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In this study, a microfluidic paper-based analytical device (mu PAD) was integrated with a smartphone app capable of offline (without internet access) image processing and analysis for the rapid colorimetric detection of glucose. A self-inking stamp was used to form hydrophobic channels on a piece of paper-towel due to its superior water absorption efficiency. As demonstrated, the developed sensor was employed for the colorimetric detection of glucose in artificial saliva in the linear scope of 0 - 1 mM with a calculated detection limit of 29.65 mu M. The experimental results show that the quantitative analysis of glucose with the proposed smartphone platform could be completed in less than one minute. The app developed for the smartphone platform is capable of extracting the color-changing area with an embedded image processing tool which could address the problem of color uniformity in the detection zones of mu PAD. The integrated platform has great potential to be used for non-invasive measurements of glucose in body fluids, like tears, sweat and saliva.

We present recent highlights from the most recent operation phases of Wendelstein 7-X, the most advanced stellarator in the world. Stable detachment with good particle exhaust, low impurity content, and energy confinement times exceeding 100 ms, have been maintained for tens of seconds. Pellet fueling allows for plasma phases with reduced ion-temperature-gradient turbulence, and during such phases, the overall confinement is so good (energy confinement times often exceeding 200 ms) that the attained density and temperature profiles would not have been possible in less optimized devices, since they would have had neoclassical transport losses exceeding the heating applied in W7-X. This provides proof that the reduction of neoclassical transport through magnetic field optimization is successful. W7-X plasmas generally show good impurity screening and high plasma purity, but there is evidence of longer impurity confinement times during turbulence-suppressed phases. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under Grant Agreement No. 633053. Peer Reviewed Article signat per 497 autors/es: Thomas Sunn Pedersen1,2,∗ , I. Abramovic3, P. Agostinetti4, M. Agredano Torres1, S. Äkäslompolo1, J. Alcuson Belloso1, P. Aleynikov1, K. Aleynikova1, M. Alhashimi1, A. Ali1, N. Allen5, A. Alonso6, G. Anda7, T. Andreeva1, C. Angioni8, A. Arkhipov8, A. Arnold1, W. Asad8, E. Ascasibar6, M.-H. Aumeunier9, K. Avramidis10, E. Aymerich11, S.-G. Baek3, J. Bähner1, A. Baillod12, M. Balden1, M. Balden8, J. Baldzuhn1, S. Ballinger3, M. Banduch1, S. Bannmann1, A. Banon Navarro8, A. Bañon Navarro ´ 1, T. Barbui13, C. Beidler1, C. Belafdil9, A. Bencze7, A. Benndorf1, M. Beurskens1, C. Biedermann1, O. Biletskyi14, B. Blackwell15, M. Blatzheim1, T. Bluhm1, D. Böckenhoff1, G. Bongiovi16, M. Borchardt1, D. Borodin17, J. Boscary8, H. Bosch1,18, T. Bosmann19, B. Böswirth8, L. Böttger1, A. Bottino8, S. Bozhenkov1, R. Brakel1, C. Brandt1, T. Bräuer1, H. Braune1, S. Brezinsek17, K. Brunner1, S. Buller1, R. Burhenn1, R. Bussiahn1, B. Buttenschön1, A. Buzás7, V. Bykov1, I. Calvo6, K. Camacho Mata1, I. Caminal20, B. Cannas11, A. Cappa6, A. Carls1, F. Carovani1, M. Carr21, D. Carralero6, B. Carvalho22, J. Casas20, D. Castano-Bardawil17, F. Castejon6, N. Chaudhary1, I. Chelis23, A. Chomiczewska24, J.W. Coenen13,17, M. Cole1, F. Cordella25, Y. Corre9, K. Crombe26, G. Cseh7, B. Csillag7, H. Damm1, C. Day10, M. de Baar27, E. De la Cal6, S. Degenkolbe1, A. Demby13, S. Denk3, C. Dhard1, A. Di Siena8,28, A. Dinklage12, T. Dittmar17, M. Dreval14, M. Drevlak1, P. Drewelow1, P. Drews17, D. Dunai7, E. Edlund3, F. Effenberg29, G. Ehrke1, M. Endler1, D.A. Ennis5, F.J. Escoto6, T. Estrada6, E. Fable8, N. Fahrenkamp1, A. Fanni11, J. Faustin1, J. Fellinger1, Y. Feng1, W. Figacz4, E. Flom13, O. Ford1, T. Fornal24, H. Frerichs13, S. Freundt1, G. Fuchert1, M. Fukuyama30, F. Füllenbach1, G. Gantenbein10, Y. Gao1, K. Garcia13, J.M. García Regaña6, I. García-Cortés6, J. Gaspar31, D.A. Gates29, J. Geiger1, B. Geiger13, L. Giudicotti32, A. González6, A. Goriaev26,33, D. Gradic1, M. Grahl1, J.P. Graves12, J. Green13, E. Grelier9, H. Greuner8, S. Groß1, H. Grote1, M. Groth34, M. Gruca24, O. Grulke1,35, M. Grün1, J. Guerrero Arnaiz1, S. Günter8, V. Haak1, M. Haas1, P. Hacker1, A. Hakola36, A. Hallenbert1, K. Hammond29, X. Han17,37, S.K. Hansen3, J.H. Harris38, H. Hartfuß1, D. Hartmann1, D. Hathiramani1, R. Hatzky8, J. Hawke39, S. Hegedus7, B. Hein8, B. Heinemann8, P. Helander12, S. Henneberg1, U. Hergenhahn8,40, C. Hidalgo6, F. Hindenlang8, M. Hirsch1, U. Höfel1, K.P. Hollfeld17, A. Holtz1, D. Hopf8, D. Höschen17, M. Houry9, J. Howard19, X. Huang41, M. Hubeny17, S. Hudson29, K. Ida9, Y. Igitkhanov10, V. Igochine8, S. Illy10, C. Ionita-Schrittwieser42, M. Isobe39, M. Jabłczynska ´ 24, S. Jablonski24, B. Jagielski1, M. Jakubowski1, A. Jansen van Vuuren1, J. Jelonnek10, F. Jenko8, F. Jenko8, T. Jensen35, H. Jenzsch1, P. Junghanns8, J. Kaczmarczyk24, J. Kallmeyer1, U. Kamionka1, M. Kandler8, S. Kasilov43, Y. Kazakov26, D. Kennedy1, A. Kharwandikar1, M. Khokhlov1, C. Kiefer8, C. Killer1, A. Kirschner17, R. Kleiber1, T. Klinger12, S. Klose1, J. Knauer1, A. Knieps17, F. Köchl44, G. Kocsis7, Ya.I. Kolesnichenko45, A. Könies1, R. König1, J. Kontula34, P. Kornejew1, J. Koschinsky, M.M. Kozulia14, A. Krämer-Flecken17, R. Krampitz1, M. Krause1, N. Krawczyk24, T. Kremeyer1, L. Krier10, D.M. Kriete5, M. Krychowiak1, I. Ksiazek46, M. Kubkowska24, M. Kuczynski1, G. Kühner1, A. Kumar15, T. Kurki-Suonio34, S. Kwak1, M. Landreman47, P.T. Lang8, A. Langenberg1, H.P. Laqua12, H. Laqua1, R. Laube1, S. Lazerson1, M. Lewerentz1, C. Li17, Y. Liang17, Ch. Linsmeier17, J. Lion1, A. Litnovsky17,48, S. Liu37, J. Lobsien1, J. Loizu12, J. Lore38, A. Lorenz1, U. Losada6, F. Louche26, R. Lunsford29, V. Lutsenko45, M. Machielsen12, F. Mackel8, J. Maisano-Brown3, O. Maj8, D. Makowski49, G. Manduchi50, E. Maragkoudakis6, O. Marchuk17, S. Marsen1, E. Martines4, J. Martinez-Fernandez6, M. Marushchenko1, S. Masuzaki41, D. Maurer5, M. Mayer8, K.J. McCarthy6, O. Mccormack4, P. McNeely1, H. Meister8, B. Mendelevitch8, S. Mendes1, A. Merlo1, A. Messian26, A. Mielczarek49, O. Mishchenko1, B. Missal1, R. Mitteau9, V.E. Moiseenko14, A. Mollen1, V. Moncada9, T. Mönnich1, T. Morisaki41, D. Moseev1, G. Motojima41, S. Mulas6, M. Mulsow1, M. Nagel1, D. Naujoks1, V. Naulin35, T. Neelis19, H. Neilson29, R. Neu8, O. Neubauer17, U. Neuner1, D. Nicolai17, S.K. Nielsen35, H. Niemann1, T. Nishiza1, T. Nishizawa1, T. Nishizawa8, C. Nührenberg1, R. Ochoukov8, J. Oelmann17, G. Offermanns17 K. Ogawa41, S. Okamura41, J. Ölmanns17, J. Ongena26, J. Oosterbeek1, M. Otte1, N. Pablant29, N. Panadero Alvarez6, N. Panadero Alvarez6, A. Pandey1, E. Pasch1, R. Pavlichenko14, A. Pavone1, E. Pawelec46, G. Pechstein1, G. Pelka24, V. Perseo1, B. Peterson41, D. Pilopp1, S. Pingel1, F. Pisano11, B. Plöckl8, G. Plunk1, P. Pölöskei1, B. Pompe2, A. Popov51, M. Porkolab3, J. Proll19, M.J. Pueschel19,27, M.-E. Puiatti52, A. Puig Sitjes1, F. Purps1, K. Rahbarnia1, M. Rasinski ´ 17, J. Rasmussen35, A. Reiman29, F. Reimold1, M. Reisner8, D. Reiter17, M. Richou9, R. Riedl8, J. Riemann1, K. Riße1, G. Roberg-Clark1, V. Rohde8, J. Romazanov17, D. Rondeshagen1, P. Rong1, L. Rudischhauser1, T. Rummel1, K. Rummel1, A. Runov1, N. Rust1, L. Ryc24, P. Salembier20, M. Salewski35, E. Sanchez6, S. Satake41, G. Satheeswaran17, J. Schacht1, E. Scharff1, F. Schauer8, J. Schilling1, G. Schlisio1, K. Schmid8, J. Schmitt5, O. Schmitz13, W. Schneider1, M. Schneider1, P. Schneider8, R. Schrittwieser42, T. Schröder1, M. Schröder1, R. Schroeder1, B. Schweer26, D. Schwörer1, E. Scott1, E. Scott8, B. Shanahan1, G. Sias11, P. Sichta29, M. Singer1, P. Sinha29, S. Sipliä34, C. Slaby1, M. Sleczka53, H. Smith1, J. Smoniewski54, E. Sonnendrücker8, M. Spolaore4, A. Spring1, R. Stadler8, D. Stanczak24, T. Stange1, I. Stepanov26, L. Stephey13, J. Stober8, U. Stroth8,55, E. Strumberger8, C. Suzuki41, Y. Suzuki41, J. Svensson1, T. Szabolics7, T. Szepesi7, M. Szücs7, F.L. Tabares6, N. Tamura41, A. Tancetti35, C. Tantos10, J. Terry3, H. Thienpondt6, H. Thomsen1, M. Thumm10, J.M. Travere9, P. Traverso5, J. Tretter8, E. Trier8, H. Trimino Mora1, T. Tsujimura41, Y. Turkin1, A. Tykhyi45, B. Unterberg17, P. van Eeten1, B.Ph. van Milligen6, M. van Schoor26, L. Vano1, S. Varoutis10, M. Vecsei7, L. Vela56, J.L. Velasco6, M. Vervier17, N. Vianello50, H. Viebke1, R. Vilbrandt1, G. Vogel8, N. Vogt1, C. Volkhausen1, A. von Stechow1, F. Wagner1, E. Wang17, H. Wang57, F. Warmer1, T. Wauters26, L. Wegener1, T. Wegner1, G. Weir1, U. Wenzel1, A. White3, F. Wilde1, F. Wilms1, T. Windisch1, M. Winkler1, A. Winter1, V. Winters1, R. Wolf118, A.M. Wright29, G.A. Wurden39, P. Xanthopoulos1, S. Xu17, H. Yamada58, H. Yamaguchi41, M. Yokoyama41, M. Yoshinuma41, Q. Yu8, M. Zamanov14, M. Zanini1, M. Zarnstorff29, D. Zhang1, S. Zhou17, J. Zhu1, C. Zhu29, M. Zilker8, A. Zocco1, H. Zohm8, S. Zoletnik7 and L. Zsuga7 // 1 Max Planck Institute for Plasma Physics, Garching and Greifswald, Germany: 2 University of Greifswald, Greifswald, Germany; 3 Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, United States of America; 4 Consorzio