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.

Radiology Image Processing Lab (RIPL)

  • Co-Registration
  • Segmentation
  • Region, Atlas and
  • Voxel-Based Analysis
  • Parametric mapping
  • (T1, T2, ADC, PET)
  • Volumetry and Morphometry
  • DCE MRI Analysis
  • Radiomic feature extraction
  • 3D Printing
  • 6 Performance Workstations with High-Resolution Displays
  • Commercial Software:
    • AMIRA, PMOD, Osirix MD, MIM, Vivoquant, MATLAB, IDL
  • Open-Source Software:
    • SPM, FSL, 3D Slicer, MIPAV, AFNI, Segment, FreeSurfer, PyRadiomics
  • 3D Printing
    • Printers: Markerbot Z18, Makerbot Replicator
    • Software: Mimics with Design Module
  • Data Management
    • ImageDrive
Image Processing $70.00 / hour
Facility Usage $15.00 / hour
3D printing price will depend
on the size of the print
($5.00 / hour x number of hours) + (Cost per gram of PLA $0.05 / gm x number of grams)

To request a quote:

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


RIPL Image Analysis Publications

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.