flight Positron Emission Tomography (ToF-PET). Sensors, 10(11), 10484-10505. doi:10.3390/s101110484Szczesniak, T., Moszynski, M., Swiderski, L., Nassalski, A., Lavoute, P., & Kapusta, M. (2009). Fast Photomultipliers for TOF PET. IEEE Transactions on Nuclear Science, 56(1), 173-181. doi:10.1109/tns.2008.2008992Renker, D. (2007). New trends on photodetectors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 571(1-2), 1-6. doi:10.1016/j.nima.2006.10.016Kim, C. L., Wang, G.-C., & Dolinsky, S. (2009). Multi-Pixel Photon Counters for TOF PET Detector and Its Challenges. IEEE Transactions on Nuclear Science, 56(5), 2580-2585. doi:10.1109/tns.2009.2028075Moses, W. W. (2002). Current trends in scintillator detectors and materials. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 487(1-2), 123-128. doi:10.1016/s0168-9002(02)00955-5Gundacker, S., Auffray, E., Pauwels, K., & Lecoq, P. (2016). Measurement of intrinsic rise times for various L(Y)SO and LuAG scintillators with a general study of prompt photons to achieve 10 ps in TOF-PET. Physics in Medicine and Biology, 61(7), 2802-2837. doi:10.1088/0031-9155/61/7/2802Gundacker, S., Acerbi, F., Auffray, E., Ferri, A., Gola, A., Nemallapudi, M. V., … Lecoq, P. (2016). State of the art timing in TOF-PET detectors with LuAG, GAGG and L(Y)SO scintillators of various sizes coupled to FBK-SiPMs. Journal of Instrumentation, 11(08), P08008-P08008. doi:10.1088/1748-0221/11/08/p08008Surti, S., & Karp, J. S. (2016). Advances in time-of-flight PET. Physica Medica, 32(1), 12-22. doi:10.1016/j.ejmp.2015.12.007Gundacker, S., Knapitsch, A., Auffray, E., Jarron, P., Meyer, T., & Lecoq, P. (2014). Time resolution deterioration with increasing crystal length in a TOF-PET system. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 737, 92-100. doi:10.1016/j.nima.2013.11.025Marcinkowski, R., España, S., Van Holen, R., & Vandenberghe, S. (2014). Optimized light sharing for high-resolution TOF PET detector based on digital silicon photomultipliers. Physics in Medicine and Biology, 59(23), 7125-7139. doi:10.1088/0031-9155/59/23/7125González-Montoro, A., Sánchez, F., Martí, R., Hernández, L., Aguilar, A., Barberá, J., … González, A. J. (2018). Detector block performance based on a monolithic LYSO crystal using a novel signal multiplexing method. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 912, 372-377. doi:10.1016/j.nima.2017.10.098Xi, D., Xie, Q., Zhu, J., Lin, L., Niu, M., Xiao, P., … Kao, C.-M. (2012). Optimization of the SiPM Pixel Size for a Monolithic PET Detector. Physics Procedia, 37, 1497-1503. doi:10.1016/j.phpro.2012.04.101Gonzalez-Montoro A, Aguilar A, Canizares G, Conde P, Hernandez L, Vidal LF, et al. Performance Study of a Large Monolithic LYSO PET Detector With Accurate Photon DOI Using Retroreflector Layers. IEEE Trans Rad Plasma Med Sci. PP. 1-1. DOI: 10.1109/TRPMS.2017.2692819.Krishnamoorthy, S., Blankemeyer, E., Mollet, P., Surti, S., Van Holen, R., & Karp, J. S. (2018). Performance evaluation of the MOLECUBES β-CUBE—a high spatial resolution and high sensitivity small animal PET scanner utilizing monolithic LYSO scintillation detectors. Physics in Medicine & Biology, 63(15), 155013. doi:10.1088/1361-6560/aacec3González-Montoro, A., Sánchez, F., Bruyndonckx, P., Cañizares, G., Benlloch, J. M., & González, A. J. (2019). Novel method to measure the intrinsic spatial resolution in PET detectors based on monolithic crystals. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 920, 58-67. doi:10.1016/j.nima.2018.12.056Van Dam, H. T., Borghi, G., Seifert, S., & Schaart, D. R. (2013). Sub-200 ps CRT in monolithic scintillator PET detectors using digital SiPM arrays and maximum likelihood interaction time estimation. 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Time and frequency pump-probe multiplexing to enhance the signal response of Brillouin optical time-domain analyzers

