Quantitative Radiology Solutions (QRS) has raised an additional $230,000 from Phase 1 Ventures to support 510(k) clearance of its Automatic Anatomy Recognition (AAR) software for radiation treatment planning. The company is leveraging an STTR grant from the National Cancer Institute to complete its product development and conduct a clinical evaluation at 4 academic medical centers. … Read More
Quantitative Radiology Solutions was awarded a Phase 2 STTR grant for “Automated object contouring methods and software for radiotherapy planning.” This two year grant will allow the company to continue development of its Automatic Anatomy Recognition (AAR) in radiation oncology and conduct a multi-center clinical evaluation. The research will be conducted in collaboration with… Read More
QRS was one of the five Digital Health Section technology companies awarded funding by Ben Franklin Technology Partners. Full article here.… Read More
QRS is proud to annouce Steve Owens has joined our team as Chief Technology Officer. Mr. Owens is an experienced leader of Innovation Centers focused on research, product development, software engineering, and IT operations in the pharmaceutical research and medical device industries. He has a record of accomplishment of leading and developing people and organizations transform… Read More
QRS presented STTR Phase I results in 2 posters at ASTRO 2017: “Auto-contouring for Radiation Therapy: Challenges in Standardizing Object Definitions, Ground Truth Delineations, Object Quality and Image Quality” and “A High-Performance Software System for Auto Contouring Anatomy in Adaptive Radiation Therapy.” ASTRO is the premier radiation oncology society in the world, with more than… Read More
QRS CEO Joe Camaratta presented at Life Sciences Future. Life Sciences Future is a conference bringing together Pennsylvania’s diverse life sciences industry – pharma, biotech, medical device and diagnostics, healthcare IT, contract research organizations, academic research institutions, and the investment community. Learn More… Read More
QRS CEO Joe Camaratta presented the company’s fund-raising opportunity at Mid-Atlantic Diamond Ventures Fall Forum. Mid-Atlantic Diamond Ventures (MADV) is an independent organization that assists emerging, technology-based companies in their effort to build sustainable businesses by better positioning their firms for funding while increasing value at an early and crucial stage. To date, 386 Mid-Atlantic companies have… Read More
QRS presented its first poster at the American Association of Physics on Aug. 2, 2017. The poster addressed evaluation of segmentation methods as a function of the quality of input images. Learn More… Read More
QRS was selected as one of the Top 30 Life Sciences Startups in the RESI Innovation Challenge. From a pool of approximately 100 applicants, the LSN Scientific Review Team has selected QRS among a diverse group of life science startups: three healthcare IT, three diagnostic, thirteen therapeutic, and eleven medical device companies. QRS will showcase in… Read More
QRS will be presenting its findings on “Evaluation of segmentation methods as a function of the quality of input images” at the American Association of Physicists in Medicine (AAPM) conference in Denver starting 7/30. Additionally, the company will “A high-performance software system for auto contouring head and neck anatomy in adaptive radiation therapy” and “Knowledge-based… Read More
QRS was awarded “Best on INVEST Pitch Perfect” in the Health IT category by MedCity News in an event in Chicago last week. The MedCity INVEST Pitch Perfect Contest hosted promising startup companies, offering innovative approaches or trying to solve some of the biggest problems in healthcare. Participating biopharma, diagnostics, medical device, and digital health startups… Read More
Joe Camaratta, the CEO of Quantitative Radiology Solutions, was awarded the PCI Ventures CEO of the Year Award for 2016. Under Joe’s leadership, the company has secured multiple commercialization grants from NIH and NSF as well as funding from Phase I Ventures to further develop its medical imaging software to aid in radiation therapy planning.… Read More
