{"id":5343,"date":"2025-05-20T12:52:49","date_gmt":"2025-05-20T16:52:49","guid":{"rendered":"https:\/\/engineering.jhu.edu\/doctor-of-engineering\/faculty\/alejandro-sisniega-crespo\/"},"modified":"2026-02-09T23:27:51","modified_gmt":"2026-02-10T04:27:51","slug":"alejandro-sisniega-crespo","status":"publish","type":"people","link":"https:\/\/engineering.jhu.edu\/doctor-of-engineering\/faculty\/alejandro-sisniega-crespo\/","title":{"rendered":"Alejandro Sisniega-Crespo"},"content":{"rendered":"<p>Alejandro Sisniega-Crespo is an adjunct research engineer in the Department of Biomedical Engineering, working in the Imaging for Surgery, Therapy, and Radiology Laboratory (I-STAR).<\/p>\n<p>His research background builds on the design of multimodality high-resolution imaging systems for small animal imaging (PET-CT, SPECT-CT, and FDOT-CT), for which he played a significant role in the design, development, and optimization of the high-resolution Cone-Beam CT (CBCT) component of the imaging system. Sisniega-Crespo\u2019s expertise includes system design and characterization through analytical modeling and Monte Carlo simulation techniques using GPU-based parallelization, and algorithm design for CBCT image processing, artifact compensation, and 3D model-based image reconstruction.<\/p>\n<p>His current work is focused on the improvement of image quality in cone-beam CT through algorithmic approaches for:<\/p>\n<ol>\n<li>high-fidelity, high-speed, artifact correction,<\/li>\n<li>volumetric image reconstruction methods for mitigation of noise and sparse sampling effects, and<\/li>\n<li>acceleration of optimization methods to bring advanced model-based iterative reconstruction to runtimes acceptable for clinical practice.<\/li>\n<\/ol>\n<p>This work comprises comprehensive artifact correction methods in soft-tissue CBCT (with application to brain imaging in traumatic brain injury scenarios), including compensation of detector non-idealities, model-based beam-hardening correction, and high-fidelity, high-speed Monte Carlo scatter correction, leveraging variance reduction and GPU parallelization to achieve competitive runtime in clinical scenarios. His last research investigated approaches for purely image-based patient motion compensation in dedicated CBCT of the extremities (undergoing rigid motion) and in more challenging scenarios involving automatic compensation of deformable motion in soft-tissue CBCT.<\/p>\n<p>Sisniega-Crespo earned his PhD in Biomedical Engineering from Universidad Carlos III de Madrid in 2013.<\/p>\n","protected":false},"template":"","class_list":["post-5343","people","type-people","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Alejandro Sisniega-Crespo - Doctor of Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/faculty\/alejandro-sisniega-crespo\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Alejandro Sisniega-Crespo - Doctor of Engineering\" \/>\n<meta property=\"og:description\" content=\"Alejandro Sisniega-Crespo is an adjunct research engineer in the Department of Biomedical Engineering, working in the Imaging for Surgery, Therapy, and Radiology Laboratory (I-STAR). 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