© 2014 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibitedA technique to enhance the response and performance of Brillouin distributed fiber sensors is proposed and experimentally validated. The method consists in creating a multi-frequency pump pulse interacting with a matching multi-frequency continuous-wave probe. To avoid nonlinear cross-interaction between spectral lines, the method requires that the distinct pump pulse components and temporal traces reaching the photodetector are subject to wavelength-selective delaying. This way the total pump and probe powers launched into the fiber can be incrementally boosted beyond the thresholds imposed by nonlinear effects. As a consequence of the multiplied pump-probe Brillouin interactions occurring along the fiber, the sensor response can be enhanced in exact proportion to the number of spectral components. The method is experimentally validated in a 50 km-long distributed optical fiber sensor augmented to 3 pump-probe spectral pairs, demonstrating a signal-to-noise ratio enhancement of 4.8 dB.The authors would like to thank Mr. Javier Urricelqui from Universidad Publica de Navarra (Spain) for the valuable discussions and help in relation to the noise characteristics of BOTDA sensors. This work was performed in the framework and with the support of the COST Action TD1001 OFSeSa. M. A. Soto and L. Thevenaz acknowledge the support from the Swiss Commission for Technology and Innovation (Project 13122.1), and from the Swiss State Secretariat for Education, Research and Innovation (SERI) through the project COST C10.0093. UPVLC group acknowledges the support from the Spanish MICINN and the Valencia Government through the projects TEC2011-29120-C05-05 and ACOMP/2013/146, respectively. L. Zhang acknowledges the support from the China Scholarship Council during his stay at EPFL in Switzerland.Soto, MA.; Ricchiuti, AL.; Zhang, L.; Barrera Vilar, D.; Sales Maicas, S.; Thevenaz, L. (2014). Time and frequency pump-probe multiplexing to enhance the signal response of Brillouin optical time-domain analyzers. Optics Express. 22(23):28584-28595. https://doi.org/10.1364/OE.22.028584S28584285952223Horiguchi, T., Shimizu, K., Kurashima, T., Tateda, M., & Koyamada, Y. (1995). Development of a distributed sensing technique using Brillouin scattering. Journal of Lightwave Technology, 13(7), 1296-1302. doi:10.1109/50.400684Soto, M. A., & Thévenaz, L. (2013). Modeling and evaluating the performance of Brillouin distributed optical fiber sensors. Optics Express, 21(25), 31347. doi:10.1364/oe.21.031347Foaleng, S. M., & Thévenaz, L. (2011). Impact of Raman scattering and modulation instability on the performances of Brillouin sensors. 21st International Conference on Optical Fiber Sensors. doi:10.1117/12.885105Alem, M., Soto, M. A., & Thévenaz, L. (2014). Modelling the depletion length induced by modulation instability in distributed optical fibre sensors. 23rd International Conference on Optical Fibre Sensors. doi:10.1117/12.2058862Thévenaz, L., Mafang, S. F., & Lin, J. (2013). Effect of pulse depletion in a Brillouin optical time-domain analysis system. Optics Express, 21(12), 14017. doi:10.1364/oe.21.014017Minardo, A., Bernini, R., & Zeni, L. (2009). A Simple Technique for Reducing Pump Depletion in Long-Range Distributed Brillouin Fiber Sensors. IEEE Sensors Journal, 9(6), 633-634. doi:10.1109/jsen.2009.2019372Soto, M. A., Bolognini, G., Di Pasquale, F., & Thévenaz, L. (2010). Simplex-coded BOTDA fiber sensor with 1 m spatial resolution over a 50 km range. Optics Letters, 35(2), 259. doi:10.1364/ol.35.000259Soto, M. A., Bolognini, G., & Di Pasquale, F. (2010). Analysis of pulse modulation format in coded BOTDA sensors. Optics Express, 