RFX, Corso Stati Uniti, 4-35127 Padova, Italy; 5 Auburn University, Auburn, AL 36849, United States of America; 6 CIEMAT, Avenida Complutense, 40, 28040 Madrid, Spain; 7 Center for Energy Research, Konkoly-Thegeut 29-33, 1121 Budapest, Hungary; 8 Max-Planck-Institute for Plasma Physics, Boltzmannstraße 2, 85748 Garching bei München, Germany; 9 CEA Cadarache, 13115 Saint-Paul-lez-Durance, France; 10 Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany; 11 University of Cagliari, Via Universita, 40, 09124 Cagliari, Italy; 12 École Polytechnique Fédérale de Lausanne, Swiss Plasma Center, CH-1015 Lausanne, Switzerland; 13 University of Wisconsin–Madison, Engineering Drive, Madison, WI 53706, United States of America; 14 Institute of Plasma Physics, National Science Center ‘Kharkiv Institute of Physics and Technology’, Kharkiv, Ukraine; 15 The Australian National University, Acton ACT 2601, Canberra, Australia; 16 Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 6, Palermo, 90128, Italy; 17 Forschungszentrum Jülich GmbH, Institut für Energie-und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany; 18 Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany; 19 Eindhoven University of Technology, 5600 MB Eindhoven, Netherlands; 20 Universitat Politècnica de Catalunya. BarcelonaTech, C. Jordi Girona, 31, 08034 Barcelona, Spain; 21 Culham Center for Fusion Energy, Abingdon OX14 3EB, United Kingdom; 22 Instituto de Plasmas e Fusao Nuclear, Av. Rovisco Pais, 1049-001 Lisboa, Portugal; 23 Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece; 24 Institute of Plasma Physics and Laser Microfusion, 23 Hery Str., 01-497 Warsaw, Poland; 25 ENEA—Centro Ricerche Frascati, Via Enrico Fermi, 45, 00044 Frascati RM, Italy; 26 Laboratory for Plasma Physics, LPP-ERM/KMS, TEC Partner, B-1000 Brussels, Belgium; 27 Dutch Institute for Fundamental Energy Research, PO Box 6336, 5600 HH Eindhoven, Netherlands; 28 University of Texas, Austin, TX, United States of America; 29 Princeton Plasma Physics Laboratory, Princeton, NJ 08543, United States of America; 30 Kyushu University, 744 Motooka Nishi-ku, Fukuoka 819-0395, Japan; 31 Aix-Marseille University, Jardin du Pharo, 58 Boulevard Charles Livon, 13007, Marseille, France; 32 Department of Physics and Astronomy, Padova University, Via Marzolo 8, 35131 Padova, Italy; 33 Department of Applied Physics, Ghent University, Sint-Pietersnieuwstraat 41 B4, 9000 Ghent, Belgium; 34 Aalto University, 02150 Espoo, Finland; 35 Department of Physics, Technical University of Denmark, Anker Engelunds Vej, 2800 Kgs Lyngby, Denmark; 36 VTT Technical Research Center of Finland Ltd., PO Box 1000, FI-02044 VTT, Finland; 37 Institute of Plasma Physics, Chinese Academy of Sciences, 230031 Hefei, Anhui, China; 38 Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, United States of America; 39 Los Alamos National Laboratory, NM 87545, United States of America; 40 Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany; 41 National Institute for Fusion Science, National Institutes of Natural Sciences, 322-6 Oroshi-cho, Toki, Gifu Prefecture 509-5292, Japan; 42 Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria; 43 Graz University of Technology, Rechbauerstraße 12, 8010 GRAZ, Austria; 44 Austrian Academy of Science, Doktor-Ignaz-Seipel-Platz 2, 1010 Wien, Austria; 45 Institute for Nuclear Research, prospekt Nauky 47, Kyiv 03028, Ukraine; 46 University of Opole, plac Kopernika 11a, 45-001 Opole, Poland; 47 University of Maryland, Paint Branch Drive, College Park, MA 20742, United States of America; 48 National Research Nuclear University MEPhI, 115409 Moscow, Russian Federation; 49 Department of Microelectronics and Computer Science, Lodz University of Technology, Wolczanska 221/223, 90-924 Lodz, Poland; 50 Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 7, 00185 Roma, Italy; 51 Ioffe Physical-Technical Institute of the Russian Academy of Sciences, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation; 52 Istituto di Fisica del Plasma Piero Caldirola, Via Roberto Cozzi, 53, 20125 Milano, Italy; 53 University of Szczecin, 70-453, aleja Papieza Jana Pawła II 22A, Szczecin, Poland; 54 Lawrence University, 711 E Boldt Way, Appleton, WI 54911, United States of America; 55 Physik-Department E28, Technische Universität München, 85747 Garching, Germany; 56 Universidad Carlos III de Madrid, Av. de la Universidad, 30 Madrid, Spain; 57 Yale University, New Haven, CT 06520, United States of America; 58 University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chhiab 277-0882, Japan Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminant

Funder: Royal Society; doi: http://dx.doi.org/10.13039/501100000288, Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727, Funder: John Fell Fund, University of Oxford; doi: http://dx.doi.org/10.13039/501100004789, We use a deep neural network (NN) to detect and place region-of-interest (ROI) boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The NN also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds’ Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying ROI and extracting Gaussian parameters.

Significance: Fourier ptychography microscopy (FPM) is a computational optical imaging technology that employs angularly varying illuminations and a phase retrieval algorithm to achieve a wide field of view and high-resolution imaging simultaneously. In the FPM, LED position error will reduce the quality of the reconstructed high-resolution image. To correct the LED positions, current methods consider each of the LED positions as independent and use an optimization algorithm to get each of the positions. When the positional misalignment is large or the search position falls into a local optimal value, the current methods may lack stability and accuracy. Aim: We improve the model of the LED position and propose an accurate and stable two-step correction scheme (tcFPM) to calibrate the LED position error. Approach: The improved LED positions model combines the overall offset, which represents the relative deviation of the LED array and the optical axis, with the slight deviation of each LED’s independent position. In the tcFPM, the overall offset of the LED array is corrected at first, which obtains an approximate value of the overall offset of the LED array. Then the position of each LED is precisely adjusted, which obtains the slight offset of each LED. Results: This LED position error model is more in line with the actual situation. The simulation and experimental results show that the method has high accuracy in correcting the LED position. Furthermore, the reconstruction process of tcFPM is more stable and significantly improves the quality of the reconstruction results, which is compared with some LED position error correction methods. Conclusions: An LED position error correction technology is proposed, which has a stable iterative process and improves the reconstruction accuracy of complex amplitude.

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Significance: Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. Aim: We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. Approach: The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. Results: The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. Conclusions: The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.

Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy. Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images. Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN). Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of L1 and L2. The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses. Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses (L1 and L2) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, S3, and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively. Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.

Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

Significance: The human vocal fold (VF) oscillates in multiple vectors and consists of distinct layers with varying viscoelastic properties that contribute to the mucosal wave. Office-based and operative laryngeal endoscopy are limited to diagnostic evaluation of the VF epithelial surface only and are restricted to axial-plane characterization of the horizontal mucosal wave. As such, understanding of the biomechanics of human VF motion remains limited. Aim: Optical coherence tomography (OCT) is a micrometer-resolution, high-speed endoscopic imaging modality which acquires cross-sectional images of tissue. Our study aimed to leverage OCT technology and develop quantitative methods for analyzing the anatomy and kinematics of in vivo VF motion in the coronal plane. Approach: A custom handheld laryngeal stage was used to capture OCT images with 800 A-lines at 250 Hz. Automated image postprocessing and analytical methods were developed. Results: Novel kinematic analysis of in vivo, long-range OCT imaging of the vibrating VF in awake human subjects is reported. Cross-sectional, coronal-plane panoramic videos of the larynx during phonation are presented with three-dimensional videokymographic and space-time velocity analysis of VF motion. Conclusions: Long-range OCT with automated computational methods allows for cross-sectional dynamic laryngeal imaging and has the potential to broaden our understanding of human VF biomechanics and sound production.

Significance: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. Aim: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. Approach: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting. Results: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200 μm×200 μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate. Conclusions: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Significance: Large space-bandwidth product is highly desirable in many biomedical imaging. Fourier ptychographic microscopy (FPM) is a computational imaging technique that can significantly increase the space-bandwidth product of a standard microscope. The illuminator of a Fourier ptychographic microscope is not flexible at present, and it is inconvenient to meet different imaging needs. Aim: An illuminator based on a two-axis motorized rotation stage was presented to provide a more flexible illuminating way with the goal of meeting different imaging needs. Approach: The illuminator adopts a concentric illuminating method to provide coherent illumination in any direction on the sample plane. The sampling pattern can be freely designed and changed according to the parameters of the imaging system. A dither removing algorithm was proposed to remove the potential dither influence introduced in the image acquisition process. Results: The illuminator could be conveniently integrated into different imaging systems. The feasibility and flexibility were demonstrated by applying it to imaging systems with numerical aperture of 0.045 and 0.01. The resolution gain is about 4- and 13-fold, respectively. The effectiveness of the dither removing algorithm was validated in both simulation and experiment. Conclusions: A more flexible illuminator for FPM was presented to meet different imaging needs. A dither removing algorithm was proposed to remove dither influence.

Significance: The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, μCT). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging. Aim: To facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a μCT scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm. Approach: The proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. NaGdF4 nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality. Results: We have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality. Conclusions: The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.

Significance: Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest. Aim: To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution. Approach: We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol. Results: The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved. Conclusions: We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.