Quantitative Radiology Solutions has raised $213,000 from Phase 1 Ventures to accelerate commercialization of its Automatic Anatomy Recognition (AAR) software in radiation treatment planning. Phase 1 Ventures is an accelerator program at University City Science Center that provides early stage businesses with funding and expertise to support commercialization of innovative technologies in healthcare and other… Read More
Quantitative Radiology Solutions was awarded a Phase I STTR grant for “Automated object contouring methods and software for thoracic radiotherapy planning.” The grant will allow the company to continue development of its Automatic Anatomy Recognition (AAR) in radiation oncology. The research will be conducted in collaboration with University of Pennsylvania.… Read More
Quantitative Radiology Solutions was awarded a Phase 1 STTR grant for “Automated object contouring methods and software for head and neck radiotherapy planning.” The grant will allow the company to continue development of its Automatic Anatomy Recognition (AAR) in radiation oncology. The research will be conducted in collaboration with University of Pennsylvania.… Read More
Quantitative Radiology Solutions presented at IMPACT 2015 Capital Conference. The company was featured in the Early Stage track and developed contacts with potential investors and technology partners. For more information, click on the link.… Read More
Quantitative Radiology Solutions participated in Penn I-Corps 2015 Summer Startup Accelerator Program. The Penn I-Corps Site is an NSF (National Science Foundation) program designed to facilitate commercialization of university research. Quantitative Radiology Solutions explored applications of its technology in radiology, medical oncology, cardiology and surgical planning.… Read More
Udupa, J.K., Odhner, D., Zao, L., Tong, Y., Matsumoto, M.M.S., Ciesielski, K.C., Falcao, A.X., Vaideeswaran, P., Ciesielski, V., Saboury, B., Mohammadianrasanani, S., Sin, S., Arens, R., Torigian, D.A.: “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Medical Image Analysis, 18: 752-771, 2014.
Tong, Y., Udupa J.K., Torigian, D.A.: “Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space – a novel approach,” Medical Physics, 41(6): 063501-1 – 063501-11, 2014.
Matsumoto, M.S.M., Udupa J.K., Saboury, B., Torigian, D.A.: “Quantitative normal thoracic anatomy at CT,” Computerized Medical Imaging and Graphics, submitted.
Wang, H., Udupa, J.K., Odhner, D., Tong, Y., Zhao, L., Torigian, D.A.: “Automatic anatomy recognition in whole-body PET/CT images,” Medical Physics, submitted.
Kobashi, S. and Udupa, J.K.: Fuzzy object models for newborn brain MR image segmentation,Proceedings of SPIE 8672: 86720J-1-86720J-6, 2013.
Kobashi, S. and Udupa, J.K.: Fuzzy object model based fuzzy connectedness image segmentation of newborn brain MR images, 2012 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1422-1427, October 14-17, 2012, COEX, Seoul, Korea.
Matsumoto, M.M.S. and Udupa, J.K.: Optimal hierarchies for fuzzy object models, Proceedings of SPIE, 8671:86712C-1-86712C-7, 2013.
Rittner, L., Udupa, J.K., and Torigian, D.A.: Mutliple fuzzy object modeling improves sensitivity in automatic anatomy recognition, Proceedings of SPIE 9034: 90342U-1 – 90342U-7, 2014.
Sun, K., Udupa, J.K., Odhner, D., Tong, Y., Torigian, D.A.: Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration, Proceedings of SPIE 9036: 90361P-1 – 90361P-8, 2014.
Matsumoto, M.M.S., Beig, N., Udupa, J.K., Archer, S., Torigian, D.A.: Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models, Proceedings of SPIE 9035: 90350J-1 – 90350J-7, 2014.
Zhao, L., Udupa, J.K., Odhner, D., Tong, Y., Wang, H., Torigian, D.A.: Automatic anatomy recognition of sparse objects, Proceedings of SPIE 9413: 94133N-1 – 94133N-8, 2015.
Tong, Y., Udupa, J.K., Odhner, D., Sin, S., Arens, R.: Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS, Proceedings of SPIE 9414: 94140Z-1 – 94140Z-6, 2015.