18(14), 14878. doi:10.1364/oe.18.014878Rodriguez-Barrios, F., Martin-Lopez, S., Carrasco-Sanz, A., Corredera, P., Ania-Castanon, J. D., Thevenaz, L., & Gonzalez-Herraez, M. (2010). Distributed Brillouin Fiber Sensor Assisted by First-Order Raman Amplification. Journal of Lightwave Technology, 28(15), 2162-2172. doi:10.1109/jlt.2010.2051141Martin-Lopez, S., Alcon-Camas, M., Rodriguez, F., Corredera, P., Ania-Castañon, J. D., Thévenaz, L., & Gonzalez-Herraez, M. (2010). Brillouin optical time-domain analysis assisted by second-order Raman amplification. Optics Express, 18(18), 18769. doi:10.1364/oe.18.018769Soto, M. A., Bolognini, G., & Di Pasquale, F. (2011). Optimization of long-range BOTDA sensors with high resolution using first-order bi-directional Raman amplification. Optics Express, 19(5), 4444. doi:10.1364/oe.19.004444Soto, M. A., Taki, M., Bolognini, G., & Pasquale, F. D. (2012). Simplex-Coded BOTDA Sensor Over 120-km SMF With 1-m Spatial Resolution Assisted by Optimized Bidirectional Raman Amplification. IEEE Photonics Technology Letters, 24(20), 1823-1826. doi:10.1109/lpt.2012.2212183Jia, X.-H., Rao, Y.-J., Yuan, C.-X., Li, J., Yan, X.-D., Wang, Z.-N., … Peng, F. (2013). Hybrid distributed Raman amplification combining random fiber laser based 2nd-order and low-noise LD based 1st-order pumping. Optics Express, 21(21), 24611. doi:10.1364/oe.21.024611Soto, M. A., Angulo-Vinuesa, X., Martin-Lopez, S., Chin, S.-H., Ania-Castanon, J. D., Corredera, P., … Thevenaz, L. (2014). Extending the Real Remoteness of Long-Range Brillouin Optical Time-Domain Fiber Analyzers. Journal of Lightwave Technology, 32(1), 152-162. doi:10.1109/jlt.2013.2292329Soto, M. A., Bolognini, G., & Pasquale, F. D. (2009). Distributed optical fibre sensors based on spontaneous Brillouin scattering employing multimode Fabry-Pérot lasers. Electronics Letters, 45(21), 1071. doi:10.1049/el.2009.2381Li, C., Wang, F., Lu, Y., & Zhang, X. (2012). SNR enhancement in Brillouin optical time domain reflectometer using multi-wavelength coherent detection. Electronics Letters, 48(18), 1139-1141. doi:10.1049/el.2012.1248Voskoboinik, A., Wang, J., Shamee, B., Nuccio, S. R., Zhang, L., Chitgarha, M., … Tur, M. (2011). SBS-Based Fiber Optical Sensing Using Frequency-Domain Simultaneous Tone Interrogation. Journal of Lightwave Technology, 29(11), 1729-1735. doi:10.1109/jlt.2011.2145411Voskoboinik, A., Yilmaz, O. F., Willner, A. W., & Tur, M. (2011). Sweep-free distributed Brillouin time-domain analyzer (SF-BOTDA). Optics Express, 19(26), B842. doi:10.1364/oe.19.00b842Chaube, P., Colpitts, B. G., Jagannathan, D., & Brown, A. W. (2008). Distributed Fiber-Optic Sensor for Dynamic Strain Measurement. IEEE Sensors Journal, 8(7), 1067-1072. doi:10.1109/jsen.2008.926107Nikles, M., Thevenaz, L., & Robert, P. A. (1997). Brillouin gain spectrum characterization in single-mode optical fibers. Journal of Lightwave Technology, 15(10), 1842-1851. doi:10.1109/50.633570Jacobs, I. (1995). Dependence of optical amplifier noise figure on relative-intensity-noise. Journal of Lightwave Technology, 13(7), 1461-1465. doi:10.1109/50.400712Bolognini, G., Soto, M. A., & Di Pasquale, F. (2009). Fiber-Optic Distributed Sensor Based on Hybrid Raman and Brillouin Scattering Employing Multiwavelength Fabry–PÉrot Lasers. IEEE Photonics Technology Letters, 21(20), 1523-1525. doi:10.1109/lpt.2009.202889

Holograms to Focus Arbitrary Ultrasonic Fields through the Skull