Significance Rehabilitation through facial prostheses' main goal is to aid individual's social reintegration as well as improving their quality of life. However, this treatment is not yet widely available in Brazil due to the lack of specialized clinics and the cost associated with the high number of necessary medical appointments until the final result. One of the steps in the process consists of measuring skin color, which is observer-dependent and may suffer from the effect of metamerism. Aim The methodology of our work aims to obtain a standard between different devices and greater fidelity to the color seen in person in order to reduce face-to-face iterations, reduce costs, and ensure better final results. Approach A physical device and a computer program were improved from previous projects. The changes included implementing the Thin-Plate Spline 3D algorithm for color calibration, in addition to an optional non-uniform illumination correction in the process. We also aim to improve the project's accessibility using a colorimeter. The methodology and the algorithms were both compared to readings from direct skin measurements as well as color references. Results After processing, the ΔEab* metric between images from the same segments is taken with different cameras and conditions of illumination decreased from 18.81 ± 4.85 to 4.85 ± 1.72. In addition, when the images were compared to colorimetric readings of the skin, the difference went from 14.93 ± 4.11 to 5.85 ± 1.61. It was also observed that using a less expensive device did not impact the readings. The project is open source and available at Github. Conclusions The results demonstrate the possibility of applying the methodology to assist in the manufacturing of facial prostheses to decrease the total number of consultations, in addition to providing greater reliability of the final result.

Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases. ispartof: JOURNAL OF MEDICAL IMAGING vol:8 issue:Suppl 1 ispartof: location:United States status: published

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.

Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient. Approach: A pretrained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH). Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87. Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient’s previously acquired imaging.

Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding. Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI. Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis. Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training. Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty. Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant. Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.

Significance: Currently, tissue biopsies are sectioned into 3- to 5-μm-thick slices that are used for conventional pathology analysis. Previous work by confocal microscopy and light-sheet microscopy has shown that analyzing biopsies intact in three-dimensions (3D) is possible and may lead to a better understanding of cancer growth patterns. Although accurate, these methods require fluorescent staining of the tissue, in addition to tissue clearing. If the 3D biopsy analysis could be done sufficiently swiftly, this approach may be used for on-site assessment of the adequacy of a biopsy taken. Aim: We aim to show that, by transmission microscopy of optically cleared tissue punches, the tissue architecture can be determined without the need for fluorescent staining. Approach: Transmission microscopy is used by combining bright field microscopy with dark field and epifluorescent microscopy to compare samples that have also been analyzed by fluorescent confocal microscopy. Results: With increasing distance to the focal plane, the higher-frequency part of the spatial frequency spectrum of transmitted light is attenuated increasingly. This property is exploited for tissue segmentation, detecting whether tissue is present at a certain position in the focal plane image. Using this approach, we show that a 3D rendering of the internal cavity or tubules structure of punch biopsies, which are up to 1-mm thick, can be acquired in ≈1 min scan time per imaging modality. The images of the overall tissue architecture that are obtained are similar to those from the confocal microscopy benchmark, without requiring fluorescent staining. Conclusions: Images of the overall tissue architecture can be obtained from transmission microcopy; they are similar to those from the confocal microscopy benchmark without requiring fluorescent staining. Tissue clearing is still needed. The total scan time of the present method is significantly shorter at a fraction of the device costs.

Visualization of 3D high-speed biological events, e.g., signaling of neural circuits, has been the driving force of many emerging 3D imaging techniques, such as random-access microscopy1,2 and light-sheet microscopy (LSM).3,4 The former approach selectively scans the laser beam in the regions of interest to monitor biological events at speeds up to 10-s kHz,1,2 and the latter approach exploits a light sheet to parallelly illuminate and record 2D images at frame rates up to 100-s Hz;3,4 accordingly, clear 3D images can be obtained through the application of custom-designed phase masks3 or synchronization with an axial scanner,4 achieving a volume image rate of ∼30 Hz. Light-field microscopy (LFM)5 is a high-speed 3D imaging technique that captures a volume image via a single exposure from a camera, where the imaging speed is limited by cameras. Briefly, a 3D image in LFM can be restored from a recorded “light-field image” that simultaneously contains the positions of objects, i.e., 2D image, and angles of illumination, i.e., another 2D image, which is termed a “4D” image. Comparing with other fast fluorescent imaging methods, LFM achieves the highest volume imaging rate at the expense of low spatial and angular resolution,5,6 which prevents the broad adoption of LFM in biological studies. To address the issue, both computational and optical methods have been developed. For example, deconvolution algorithms have been applied to LFM7 for imaging neuronal activities at high speed.8–10 Although simple and effective, this method is prone to generating artifacts near the native image plane (NIP) and requires powerful computers for processing dense 3D images. In addition to the deconvolution algorithm, the phase space retrieval method11 has been developed to computationally recover a high-resolution light-field image. Comparing with the deconvolution method, this method shows decreasing fidelity away from the NIP. In terms of optical methods, insertion of phase masks,12 optimization of optical distances,13 adoption of an orthogonal detection arm,14 and selective plane illumination15 have been demonstrated to improve the spatial and angular resolution of LFM. However, the increased complexity in optical design often prevents the easy application of computational methods, e.g., deconvolution algorithms. In this work, we present a new hybrid LFM via selective optical illumination, which reconstructs a high-resolution 4D light-field image from a simultaneously recorded 2D image and low-resolution 4D light-field image. Briefly, multi-layer 2D images within the depth of the focus (DOF) are generated via deconvolution. Next, the multi-layer images are used as constraints for the iterated Fourier transform of the interpolated 4D light-field image to reconstruct a high-resolution 4D light-field image. Accordingly, the hybrid LFM achieves an imaging speed of 100-s volumes per second (vps) with a 3D field of view of 250×250×80 μm3 in the x,y, and z axes, respectively. The measured axial and lateral resolutions, i.e., full width at half maximum (FWHM), are 9.36 and 1.85 μm, respectively.

Significance: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures. Aim: To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues. Approach: We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS. Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00±11.77% (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra. Conclusions: LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.

Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores=94% for non-zeros padding and F1-score=96% for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.

Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in the medical domain. Herein, we are focused on the required preprocessing steps that should be applied to medical images prior to deep neural networks. Approach: To be able to employ the publicly available algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. Required pre-processing steps for computed tomography (CT) and magnetic resonance (MR) images in their correct order are discussed in detail. We further supported our discussion by relevant experiments to investigate the efficiency of the listed preprocessing steps. Results: Our experiments confirmed how using appropriate image pre-processing in the right order can improve the performance of deep neural networks in terms of better classification and segmentation. Conclusions: This work investigates the appropriate pre-processing steps for CT and MR images of prostate cancer patients, supported by several experiments that can be useful for educating those new to the field (https://github.com/NIH-MIP/Radiology_Image_Preprocessing_for_Deep_Learning).

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities. ispartof: JOURNAL OF MEDICAL IMAGING vol:7 issue:4 ispartof: location:United States status: published

Significance: The rates of melanoma and nonmelanoma skin cancer are rising across the globe. Due to a shortage of board-certified dermatologists, the burden of dermal lesion screening and erythema monitoring has fallen to primary care physicians (PCPs). An adjunctive device for lesion screening and erythema monitoring would be beneficial because PCPs are not typically extensively trained in dermatological care. Aim: We aim to examine the feasibility of using a smartphone-camera-based dermascope and a USB-camera-based dermascope utilizing polarized white-light imaging (PWLI) and polarized multispectral imaging (PMSI) to map dermal chromophores and erythema. Approach: Two dermascopes integrating LED-based PWLI and PMSI with both a smartphone-based camera and a USB-connected camera were developed to capture images of dermal lesions and erythema. Image processing algorithms were implemented to provide chromophore concentrations and redness measures. Results: PWLI images were successfully converted to an alternate colorspace for erythema measures, and the spectral bandwidth of the PMSI LED illumination was sufficient for mapping of deoxyhemoglobin, oxyhemoglobin, and melanin chromophores. Both types of dermascopes were able to achieve similar relative concentration results. Conclusion: Chromophore mapping and erythema monitoring are feasible with PWLI and PMSI using LED illumination and smartphone-based cameras. These systems can provide a simpler, more portable geometry and reduce device costs compared with interference-filter-based or spectrometer-based clinical-grade systems. Future research should include a rigorous clinical trial to collect longitudinal data and a large enough dataset to train and implement a machine learning-based image classifier.

Purpose:\ud \ud The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.\ud \ud Approach:\ud \ud A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).\ud \ud Results:\ud \ud MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR = 1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR = 0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR = 1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR = 1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21).\ud \ud Conclusions:\ud \ud MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.

New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from cancer cell cultures grown on vascular structures in phase-contrast microscope images using U-Net neural networks to enable future drug efficacy assessments. We cultivated prostate carcinoma cell lines PC3 and LNCaP on the top of a matrix containing vascular structures. The cells were imaged with a Cell-IQ phase-contrast microscope. Automatic analysis of microscope images could assess the efficacy of tested drugs. The dataset included 36 RGB images and ground-truth segmentations with mutually not exclusive classes. The used method could distinguish vascular structures, cells, spheroids, and cell matter around spheroids in the test images. Some invasive spikes were also detected, but the method could not distinguish the invasive cells in the test images. publishedVersion

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μa) and reduced scattering (μ0 s) on a pixelby-pixel basis. Measurements of μa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, threewavelength SFDI system capable of quantifying μa and μ0 s as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system. Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website. Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μa and μ0 s with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h. Results: The openSFDI systems had an error of 0 6% in μa and −2 3% in μ0 s, compared to a commercial SFDI system. Bland–Altman analysis revealed the limits of agreement between the two systems to be 0.004 mm−1 for μa and −0.06 to 0.1 mm−1 for μ0 s. The openSFDI system had low drift with an average standard deviation of 0.0007 mm−1 and 0.05 mm−1 in μa and μ0 s, respectively. Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging. Funding agencies: U.S. Department of Defense (DoD)United States Department of Defense [W81XWH-15-1-0070]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; European Research Council under the European UnionEuropean Research Council (ERC) [715737]; Fre

