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2. Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence

Author

Shiyam Sundar, Lalith Kumar, Yu, Josef, Muzik, Otto, Kulterer, Oana C, Fueger, Barbara, Kifjak, Daria, Nakuz, Thomas, Shin, Hyung Min, Sima, Annika Katharina, Kitzmantl, Daniela, Badawi, Ramsey D, Nardo, Lorenzo, Cherry, Simon R, Spencer, Benjamin A, Hacker, Marcus, and Beyer, Thomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Networking and Information Technology R&D (NITRD), Biomedical Imaging, Neurosciences, Clinical Research, Bioengineering, Male, Humans, Female, Fluorodeoxyglucose F18, Positron Emission Tomography Computed Tomography, Artificial Intelligence, Human Body, Semantics, Image Processing, Computer-Assisted, multiorgan segmentation, total-body PET, systems medicine, artificial neural networks, automated segmentation, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.

Published
2022

17. Efficacy of Low-Dose Buspirone for Restricted and Repetitive Behavior in Young Children with Autism Spectrum Disorder: A Randomized Trial

Author

Jiang, Huiyuan, Sivaswamy, Lalitha, Steinhilber, Ginger, Kennedy, Kristin, Pawlik, Kathy, Roeder, Ruth, Malian, Monica, Turner, Hailey, Hanna, Amira, Katusin, Jamie, Supula, Deanna, Halverson, Stacey, Guy, William, Gjolaj, Nore, Mangner, Thomas, Chakraborty, Pulak, Wigeluk, Angela, Patterson, Gregory, Mosqueda, Andrew, Burkett, Melissa, DeBoard, Anna, Cornett, Jane, Lee, Mei-li, Lu, Xin, Germain, Catherine, Majkowski, Marianne, French, Beth, Stolinski, Amy, Naguib, Suzi, Wolfe-Christensen, Courtney, Taylor, Sara, Ameli, Jamal, McQueeney, Julie Kemp, Rubinlicht, Michelle, Gorka, Bethany, Zacharski, Erin, Blumer, Jeffrey L., Maxwell, Kathleen, Tasi, Catherine, Toltzis, Philip, Kalic, Jolee, Morus, Dianne, Paulett, Jany, Rosolowski, Bonnie, Banchy, Michael, Westlake, Kathryn, Calabro, Barbara, Tacket, Carol, Adkins, Ina, Buchheit, Kenneth, Hepper, Kathryn, Speicher, David, Bergant, Susan, Haky, Jennifer, Frohnapfel, Mary Beth, Schaefer, Cindy, Browning, Allison, Bauer, Roberta, Embacher, Rebecca, Knapp, Julie, Levy, Stephanie, Davies, Diane, Lach, Donna, Petrich, John, Kreischer, Sharon, Blubaugh, Patty, Morris, Mary Ann, Garver, Carolyn, Andersen, Mariam, Greene, Meredith, Richards, Amanda, Rocha, Maria, Merryman, Ruth, Golla, Sailaja, Lorenzen, Alan, Sherrod, Julie Long, Perryman, Paulette, Lorduy, Kara, Angkustsiri, Kathleen, Plumer, Lauren Back, Siy Leigh, Mary Jacena, Bacalman, Susan C., Roa, Dorcas Liriano, Govindappa, Kushma, Mahajan, Gayatri, Bickel, Erika, Brule, Norman, Hare, Emma, Sarwary, Nadir, Cargill, Dominique, Lee, Joyce, Hom, Smiley, Steindorf, Dennis, Pietrykowski, Mandy, Root, Kindra, Coffey, Sarah, Hanzel, Elise Phelps, Santana, Adriana, Ramirez, Clara, Berci, Stefanie, Gabriel, Nielsen, Hirsch, Glenn, Roberts, Elizabeth, Zwilling, Amanda, Gellatly, Resham, Levy, Dana, Lee, Sandi, Rodman, Jennifer, Oberfield, Nora, Friedland, Susan, Lord, Catherine, Kozloff, Rene, Crouse, Celeste, Millen, Stephanie, Coímbre, Alex J., McCarter, Kelly L., Posey, David, Chugani, Diane C., Chugani, Harry T., Wiznitzer, Max, Parikh, Sumit, Evans, Patricia A., Hansen, Robin L., Nass, Ruth, Janisse, James J., Dixon-Thomas, Pamela, Behen, Michael, Rothermel, Robert, Parker, Jacqueline S., Kumar, Ajay, Muzik, Otto, Edwards, David J., and Hirtz, Deborah