[EN] We report 3D-printed acoustic holographic lenses for the formation of ultrasonic fields of complex spatial distribution inside the skull. Using holographic lenses, we experimentally, numerically and theoretically produce acoustic beams whose spatial distribution matches target structures of the central nervous system. In particular, we produce three types of targets of increasing complexity. First, a set of points are selected at the center of both right and left human hippocampi. Experiments using a skull phantom and 3D printed acoustic holographic lenses show that the corresponding bi-focal lens simultaneously focuses acoustic energy at the target foci, with good agreement between theory and simulations. Second, an arbitrary curve is set as the target inside the skull phantom. Using time-reversal methods the holographic beam bends following the target path, in a similar way as self-bending beams do in free space. Finally, the right human hippocampus is selected as a target volume. The focus of the corresponding holographic lens overlaps with the target volume in excellent agreement between theory in free-media, and experiments and simulations including the skull phantom. The precise control of focused ultrasound into the central nervous system is mainly limited due to the strong phase aberrations produced by refraction and attenuation of the skull. Using the present method, the ultrasonic beam can be focused not only at a single point but overlapping one or various target structures simultaneously using low-cost 3D-printed acoustic holographic lens. The results open new paths to spread incoming biomedical ultrasound applications including blood-brain barrier opening and neuromodulation.This work is supported by the Spanish Ministry of Economy and Innovation (MINECO) through Project No. TEC2016-80976-R. 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Ultrasound focusing using magnetic resonance acoustic radiation force imaging: Application to ultrasound transcranial therapy. Medical Physics, 37(6Part1), 2934-2942. doi:10.1118/1.3395553Jolesz, F. A. (Ed.). (2014). Intraoperative Imaging and Image-Guided Therapy. doi:10.1007/978-1-4614-7657-3Shen, C., Xu, J., Fang, N. X., & Jing, Y. (2014). Anisotropic Complementary Acoustic Metamaterial for Canceling out Aberrating Layers. Physical Review X, 4(4). doi:10.1103/physrevx.4.041033Maimbourg, G., Houdouin, A., Deffieux, T., Tanter, M., & Aubry, J.-F. (2018). 3D-printed adaptive acoustic lens as a disruptive technology for transcranial ultrasound therapy using single-element transducers. Physics in Medicine & Biology, 63(2), 025026. doi:10.1088/1361-6560/aaa037Ferri, M., Bravo, J. M., Redondo, J., & Sánchez-Pérez, J. V. (2019). Enhanced Numerical Method for the Design of 3-D-Printed Holographic Acoustic Lenses for Aberration Correction of Single-Element Transcranial Focused Ultrasound. Ultrasound in Medicine & Biology, 45(3), 867-884. doi:10.1016/j.ultrasmedbio.2018.10.022Hynynen, K., McDannold, N., Vykhodtseva, N., & Jolesz, F. A. (2001). Noninvasive MR Imaging–guided Focal Opening of the Blood-Brain Barrier in Rabbits. Radiology, 220(3), 640-646. doi:10.1148/radiol.2202001804Tyler, W. J., Tufail, Y., Finsterwald, M., Tauchmann, M. L., Olson, E. J., & Majestic, C. (2008). Remote Excitation of Neuronal Circuits Using Low-Intensity, Low-Frequency Ultrasound. PLoS ONE, 3(10), e3511. doi:10.1371/journal.pone.0003511Schneider, U., Pedroni, E., & Lomax, A. (1996). The calibration of CT Hounsfield units for radiotherapy treatment planning. Physics in Medicine and Biology, 41(1), 111-124. doi:10.1088/0031-9155/41/1/009Mast, T. D. (2000). Empirical relationships between acoustic parameters in human soft tissues. Acoustics Research Letters Online, 1(2), 37-42. doi:10.1121/1.1336896Mazziotta, J. C., Toga, A. W., Evans, A., Fox, P., & Lancaster, J. (1995). 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Broadband acoustic focusing by Airy-like beams based on acoustic metasurfaces. Journal of Applied Physics, 123(4), 044503. doi:10.1063/1.501070