Noninvasive in vivo imaging of microcirculatory blood flow or perfusion (average speed times concentration of moving red blood cells) is of interest in several clinical applications, including monitoring of burn wounds1,2 and investigation of peripheral arterial disease,3 as well as several others.4 One technique that has gained a lot of focus in the last decades is laser speckle contrast imaging [LSCI; sometimes laser speckle contrast analysis (LASCA)] in which tissue is illuminated with a laser and the resulting speckle pattern is detected with a camera. Light that scatters when interacting with moving particles in the tissue will obtain a Doppler frequency shift depending on the velocity of the particles. The interference of light with different frequencies will give rise to fluctuations in the speckle pattern formed on the imaging sensor. The movement of the speckles on the sensor will cause an image blur that increases with exposure time. The local spatial statistics of this blur can be related to the movement of particles in the tissue, as is done in LSCI.5 LSCI has been increasingly used over laser Doppler imaging mainly due to its simple setup and fast acquisition and data processing. However, conventional LSCI has drawbacks such as a nonlinear response to perfusion, dependency to static scattering contrast, and a high variability in the presence of noise.5,6 Recent work has gone into calibrating and correcting for these issues to make LSCI a more accurate technique.7 Despite this, while it is generally accepted that LSCI is related to microcirculatory perfusion, the relationship is complex and a direct mapping is still not known.4,8 For the older laser Doppler flowmetry (LDF) technique, the relation to microcircular perfusion is better understood, where it is, for example, possible to theoretically show that the perfusion estimate is linearly related to the flow speed.9,10 To address the nonlinearity of LSCI to perfusion, Parthasarathy et al.11 proposed a new setup using multiple exposure times. The technique, called multi-exposure laser speckle contrast imaging (MELSCI, sometimes MESI), obtains information about the speckle motion blur at various exposures, enabling more advanced models to be used when estimating perfusion from the contrast.5,12 Several approaches to capture multi-exposure images have been proposed. The initial approach by Parthasarathy et al.11 was to use a time-modulated laser to achieve multi-exposure images. By capturing all images with a fixed exposure time but only illuminating the tissue during parts of that time, multi-exposures can be generated. However, a problem with this approach is that images with different exposure times are separated in time since they are captured sequentially. Dragojevic et al.13 suggested another approach that addressed this drawback. This was based on a high-speed camera, only capturing images at the shortest exposure time required, and in postprocessing adding successive images together to create longer exposure times. These synthetic exposure times are valid if the interframe delay in the camera is negligible. This method is faster and more accurate than previous methods due to all exposure times essentially being captured simultaneously, but it produces immense amounts of data that must be transferred and processed. At the time, this was, therefore, limited to an offline technique. Hence, MELSCI has so far been held back by technical limitations in both the imaging setup and the computation time of the models. To address this problem of a data and processing bottleneck, we have previously presented a system for synthetic MELSCI,14 based on a high-speed 1000-frames per second (fps) 1-megapixel camera directly interfaced to a field programmable gate array (FPGA) performing the multi-exposure contrast algorithm outlined by Dragojevic13 in real-time. Processed contrast images were sent to a computer, massively reducing the data throughput requirements. This system could produce multi-exposure contrast images at 15.6 fps, using all available frames from the 1000-fps camera, without any loss of data. However, due to insufficient processing power, the system was not able to transfer the processed images to the computer fast enough, and thus could not provide a real-time video-rate output. Furthermore, while the calculation of multi-exposure contrast images was fast enough, at the time there was no multi-exposure perfusion algorithm fast enough to keep up with the continuous stream of contrast images. The model proposed by Parthasarathy et al.11 and later refined by Kazmi et al.15 requires nonlinear fitting to extract perfusion-related parameters from the multi-exposure contrast. This process must be performed individually in each pixel for full field perfusion images, which is not feasible in real-time with satisfactory frame rates.6 We have previously presented a method using machine learning and simulated data from thousands of Monte Carlo tissue models from which we train an artificial neural network (ANN) to translate multi-exposure contrast to laser Doppler perfusion. We showed that this technique not only achieves a model-inference time of

X-ray cone-beam holotomography of unstained tissue from the human central nervous system reveals details down to subcellular length scales. This visualization of variations in the electron density of the sample is based on phase-contrast techniques using intensities formed by self-interference of the beam between object and detector. Phase retrieval inverts diffraction and overcomes the phase problem by constraints such as several measurements at different Fresnel numbers for a single projection. Therefore, the object-to-detector distance (defocus) can be varied. However, for cone-beam geometry, changing defocus changes magnification, which can be problematic in view of image processing and resolution. Alternatively, the photon energy can be altered (multi-E). Far from absorption edges, multi-E data yield the wavelength-independent electron density. We present the multi-E holotomography at the Göttingen Instrument for Nano-Imaging with X-Rays (GINIX) setup of the P10 beamline at Deutsches Elektronen-Synchrotron. The instrument is based on a combined optics of elliptical mirrors and an x-ray waveguide positioned in the focal plane for further coherence, spatial filtering, and high numerical aperture. Previous results showed the suitability of this instrument for nanoscale tomography of unstained brain tissue. We demonstrate that upon energy variation, the focal spot is stable enough for imaging. To this end, a double-crystal monochromator and automated alignment routines are required. Three tomograms of human brain tissue were recorded and jointly analyzed using phase retrieval based on the contrast transfer function formalism generalized to multiple photon energies. Variations of the electron density of the sample are successfully reconstructed.

Image segmentation is a fundamental precursor to quantitative image analysis. At present, no standardised methodology exists for segmenting images of fluorescent proxies for trace evidence. Experiments evaluated (i) whether manual segmentation is reproducible within and between examiners (with three participants repeatedly tracing three images) (ii) whether manually defining a threshold level offers accurate and reproducible results (with 20 examiners segmenting 10 images), and (iii) whether a global thresholding algorithm might perform with similar accuracy, while offering improved reproducibility and efficiency (16 algorithms tested). Statistically significant differences were seen between examiners’ traced outputs. Manually thresholding produced good accuracy on average (within ±1% of the expected values), but poor reproducibility (with multiple outliers). Three algorithms (Yen, MaxEntropy, and RenyiEntropy) offered similar accuracy, with improved reproducibility and efficiency. Together, these findings suggest that appropriate algorithms could perform thresholding tasks as part of a robust workflow for reconstruction studies employing fluorescent proxies for trace evidence.

Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges. Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient’s skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK. Main results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors. Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient’s skin during which the patient is present and no clinical intervention is undertaken.

A significant number of patients receiving breast-conserving surgery (BCS) for invasive carcinoma and ductal carcinoma in situ (DCIS) may need reoperation following tumor-positive margins from final histopathology tests. All current intraoperative margin assessment modalities have specific limitations. As a first step towards the development of a compact system for intraoperative specimen imaging based on edge illumination x-ray phase contrast, we prove that the system’s dimensions can be reduced without affecting imaging performance. We analysed the variation in noise and contrast to noise ratio (CNR) with decreasing system length using the edge illumination x-ray phase contrast imaging setup. Two-(planar) and three-(computed tomography (CT)) dimensional imaging acquisitions of custom phantoms and a breast tissue specimen were made. Dedicated phase retrieval algorithms were used to separate refraction and absorption signals. A ‘single-shot’ retrieval method was also used, to retrieve thickness map images, due to its simple acquisition procedure and reduced acquisition times. Experimental results were compared to numerical simulations where appropriate. The relative contribution of dark noise signal in integrating detectors is significant for low photon count statistics acquisitions. Under constant exposure factors and magnification, a more compact system provides an increase in CNR. Superior CNR results were obtained for refraction and thickness map images when compared to absorption images. Results indicate that the ‘single-shot’ acquisition method is preferable for a compact CT intraoperative specimen scanner; it allows for shorter acquisition times and its combination of the absorption and refraction signals ultimately leads to a higher contrast. The first CT images of a breast specimen acquired with the compact system provided promising results when compared to those of the longer length system.

