As part of our efforts on task-specific imaging, we are developing techniques to optimize quantitative imaging methods using patient data. Such optimization can substantially accelerate the clinical translation of these methods but is complicated by the lack of ground truth. We are developing no-gold-standard evaluation techniques to address this issue. Our results demonstrate that these techniques, without access to any ground truth, are able to rank imaging methods based on how precisely they estimate the  true quantitative value. A video presentation that describes our latest innovation in this area is on this page.

References

  1. J. Liu, Z. LiuH. S. MoonJ. Mhlanga and A. K. Jha, “A no-gold-standard technique for objective evaluation of quantitative nuclear-medicine imaging methods in the presence of correlated noise”, J. Nucl. Med. May 2020, 61 (supplement 1) 523 (link)
  2. A. K. Jha, E. Mena, B. Caffo, S. Ashrafinia, A. Rahmim, E. C. Frey and R. Subramaniam, “Practical no-gold-standard framework to evaluate quantitative imaging methods: Application to lesion segmentation in PET”, J. Med. Imag. (Special Section on PET imaging), 4(1), 2017 (pdf) (link).
  3. A. K. Jha, B. Caffo, E. Frey, “A no-gold-standard technique for objective assessment of quantitative nuclear-medicine imaging methods”, Phys. Med. Biol., 61(7), 2780-2800, 2016 (pdf)
  4. A. K. Jha, N. Song, B. Caffo, E. C. Frey,  Objective evaluation of reconstruction methods for quantitative SPECT imaging in the absence of ground truth. Proc SPIE Int Soc Opt Eng. 2015 Apr 13;9416:94161K.
  5. A. K. Jha, M. A. Kupinski, J. J. Rodriguez, R. Stephen, A. Stopeck, “Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard”, Phys. Med. Biol., 57(13) 4425-46, 2012 (link)
  6. A. K. Jha, M. A. Kupinski, J. J. Rodriguez, R. M. Stephen, A. T. Stopeck. Evaluating segmentation algorithms for diffusion-weighted MR images: a task-based approach. Proc. SPIE Medical Imaging; San Diego, CA, USA. Feb 2010.pp. 76270L1–L8, Best Student Paper award (link)