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844S117112Marra, G., Ploussard, G., Futterer, J., & Valerio, M. (2019). Controversies in MR targeted biopsy: alone or combined, cognitive versus software-based fusion, transrectal versus transperineal approach? World Journal of Urology, 37(2), 277-287. doi:10.1007/s00345-018-02622-5Ahdoot, M., Lebastchi, A. H., Turkbey, B., Wood, B., & Pinto, P. A. (2019). Contemporary treatments in prostate cancer focal therapy. Current Opinion in Oncology, 31(3), 200-206. doi:10.1097/cco.0000000000000515Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Allen, P. D., Graham, J., Williamson, D. C., & Hutchinson, C. E. (s. f.). Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification. 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006. doi:10.1109/isbi.2006.1624940Freedman, D., Radke, R. J., Tao Zhang, Yongwon Jeong, Lovelock, D. M., & Chen, G. T. Y. (2005). Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging, 24(3), 281-292. doi:10.1109/tmi.2004.841228Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407-1417. doi:10.1118/1.2842076Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241. doi:10.1007/978-3-319-24574-4_28He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.322Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). doi:10.1109/3dv.2016.79Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., & Yan, P. (2017). Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-

Planck intermediate results: XLVII. Planck constraints on reionization history

open167siWe investigate constraints on cosmic reionization extracted from the Planck cosmic microwave background (CMB) data. We combine the Planck CMB anisotropy data in temperature with the low-multipole polarization data to fit Lambda CDM models with various parameterizations of the reionization history. We obtain a Thomson optical depth tau = 0.058 +/- 0.012 for the commonly adopted instantaneous reionization model. This confirms, with data solely from CMB anisotropies, the low value suggested by combining Planck 2015 results with other data sets, and also reduces the uncertainties. We reconstruct the history of the ionization fraction using either a symmetric or an asymmetric model for the transition between the neutral and ionized phases. To determine better constraints on the duration of the reionization process, we also make use of measurements of the amplitude of the kinetic Sunyaev-Zeldovich (kSZ) effect using additional information from the high-resolution Atacama Cosmology Telescope and South Pole Telescope experiments. The average redshift at which reionization occurs is found to lie between z = 7.8 and 8.8, depending on the model of reionization adopted. Using kSZ constraints and a redshift-symmetric reionization model, we find an upper limit to the width of the reionization period of Delta z < 2.8. In all cases, we find that the Universe is ionized at less than the 10% level at redshifts above z similar or equal to 10. This suggests that an early onset of reionization is strongly disfavoured by the Planck data. We show that this result also reduces the tension between CMB-based analyses and constraints from other astrophysical sources.openAdam, R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Ballardini, M.; Banday, A.J.; Barreiro, R.B.; Bartolo, N.; Basak, S.; Battye, R.; Benabed, K.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Boulanger, F.; Bucher, M.; Burigana, C.; Calabrese, E.; Cardoso, J.-F.; Carron, J.; Chiang, H.C.; Colombo, L.P.L.; Combet, C.; Comis, B.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Davis, R.J.; De Bernardis, P.; De Rosa, A.; De Zotti, G.; Delabrouille, J.; Di Valentino, E.; Dickinson, C.; Diego, J.M.