MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

Authors

  • Yu H,

    1. Sunnybrook Health Sciences Centre, Toronto, Ontario
    2. Sunnybrook Odette Cancer Centre, Toronto, Ontario
    3. Odette Cancer Centre, Toronto, ON
    4. Sunnybrook Odette Cancer Center, Toronto, Ontario
    5. University of Toronto, Toronto, ON
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  • Lee Y,

    1. Sunnybrook Health Sciences Centre, Toronto, Ontario
    2. Sunnybrook Odette Cancer Centre, Toronto, Ontario
    3. Odette Cancer Centre, Toronto, ON
    4. Sunnybrook Odette Cancer Center, Toronto, Ontario
    5. University of Toronto, Toronto, ON
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  • Ruschin M,

    1. Sunnybrook Health Sciences Centre, Toronto, Ontario
    2. Sunnybrook Odette Cancer Centre, Toronto, Ontario
    3. Odette Cancer Centre, Toronto, ON
    4. Sunnybrook Odette Cancer Center, Toronto, Ontario
    5. University of Toronto, Toronto, ON
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  • Karam I,

    1. Sunnybrook Health Sciences Centre, Toronto, Ontario
    2. Sunnybrook Odette Cancer Centre, Toronto, Ontario
    3. Odette Cancer Centre, Toronto, ON
    4. Sunnybrook Odette Cancer Center, Toronto, Ontario
    5. University of Toronto, Toronto, ON
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  • Sahgal A

    1. Sunnybrook Health Sciences Centre, Toronto, Ontario
    2. Sunnybrook Odette Cancer Centre, Toronto, Ontario
    3. Odette Cancer Centre, Toronto, ON
    4. Sunnybrook Odette Cancer Center, Toronto, Ontario
    5. University of Toronto, Toronto, ON
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Abstract

Purpose:

Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours.

Methods:

20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution. Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated.

Results:

The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively.

Conclusion:

The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.

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