Published
2016
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18. Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates

Author

Sundar, Lalith KS, Muzik, Otto, Rischka, Lucas, Hahn, Andreas, Rausch, Ivo, Lanzenberger, Rupert, Hienert, Marius, Klebermass, Eva-Maria, Füchsel, Frank-Günther, Hacker, Marcus, Pilz, Magdalena, Pataraia, Ekaterina, Traub-Weidinger, Tatjana, and Beyer, Thomas

Published
2019
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19. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies

Author

Sundar, Lalith Kumar Shiyam, Lassen, Martin Lyngby, Gutschmayer, Sebastian, Ferrara, Daria, Calabrò, Anna, Yu, Josef, Kluge, Kilian, Wang, Yiran, Nardo, Lorenzo, Hasbak, Philip, Kjaer, Andreas, Abdelhafez, Yasser G., Wang, Guobao, Cherry, Simon R., Spencer, Benjamin A., Badawi, Ramsey D., Beyer, Thomas, Muzik, Otto, Sundar, Lalith Kumar Shiyam, Lassen, Martin Lyngby, Gutschmayer, Sebastian, Ferrara, Daria, Calabrò, Anna, Yu, Josef, Kluge, Kilian, Wang, Yiran, Nardo, Lorenzo, Hasbak, Philip, Kjaer, Andreas, Abdelhafez, Yasser G., Wang, Guobao, Cherry, Simon R., Spencer, Benjamin A., Badawi, Ramsey D., Beyer, Thomas, and Muzik, Otto

Abstract

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTA-TATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (

Published
2023

20. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies

Author

Shiyam Sundar, Lalith Kumar, primary, Lassen, Martin Lyngby, additional, Gutschmayer, Sebastian, additional, Ferrara, Daria, additional, Calabrò, Anna, additional, Yu, Josef, additional, Kluge, Kilian, additional, Wang, Yiran, additional, Nardo, Lorenzo, additional, Hasbak, Philip, additional, Kjaer, Andreas, additional, Abdelhafez, Yasser G., additional, Wang, Guobao, additional, Cherry, Simon R., additional, Spencer, Benjamin A., additional, Badawi, Ramsey D., additional, Beyer, Thomas, additional, and Muzik, Otto, additional

Published
2023
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28. Non-rigid Surface Registration Using Spherical Thin-Plate Splines

Author

Zou, Guangyu, Hua, Jing, Muzik, Otto, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ayache, Nicholas, editor, Ourselin, Sébastien, editor, and Maeder, Anthony, editor

Published
2007
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29. Depth and hierarchies in the predictive brain: From reaction to action.

Author

Muzik, Otto and Diwadkar, Vaibhav A.

Subjects
*BRAIN anatomy, *TIME perspective, *BODY temperature regulation
Abstract

The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub‐networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub‐networks) and action (implemented in executive sub‐networks). These sub‐networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under:Neuroscience > BehaviorPhilosophy > ActionPsychology > Prediction [ABSTRACT FROM AUTHOR]

Published
2023
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30. A scale space theory based motion correction approach for dynamic PET brain imaging studies

Author

Gutschmayer, Sebastian, primary, Muzik, Otto, additional, Chalampalakis, Zacharias, additional, Ferrara, Daria, additional, Yu, Josef, additional, Kluge, Kilian, additional, Rausch, Ivo, additional, Boellaard, Ronald, additional, Golla, Sandeep S.V., additional, Zuehlsdorff, Sven, additional, Newiger, Hartwig, additional, Beyer, Thomas, additional, and Kumar Shiyam Sundar, Lalith, additional

Published
2022
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39. Fully Automated, Semantic Segmentation of Whole-Body18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence

Author

Sundar, Lalith Kumar Shiyam, primary, Yu, Josef, additional, Muzik, Otto, additional, Kulterer, Oana C., additional, Fueger, Barbara, additional, Kifjak, Daria, additional, Nakuz, Thomas, additional, Shin, Hyung Min, additional, Sima, Annika Katharina, additional, Kitzmantl, Daniela, additional, Badawi, Ramsey D., additional, Nardo, Lorenzo, additional, Cherry, Simon R., additional, Spencer, Benjamin A., additional, Hacker, Marcus, additional, and Beyer, Thomas, additional

Published
2022
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40. Automated radiosynthesis of 1‐(2‐[18F]fluoroethyl)‐L‐tryptophan ([18F]FETrp) for positron emission tomography (PET) imaging of cancer in humans.