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Elsner, F.; Enßlin, T.A.; Eriksen, H.K.; Falgarone, E.; Fantaye, Y.; Finelli, F.; Forastieri, F.; Frailis, M.; Fraisse, A.A.; Franceschi, E.; Frolov, A.; Galeotta, S.; Galli, S.; Ganga, K.; Génova-Santos, R.T.; Gerbino, M.; Ghosh, T.; González-Nuevo, J.; Górski, K.M.; Gruppuso, A.; Gudmundsson, J.E.; Hansen, F.K.; Helou, G.; Henrot-Versillé, S.; Herranz, D.; Hivon, E.; Huang, Z.; Ilić, S.; Jaffe, A.H.; Jones, W.C.; Keihänen, E.; Keskitalo, R.; Kisner, T.S.; Knox, L.; Krachmalnicoff, N.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Langer, M.; Lasenby, A.; Lattanzi, M.; Lawrence, C.R.; Le Jeune, M.; Levrier, F.; Lewis, A.; Liguori, M.; Lilje, P.B.; López-Caniego, M.; Ma, Y.-Z.; MacIás-Pérez, J.F.; Maggio, G.; Mangilli, A.; Maris, M.; Martin, P.G.; Martínez-González, E.; Matarrese, S.; Mauri, N.; Mcewen, J.D.; Meinhold, P.R.; Melchiorri, A.; Mennella, A.; Migliaccio, M.; Miville-Deschênes, M.-A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Moss, A.; Naselsky, P.; Natoli, P.; Oxborrow, C.A.; Pagano, L.; Paoletti, D.; Partridge, B.; Patanchon, G.; Patrizii, L.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Plaszczynski, S.; Polastri, L.; Polenta, G.; Puget, J.-L.; Rachen, J.P.; Racine, B.; Reinecke, M.; Remazeilles, M.; Renzi, A.; Rocha, G.; Rossetti, M.; Roudier, G.; Rubinõ-Martín, J.A.; Ruiz-Granados, B.; Salvati, L.; Sandri, M.; Savelainen, M.; Scott, D.; Sirri, G.; Sunyaev, R.; Suur-Uski, A.-S.; Tauber, J.A.; Tenti, M.; Toffolatti, L.; Tomasi, M.; Tristram, M; Trombetti, T.; Valiviita, J.; Van Tent, F.; Vielva, P.; Villa, F.; Vittorio, N.; Wandelt, B.D.; Wehus, I.K.; White, M.; Zacchei, A.; Zonca, A.Adam, R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Ballardini, M.; Banday, A. J.; Barreiro, R. B.; Bartolo, Nicola; Basak, S.; Battye, R.; Benabed, K.; Bernard, J. P.; Bersanelli, M.; Bielewicz, P.; Bock, J. J.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Boulanger, F.; Bucher, M.; Burigana, C.; Calabrese, E.; Cardoso, J. F.; Carron, J.; Chiang, H. C.; Colombo, L. P. L.; Combet, C.; Comis, B.; Couchot, F.; Coulais, A.; Crill, B. P.; Curto, A.; Cuttaia, F.; Davis, R. J.; De Bernardis, P.; De Rosa, A.; De Zotti, G.; Delabrouille, J.; Di Valentino, E.; Dickinson, C.; Diego, J. M.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Falgarone, E.; Fantaye, Y.; Finelli, F.; Forastieri, F.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Frolov, A.; Galeotta, S.; Galli, S.; Ganga, K.; Génova Santos, R. T.; Gerbino, M.; Ghosh, T.; González Nuevo, J.; Górski, K. M.; Gruppuso, A.; Gudmundsson, J. E.; Hansen, F. K.; Helou, G.; Henrot Versillé, S.; Herranz, D.; Hivon, E.; Huang, Z.; Ilić, S.; Jaffe, A. H.; Jones, W. C.; Keihänen, E.; Keskitalo, R.; Kisner, T. S.; Knox, L.; Krachmalnicoff, N.; Kunz, M.; Kurki Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J. M.; Langer, M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Le Jeune, M.; Levrier, F.; Lewis, A.; Liguori, Michele; Lilje, P. B.; López Caniego, M.; Ma, Y. Z.; MacIás Pérez, J. F.; Maggio, G.; Mangilli, A.; Maris, M.; Martin, P. G.; Martínez González, E.; Matarrese, Sabino; Mauri, N.; Mcewen, J. D.; Meinhold, P. R.; Melchiorri, A.; Mennella, A.; Migliaccio, M.; Miville Deschênes, M. A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Moss, A.; Naselsky, P.; Natoli, P.; Oxborrow, C. A.; Pagano, L.; Paoletti, D.; Partridge, B.; Patanchon, G.; Patrizii, L.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Plaszczynski, S.; Polastri, L.; Polenta, G.; Puget, J. L.; Rachen, J. P.; Racine, B.; Reinecke, M.; Remazeilles, M.; Renzi, Alessandro; Rocha, G.; Rossetti, M.; Roudier, G.; Rubinõ Martín, J. A.; Ruiz Granados, B.; Salvati, L.; Sandri, M.; Savelainen, M.; Scott, D.; Sirri, G.; Sunyaev, R.; Suur Uski, A. S.; Tauber, J. A.; Tenti, M.; Toffolatti, L.; Tomasi, M.; Tristram, M; Trombetti, T.; Valiviita, J.; Van Tent, F.; Vielva, P.; Villa, F.; Vittorio, N.; Wandelt, B. D.; Wehus, I. K.; White, M.; Zacchei, A.; Zonca, A