Browsing by Author "Zarei, Mojtaba"
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Item Open Access Comparison of image quality of abdominal CT examinations and virtual noncontrast images between photon-counting and energy-integrating detector CT(2023-11-26) Lofino, Ludovica; Schwartz, fides; Ria, francesco; Zarei, Mojtaba; Samei, Ehsan; Abadia, Andres; Marin, DanielePurpose: To compare image quality of portal venous phase (PVP) abdominal CT examinations and virtual non-contrast (VNC) images between photon-counting CT (PCCT) and energy-integratingDetector CT (EID). Methods and Materials: In this HIPAA compliant, IRB-approved, retrospective study, multi-phase CT scans from one commercially available PCCT (NAEOTOM Alpha, Siemens Healthineers) and two EID dual-source dual-energy CT systems (SOMATOM Definition Flash and SOMATOM Force, Siemens Healthineers) were retrieved. A total of 45 BMI-matched patients (21 women, 24 men; mean age58.5 ± 15.3 years, range 19-81 years; mean BMI 29.0 ± 6.8 kg/m2, range 13-47 kg/m2) were included: 15 for PCCT and 15 for each EID system. In vivo image quality parameters (MTFf10, noisemagnitude, Fav, Fpeak, NPSf10) were measured and compared between PCCT and EID for standard PVP and VNC images. A subset analysis was also performed in the overweight patient population(BMI>25 kg/m2). CTDIvol values were recorded for the three scanners. Because scanner tube current modulation adapts to patient size, radiation dose was compared among scanners accounting forBMI using a figure of merit: FOM=1/(BMI*lnCTDIvol). A five-point scale (1=best and 5=worst) was used to assess reader perception of noise, visibility of small structures, and overall image quality. Results: Compared to the two EID systems, PCCT yielded significantly improved resolution and noise magnitude for both PVP (MTFf10 = 0.55 ± 0.08 for PCCT vs. 0.50 ± 0.04 and 0.49 ± 0.03 for Flashand Force, P = 0.02; noise = 9.76 ± 3.10 vs. 15.35 ± 4.14 and 10.70 ± 1.34, P = 0.02) and VNC images (MTFf10 = 0.56 ± 0.01 for PCCT vs. 0.51 ± 0.05 and 0.51 ± 0.03 for Flash and Force, P = 0.02; noise =9.59 ± 2.77 vs. 13.90 ± 3.57 and 10.83 ± 2.83, P = 0.02). A similar statistically significant trend was confirmed in the smaller subset of overweight patients. Our FOM analysis suggests that, for equal radiation exposure levels and comparable patient size, PCCT yields 20% noise reduction compared to the two EID systems, with 18% reduction in overweight patients. Reader’s perceived image noise was significantly lower for PCCT compared to EID for both PVP (1.85 ± 0.88 vs. 2.60 ± 0.88 and 2.70 ± 0.80) and VNC images (1.95 ± 0.83 vs. 3.0 ± 0.97 and 2.90 ±0.85). Of note, overall image quality improved significantly for PCCT compared to EID (1.35 ± 0.67 vs. 2.60 ± 0.82 and 2.45 ± 0.69 for PVP and 1.50 ± 0.67 vs 2.85 ± 0.81 and 2.55 ± 0.60 for VNC). Conclusions: Compared to conventional EID systems, PCCT yields significantly lower radiation dose along with improved image quality on PVP and VNC images of abdominal CT examinations. Clinical Relevance/Application: PCCT has a lower radiation dose compared to EID CT, with better image quality parameters and lower noise magnitude.Item Open Access ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.(Translational psychiatry, 2020-03) Thompson, Paul M; Jahanshad, Neda; Ching, Christopher RK; Salminen, Lauren E; Thomopoulos, Sophia I; Bright, Joanna; Baune, Bernhard T; Bertolín, Sara; Bralten, Janita; Bruin, Willem B; Bülow, Robin; Chen, Jian; Chye, Yann; Dannlowski, Udo; de Kovel, Carolien GF; Donohoe, Gary; Eyler, Lisa T; Faraone, Stephen V; Favre, Pauline; Filippi, Courtney A; Frodl, Thomas; Garijo, Daniel; Gil, Yolanda; Grabe, Hans J; Grasby, Katrina L; Hajek, Tomas; Han, Laura KM; Hatton, Sean N; Hilbert, Kevin; Ho, Tiffany C; Holleran, Laurena; Homuth, Georg; Hosten, Norbert; Houenou, Josselin; Ivanov, Iliyan; Jia, Tianye; Kelly, Sinead; Klein, Marieke; Kwon, Jun