Urgent (Thesis project): Thresholding Impossible due to High Noise Levels in CT Reconstruction

To Whom It May Concern,
I am experiencing a critical technical issue with my CT scan data that is preventing me from proceeding with my thesis analysis.
The primary problem is that the Signal-to-Noise Ratio (SNR) is so low that I cannot perform a reliable thresholding/segmentation process. The bone tissue is indistinguishable from the background noise and artifacts.
Key issues I am facing:

  • Threshold Inconsistency: It is impossible to select a Hounsfield Unit (HU) or grayscale range that captures the bone structure without including massive amounts of noise.
  • Loss of Connectivity: When I attempt to isolate the bone, the structural integrity breaks down, resulting in a “fragmented” or “moth-eaten” appearance that does not reflect the actual anatomy.
  • Boundary Blur: The interface between the cortical bone and surrounding tissue is too blurred for manual or automatic segmentation tools to function correctly.
    Since this data is vital for my thesis and I am working against a strict deadline, I urgently need your guidance on:
  • Which reconstruction filters or denoising algorithms should be applied to the raw data to clean up this noise?
  • Are there specific artifact reduction settings you recommend to stabilize the image for thresholding?
    I have attached a representative slice and dicom showing the severity of the noise. I look forward to your immediate technical feedback.
    Best regards,

Dıcom

You could try https://www.openrtk.org/ for reconstruction, I have never used it myself. Maybe also learn if there are some new AI reconstruction methods published

If you have control of the CT scanner you could increase the radiation and the scanning-time to have better images

Use the nninteractive. There is an extension. Expecting an ack in your thesis….