Course Introduction

Medical Imaging Modalities

  • Each imaging modality provides distinct sets of information
  • In computational imaging, images are essentially arrays, although embedded in additional data structures
  • Research should be thoughtfully designed, taking into account the constraints and capabilities inherent in human capacities
  • We can expect the emergence of additional imaging modalities in the future

Working with MRI

  • Imaging MRIs commonly used for research can be anatomical, functional or diffusion
  • MRIs can be converted from DICOMs to NIfTIs
  • BIDS is a standard about organizing neuroimaging data
  • NIfTI images contain a header, which describes the contents, and the data
  • The position of the NIfTI data in space is determined by the affine matrix
  • NIfTI data is a multi-dimensional array of values
  • Functional MRIs and Diffusion MRIs require heavy (pre)processing
  • Functional MRIs have time dimension
  • Diffusion MRI has b-values and b-vectors
  • There are many various tractography methods, each with imperfections

Registration and Segmentation with SITK

  • Registration aligns images for data merging or temporal tracking, while segmentation identifies objects within images, which is critical for detailed analysis.
  • SITK simplifies segmentation, registration, and advanced analysis tasks using ITK algorithms and supporting several programming languages.
  • Images in SITK are defined by physical space, unlike array-based libraries, ensuring accurate spatial representation and metadata management.
  • SITK offers global and bounded domain transformations for spatial manipulation and efficient resampling techniques with various interpolation options.
  • Use SITK’s robust capabilities for registration and classical segmentation methods such as thresholding and region growth, ensuring efficient analysis of medical images.

Preparing Images for Machine Learning

  • Direct knowledge of specific data cannot be substituted
  • Statistical analysis is essential to detect and mitigate biases in patient distribution
  • Verify if derived data aligns with known clinical realities through statistical examination
  • Evaluate the validity and utility of data augmentation methods before applying them
  • Radiomics enables the use of mathematical image qualities as features
  • There are several accessible pipelines for volumetrics and radiomics
  • Data from different machines (or even the same machines with different settings) often requires harmonization, achievable through coding and the use of existing libraries

Anonymizing Medical Images

  • Certain metadata should almost always be removed from DICOM files before sharing
  • Sharing only image files such as JPEGs or NIfTI can mitigate risks associated with metadata
  • Imaging data alone, even without explicit metadata, can sometimes lead to patient identification
  • Automated tools are available to strip metadata from DICOMs, but manual verification is necessary due to inconsistencies in how fields are utilized.
  • Tools exist to deface images to further protect patient identity
  • Several Python libraries enable access to DICOM metadata

Generative AI in Medical Imaging

  • Generative programs can create synthetic data, potentially enhancing various algorithms
  • Generative AI models have inherent limitations
  • Running generative AI programs locally on your own server is safer than using programs that send prompts to external servers
  • Exercise caution when entering patient data into generative AI programs
  • Numerous policies exist to ensure the safe and ethical use of generative AI tools across institutions