Soo; Laansma, Max A; Leerssen, Jeanne; Lueken, Ulrike; Nunes, Abraham; Neill, Joseph O'; Opel, Nils; Piras, Fabrizio; Piras, Federica; Postema, Merel C; Pozzi, Elena; Shatokhina, Natalia; Soriano-Mas, Carles; Spalletta, Gianfranco; Sun, Daqiang; Teumer, Alexander; Tilot, Amanda K; Tozzi, Leonardo; van der Merwe, Celia; Van Someren, Eus JW; van Wingen, Guido A; Völzke, Henry; Walton, Esther; Wang, Lei; Winkler, Anderson M; Wittfeld, Katharina; Wright, Margaret J; Yun, Je-Yeon; Zhang, Guohao; Zhang-James, Yanli; Adhikari, Bhim M; Agartz, Ingrid; Aghajani, Moji; Aleman, André; Althoff, Robert R; Altmann, Andre; Andreassen, Ole A; Baron, David A; Bartnik-Olson, Brenda L; Marie Bas-Hoogendam, Janna; Baskin-Sommers, Arielle R; Bearden, Carrie E; Berner, Laura A; Boedhoe, Premika SW; Brouwer, Rachel M; Buitelaar, Jan K; Caeyenberghs, Karen; Cecil, Charlotte AM; Cohen, Ronald A; Cole, James H; Conrod, Patricia J; De Brito, Stephane A; de Zwarte, Sonja MC; Dennis, Emily L; Desrivieres, Sylvane; Dima, Danai; Ehrlich, Stefan; Esopenko, Carrie; Fairchild, Graeme; Fisher, Simon E; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Garavan, Hugh P; Glahn, David C; Groenewold, Nynke A; Gurholt, Tiril P; Gutman, Boris A; Hahn, Tim; Harding, Ian H; Hernaus, Dennis; Hibar, Derrek P; Hillary, Frank G; Hoogman, Martine; Hulshoff Pol, Hilleke E; Jalbrzikowski, Maria; Karkashadze, George A; Klapwijk, Eduard T; Knickmeyer, Rebecca C; Kochunov, Peter; Koerte, Inga K; Kong, Xiang-Zhen; Liew, Sook-Lei; Lin, Alexander P; Logue, Mark W; Luders, Eileen; Macciardi, Fabio; Mackey, Scott; Mayer, Andrew R; McDonald, Carrie R; McMahon, Agnes B; Medland, Sarah E; Modinos, Gemma; Morey, Rajendra A; Mueller, Sven C; Mukherjee, Pratik; Namazova-Baranova, Leyla; Nir, Talia M; Olsen, Alexander; Paschou, Peristera; Pine, Daniel S; Pizzagalli, Fabrizio; Rentería, Miguel E; Rohrer, Jonathan D; Sämann, Philipp G; Schmaal, Lianne; Schumann, Gunter; Shiroishi, Mark S; Sisodiya, Sanjay M; Smit, Dirk JA; Sønderby, Ida E; Stein, Dan J; Stein, Jason L; Tahmasian, Masoud; Tate, David F; Turner, Jessica A; van den Heuvel, Odile A; van der Wee, Nic JA; van der Werf, Ysbrand D; van Erp, Theo GM; van Haren, Neeltje EM; van Rooij, Daan; van Velzen, Laura S; Veer, Ilya M; Veltman, Dick J; Villalon-Reina, Julio E; Walter, Henrik; Whelan, Christopher D; Wilde, Elisabeth A; Zarei, Mojtaba; Zelman, Vladimir; ENIGMA ConsortiumThis review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.Item Open Access Evaluation and extension of in vivo detectability index to deep-learning and photon counting CT techniques(2022-12-01) Ria, Francesco; Jensen, Corey; Zarei, Mojtaba; Liu, Xinming; Schwartz, Fides; Abbey, Craig; Samei, EhsanItem Open Access Image Quality of Photon Counting and Energy Integrating Chest CT – Prospective Head-to-Head Comparison on Same Patients(European Journal of Radiology, 2023-07) Schwartz, Fides R; Ria, Francesco; McCabe, Cindy; Zarei, Mojtaba; Rajagopal, Jayasai; Molvin, Lior; Marin, Daniele; O'Sullivan-Murphy, Bryan; Kalisz, Kevin R; Tailor, Tina D; Washington, Lacey; Henry, Travis; Samei, EhsanItem Embargo Task-Targeted Pre- and Post-acquisition Methodologies for Optimal Conditioning and Interpretation of Medical Images Using Virtual Imaging Trial(2023) Zarei, MojtabaThe use of medical imaging plays a vital role in diagnosing and monitoring diseases, significantly enabling healthcare providers to provide better patient care. Medical images have much wider significance than just a single clinical evaluation; they are crucial components of developing data-driven algorithms and conducting studies that involve multiple healthcare centers. These algorithms and research studies depend heavily on medical image data consistency and accuracy, which ultimately impact treatment precision and diagnostic accuracy.