Author

Jiang, Huailei, Guo, Yan, Cai, Hancheng, Viola, Nerissa, Shields, Anthony Frank, Muzik, Otto, and Juhasz, Csaba

Subjects
POSITRON emission tomography, TRYPTOPHAN, DIOXYGENASES, DRUG approval, ENANTIOMERIC purity, HIGH performance liquid chromatography, INDOLEAMINE 2,3-dioxygenase
Abstract

The radiotracer 1‐(2‐[18F]fluoroethyl)‐L‐tryptophan (L‐[18F]FETrp or [18F]FETrp) is a substrate of indoleamine 2,3‐dioxygenase, the initial and key enzyme of the kynurenine pathway associated with tumoral immune resistance. In preclinical positron emission tomography studies, [18F]FETrp is highly accumulated in a wide range of primary and metastatic cancers, such as lung cancer, prostate cancer, and gliomas. However, the clinical translation of this radiotracer into the first‐in‐human trial has not been reported, partially due to its racemization during radiofluorination which renders the purification of the final product challenging. However, efficient purification is essential for human studies in order to assure radiochemical and enantiomeric purity. In this work, we report a fully automated radiosynthesis of [18F]FETrp on a Synthra RNPlus research module, including a one‐pot two steps radiosynthesis, dual independent chiral and reverse‐phase semipreparative high‐performance liquid chromatography purifications, and solid‐phase extraction‐assisted formulation. The presented approach has led to its Investigational New Drug application and approval that allows the testing of this tracer in humans. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Promise of Fully Integrated PET/MRI: Noninvasive Clinical Quantification of Cerebral Glucose Metabolism

Author

Shiyam Sundar, Lalith Kumar, Muzik, Otto, Rischka, Lucas, Hahn, Andreas, Lanzenberger, Rupert, Hienert, Marius, Klebermass, Eva-Maria, Bauer, Martin, Rausch, Ivo, Pataraia, Ekaterina, Traub-Weidinger, Tatjana, and Beyer, Thomas

Subjects
Physics and Instrumentation, Adult, Male, fully automatic pipeline, absolute quantification, Brain, brain imaging, image-derived input function, Magnetic Resonance Imaging, Multimodal Imaging, 18F-FDG, Clinical, PET/MRI, Glucose, Patlak analysis, Positron-Emission Tomography, Humans, Female
Abstract

We describe a fully automated processing pipeline to support the noninvasive absolute quantification of the cerebral metabolic rate for glucose (CMRGlc) in a clinical setting. This pipeline takes advantage of “anatometabolic” information associated with fully integrated PET/MRI. Methods: Ten healthy volunteers (5 men and /5 women; 27 ± 7 y old; 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain. The imaging protocol consisted of a 60-min PET list-mode acquisition with parallel MRI acquisitions, including 3-dimensional time-of-flight MR angiography, MRI navigators, and a T1-weighted MRI scan. State-of-the-art MRI-based attenuation correction was derived from T1-weighted MRI (pseudo-CT [pCT]). For validation purposes, a low-dose CT scan was also performed. Arterial blood samples were collected as the reference standard (arterial input function [AIF]). The developed pipeline allows the derivation of an image-derived input function (IDIF), which is subsequently used to create CMRGlc maps by means of a Patlak analysis. The pipeline also includes motion correction using the MRI navigator sequence as well as a novel partial-volume correction that accounts for background heterogeneity. Finally, CMRGlc maps are used to generate a normative database to facilitate the detection of metabolic abnormalities in future patient scans. To assess the performance of the developed pipeline, IDIFs extracted by both CT-based attenuation correction (CT-IDIF) and MRI-based attenuation correction (pCT-IDIF) were compared with the reference standard (AIF) using the absolute percentage difference between the areas under the curves as well as the absolute percentage difference in regional CMRGlc values. Results: The absolute percentage differences between the areas under the curves for CT-IDIF and pCT-IDIF were determined to be 1.4% ± 1.0% and 3.4% ± 2.6%, respectively. The absolute percentage difference in regional CMRGlc values based on CT-IDIF and pCT-IDIF differed by less than 6% from the reference values obtained from the AIF. Conclusion: By taking advantage of the capabilities of fully integrated PET/MRI, we developed a fully automated computational pipeline that allows the noninvasive determination of regional CMRGlc values in a clinical setting. This methodology might facilitate the proliferation of fully quantitative imaging into the clinical arena and, as a result, might contribute to improved diagnostic efficacy.

Published
2020

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