Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets

Abstract

The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.

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Correspondence to Romane Gauriau.

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Appendix A

Appendix A

The original list of DICOM attributes was the following:

Image Type, Samples Per Pixel, Photometric Interpretation, Bits Allocated, Bits Stored, High Bit, Scanning Sequence, Sequence Variant, Scan Options, MR Acquisition Type, Repetition Time, Echo Time, Echo Train Length, Inversion Time, Trigger Time, Sequence Name, Angio Flag, Number Of Averages, Imaging Frequency, Imaged Nucleus, Echo Number, Magnetic Field Strength, Spacing Between Slices, Number Of Phase Encoding Steps, Percent Sampling, Percent Phase Field Of View, Pixel Bandwidth, Nominal Interval, Beat Refection Flag, Low RR Value, High RR Value, Intervals Acquired, Intervals Rejected, PVC Rejection, Skip Beats, Heart Rate, Cardiac Number Of Images, Trigger Window, Rate, Reconstruction Diameter, Receive Coil Name, Transmit Coil Name, Acquisition Matrix, In Plane Phase Encoding Direction, Flip Angle, SAR, Variable Flip Angle Flag, DB-Dt, Temporal Position Identifier, Number Of Temporal Positions,Temporal Resolution, Pulse Sequence Name, MR Acquisition Type, Echo Pulse Sequence, Multiple Sin Echo, Multiplanar Excitation, Phase Contrast, Time Of Flight Contrast, Arterial Spin Labeling Contrast, Steady State Pulse Sequence, Echo Planar Pulse Sequence, Saturation Recovery, Spectrally Selected Suppression, Oversampling Phase, Geometry Of K Sapce Traversal, Rectilinear Phase Encode Reordering, Segmented K Space Traversal, Coverage Of K Space, Number Of K Space Trajectories, Pixel Spacing, Slice Thickness, Images In Acquisition, Contrast Bolus Agent.

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Gauriau, R., Bridge, C., Chen, L. et al. Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets. J Digit Imaging 33, 747–762 (2020). https://doi.org/10.1007/s10278-019-00308-x

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Keywords

  • Series categorization
  • DICOM
  • Machine learning
  • Workflow
  • Automation