We present a multispectral fundus camera that performs fast imaging of the ocular posterior pole in the visible and near-infrared (400 to 1300 nm) wavelengths through 15 spectral bands, using a flashlight source made of light-emitting diodes, and CMOS and InGaAs cameras. We investigate the potential of this system for visualizing occult and overlapping structures of the retina in the unexplored wavelength range beyond 900 nm, in which radiation can penetrate deeper into the tissue. Reflectance values at each pixel are also retrieved from the acquired images in the analyzed spectral range. The available spectroscopic information and the visualization of retinal structures, specifically the choroidal vasculature and drusen-induced retinal pigment epithelium degeneration, which are hardly visible in conventional color fundus images, underline the clinical potential of this system as a new tool for ophthalmic diagnosis.

X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality which has the potential for achieving both high sensitivity and spatial resolution simultaneously. For the narrow x-ray beam-based XLCT imaging, based on previous work, a spatial resolution of about double the x-ray beam size can be achieved using a translate/rotate scanning scheme, taking step sizes equal to the x-ray beam width. To break the current spatial resolution limit, we propose a scanning strategy achieved by reducing the scanning step size to be smaller than the x-ray beam size. We performed four sets of numerical simulations and a phantom experiment using cylindrical phantoms and have demonstrated that our proposed scanning method can greatly improve the XLCT-reconstructed image quality compared with the traditional scanning approach. In our simulations, by using a fixed x-ray beam size of 0.8 mm, we were able to successfully reconstruct six embedded targets as small as 0.5 mm in diameter and with the same edge-to-edge distances by using a scanning step as small as 0.2 mm which is a 1.6 times improvement in the spatial resolution compared with the traditional approach. Lastly, the phantom experiment further demonstrated the efficacy of our proposed method in improving the XLCT image quality, with all image quality metrics improving as the step size decreased.

Blood flow is a parameter used to diagnose vascular diseases based on flow speed, blood pressure, and vessel size. Different techniques have been developed to estimate the relative blood flow speed and to improve the visualization of deep blood vessels; one such technique is laser speckle contrast imaging (LSCI). LSCI images contain a high level of noise mainly when deep blood vessels are imaged. To improve their visualization, several approaches for contrast computation have been developed. However, there is a compromise between noise attenuation and temporal resolution. On the one hand, spatial approaches have low spatial resolution, high temporal resolution, and significant noise attenuation, while temporal approaches have the opposite. A recent approach combines a temporal base with a directional process that allows improving the visualization of blood vessels. Nevertheless, it still contains a high level of noise and requires a high number of raw frames for its base. We propose, a space-directional approach focused on improving noise attenuation and temporal resolution for contrast computation. The results of reference approaches and the proposed one are compared quantitatively. Moreover, it is shown that the visualization of blood vessels in LSCI images can be improved by a general morphological process when the noise level is reduced.

Relatively few imaging and sensing technologies are employed to study human lactation physiology. In particular, human mammary development during pregnancy as well as mammary involution after lactation have been poorly described, despite their importance for breast cancer diagnosis and treatment during these phases. Our case study shows the potential of diffuse optical spectroscopic imaging (DOSI) to uniquely study the spatiotemporal changes in mammary tissue composition during the involution of the lactating breast toward its pre-pregnant state. At nine time intervals over a period of eight months after the cessation of breastfeeding, we reconstructed 2-D maps of mammary water content, lipid content, total hemoglobin (THb) concentration, oxygen saturation (StO2), and tissue optical scattering. Mammary lipid content in the nonareolar region showed a significant relative increase of 59%, whereas water content and THb concentration showed a significant relative decrease of 50% and 48%, respectively. Significant changes were also found in StO2 and tissue optical scattering. Our findings are consistent with the gradual replacement of fibroglandular tissue by adipose tissue and vascular regression during mammary involution. Moreover, our data provide unique insight into the dynamics of breast tissue composition and demonstrate the effectiveness of DOSI as a technique to study human lactation physiology.

We report on a pathway for Gabor domain optical coherence microscopy (GD-OCM)-based metrology to assess the donor’s corneal endothelial layers ex vivo. Six corneas from the Lions Eye Bank at Albany and Rochester were imaged with GD-OCM. The raw 3-D images of the curved corneas were flattened using custom software to enhance the 2-D visualization of endothelial cells (ECs); then the ECs within a circle of 500-μm-diameter were analyzed using a custom corner method and a cell counting plugin in ImageJ. The EC number, EC area, endothelial cell density (ECD), and polymegethism (CV) were quantified in five different locations for each cornea. The robustness of the method (defined as the repeatability of measurement together with interoperator variability) was evaluated by independently repeating the entire ECD measurement procedure six times by three different examiners. The results from the six corneas show that the current modality reproduces the ECDs with a standard deviation of 2.3% of the mean ECD in every location, whereas the mean ECD across five locations varies by 5.1%. The resolution and imaging area provided through the use of GD-OCM may help to ultimately better assess the quality of donor corneas in transplantation.

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 − 4, in tapering between healthy airways ( n = 35) and those affected by bronchiectasis ( n = 39). The difference between the mean of the two populations is 0.011 mm − 1, and the difference between the medians of the two populations was 0.006 mm − 1. The tapering measurement retained a 95% confidence interval of ±0.005 mm − 1 in a simulated 25 mAs scan and retained a 95% confidence of ±0.005 mm − 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.