Radiology Image Processing Laboratory (RIPL)

The Radiology Image Processing Laboratory (RIPL) at UNM was established with the goal of facilitating translational research through the provision of state-of-the-art image processing tools for the intramural community. The laboratory is focused on quantitative image analysis and 3D printing for both preclinical and clinical research applications.

To request a quote:

Please email request with details to RadiologyResearch@salud.unm.edu

RIPL Image Analysis Publications

Divani AA, Salazar P, Ikram HA, Taylor E, Wilson CM, Yang Y, Mahmoudi J, Seletska A, SantaCruz KS, Torbey MT, Liebler EJ, Bragina OA, Morton RA, Bragin DE. Non-invasive Vagus nerve stimulation improves brain lesion volume and neurobehavioral outcomes in a rat model of traumatic brain injury. J Neurotrauma. 2023 Jul;40(13-14):1481-1494. doi: 10.1089/neu.2022.0153.

Taylor EN, Wilson CM, Franco S, De May H, Medina LY, Yang Y, Flores EB, Bartee E, Selwyn RG, Serda RE. Monitoring Therapeutic Responses to Silicified Cancer Cell Immunotherapy Using PET/MRI in a Mouse Model of Disseminated Ovarian Cancer. Int J Mol Sci. 2022 Sep 10;23(18):10525. doi: 10.3390/ijms231810525.

Wilson CM, Selwyn RG, Elojeimy S. Comparison of PET/CT SUV metrics across different clinical software platforms. Clin Imaging. 2022 Sep:89:104-108. doi: 10.1016/j.clinimag.2022.06.005.

McIlwrath SL, Montera MA, Gott KM, Yang Y, Wilson CM, Selwyn R, Westlund KN. Manganese-enhanced MRI reveals changes within brain anxiety and aversion circuitry in rats with chronic neuropathic pain- and anxiety-like behaviors. Neuroimage. 2020 Sep 6;223:117343. doi: 10.1016/j.neuroimage.2020.117343.

Maphis NM, Peabody J, Crossey E, Jiang S, Jamaleddin Ahmad FA, Alvarez M, Mansoor SK, Yaney A, Yang Y, Sillerud LO, Wilson CM, Selwyn R, Brigman JL, Cannon JL, Peabody DS, Chackerian B, Bhaskar K. Qß Virus-like particle-based vaccine induces robust immunity and protects against tauopathy. NPJ Vaccines. 2019 Jun 3;4:26. doi: 10.1038/s41541-019-0118-4.

Brocato TA, Brown-Glaberman U, Wang Z, Selwyn RG, Wilson CM, Wyckoff EF, Lomo LC, Saline JL, Hooda-Nehra A, Pasqualini R, Arap W, Brinker CJ, Cristini V. Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies. JCI Insight. 2019 Mar 5;5(8):e126518. doi: 10.1172/jci.insight.126518.