The ever-evolving landscape of medical imaging has led to diverse imaging systems and modalities over the past decades. While this diversity offers a wealth of options for generating medical images, it also poses challenges. Due to this diversity, determining the most suitable protocols and parameters for achieving optimal image quality for specific clinical tasks has become a complex task. Consequently, inconsistencies in protocol selection can result in variable quantification outcomes.
The variety of scanning systems and protocols for capturing patient images has made establishing a multidimensional correlation between protocol selection and image quality challenging. Consequently, finding the most appropriate protocols to maximize image quality for specific tasks has become a more complex endeavor. Furthermore, when dealing with previously acquired medical images, the available data is typically limited to the image itself and associated meta-data. Therefore, understanding the governing physics of the acquisition is vital for tailoring post-processing tools to preserve diagnostic information and maintain consistency in quantification.
This proposal sets out to develop patient-centric, data-driven approaches customized for specific diagnostic tasks. These methodologies aim to enhance the consistency and accuracy of medical images, guided by two key aims: - Optimizing the protocol selection: Developing and evaluating optimization-based methodologies for selecting protocols customized for distinct tasks. The emphasis here is on refining medical image acquisition protocols to attain the highest levels of consistency and quality. - Harmonizing the images: Design and validate Deep Neural Network (DNN) architectures focused on post-processing of the images to reduce quantification variability. These DNN-based solutions enhance quantification consistency while preserving essential diagnostic information and the clinical appearance of medical images for visual inspection and interpretation by radiologists.
However, clinical data scarcity poses significant challenges to algorithms designed for medical applications. The limited availability of data hampers the development of effective algorithms. The absence of precise ground truth information restricts medical evaluations to variability assessment, leaving biases and accuracy largely unexplored.
To address the latter problem, we utilized VIT. By leveraging VIT, one can access extensive datasets encompassing a wide range of variations while providing accurate ground truth data. This approach circumvents patient health and privacy concerns, as the data is virtual rather than derived from actual patients. Emphasis has been placed on the chest area, which harbors vital organs such as the heart and lungs, while targeting diseases like coronary stenosis, lung cancer, and chronic obstructive pulmonary disease (COPD), all of which are globally recognized as some of the most lethal diseases.
To optimize the selection guidelines for protocols, correlation models were calculated to capture the governing physics of the acquisitions, allowing us to investigate how changes in protocols may affect the selection of other parameters. Polynomial approximations have been introduced in place of the resource-intensive and computationally demanding algorithms used to assess measurement quality based on sampling. Subsequently, a framework for a constrained optimization problem was formulated to determine the optimal protocols that enhance relevant metrics within clinically relevant domains. The practicality of the results was demonstrated through evaluation in real clinical settings and comparing the optimal protocols with the suggested protocol via routine clinical practice guidelines.
The accurate ground truth images and associated renditions were employed to train DNNs for harmonization aims. Different DNNs were tailored to the specific domain specifications, modalities, and available information. To enhance the generalizability of the developed DNNs, supplementary information from the physics of the acquisition or regulatory loss stemming from the harmonization principle was introduced and implemented. The algorithms were tested on diverse clinical data for various tasks, enabling the evaluation of quantification and the measurement of feature deviations.
Item Open Access Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.(J Comput Assist Tomogr, 2024-04-15) Rajagopal, Jayasai R; Schwartz, Fides R; McCabe, Cindy; Farhadi, Faraz; Zarei, Mojtaba; Ria, Francesco; Abadi, Ehsan; Segars, Paul; Ramirez-Giraldo, Juan Carlos; Jones, Elizabeth C; Henry, Travis; Marin, Daniele; Samei, EhsanOBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.