MIDAS Processing of Segmentation Data for Brain Lesions

Transcription

1 MIDAS Processing of Segmentation Data for Brain Lesions A. A. Maudsley 4/6/2012 For studies that contain lesions, the MIDAS processing has two steps that benefit from a modified processing pipeline to account for differences due to the presence of the lesion. Firstly, the quality of the tissue segmentation will be degraded if the lesion is not accounted for, potentially resulting in miss-assignment of normal grey and white-matter tissues. Secondly, the metabolite signal normalization (SINORM) may be impacted by altered tissue water density and T1 in the lesion. To account for differences in water density and T1 the signal normalization should incorporate specific measurements of these parameters, which is done in the so-called TIMO measurement and processing. However, if this measurement is not available the SI_Ref based signal normalization can still be used, with corresponding limitations regarding the accuracy of the normalization, using the procedure described in this document. To account for the lesion the processing is modified to include an other tissue class that encompasses the lesion and surrounding edema. This requires manual creation of a mask at the MRI_T1 resolution that includes all abnormal tissue, i.e. tumor plus surrounding edema, which should be created before running the SI processing. This mask is used to define an other tissue class in the segmentation. The format of this mask is as an Analyze file, with 8-bit unsigned integer. There are 3 steps that differ from the standard processing pipeline: Step 1: Mask creation The following steps describe manual mask creation using MIPAV (mipav.cit.nih.gov), however, several other image analysis programs could be used for the same purpose. 1. Run the volumizer processing step in the usual manner for the MRI_T1 and MRI_T2 images. 2. Save the T1 MRi to an Analyze format file: Using the MIDAS Browser (started from any MIDAS program e.g. volumizer), navigate to the MRI_T1\Volume\process\data node and right click on data and select Save to Analyze. Save it under the MRI directory of the study you are processing, in a new subdirectory, e.g. named mask_data (any directory name and location can be used, but it s convenient to keep it under the Study). 3. Do the same for the MRI_T2: Browser MRI_T2/Volume/Data Save to Analyze. 4. Open MRI T1 and T2 images in MIPAV. These are different resolutions, but we need the result in the MRI_T1 resolution (usually 1 mm). There are two options for using the T2: 1

2 a. The T2 can remain in the original resolution and used simply as a visual guide, e.g. to identify regions of edema. However, the outline must be drawn on the T1. b. The T2 can be matched to the T1 resolution and the lesion outline drawn directly on the T2. Do by first selecting the T2 image, then select Algorithms Registration Optimized Image Registration. The widget should already show the T1 image as the one to match to. You can run with the default settings, which uses correlation ratio as the cost function, but mutual Information has been reported to work better for some image types. This will create a new image. You can optionally remove the original T2 and save the new one (Ctrl-S) if you wish. 5. Segment the tumor using any of the VOI or paint tools. Save Frequently! There are many options to do this type of processing, but this is what has generally been used: - Set the link to have T1 and T2 scroll together. - Zoom in and enlarge the images. - Use the Draw Polygon tool. It s not necessary for the contour to conform precisely to the edges of the tumor as the result will be converted to SI resolution and largely used to exclude this region. - With the contour selected, use the To next slice to propagate it to a neighboring slice. - The contours cannot be simultaneously displayed on the T1, but you can copy and paste them. However, avoid getting multiple overlapping contours. 6. If you used the paint tools, do a Paint VOI conversion. 7. Do VOI VOI Conversion VOI All to unsigned byte mask. (Note, the equivalent Paint option gets saved in integer, not byte format). 8. Check the new image and that it is all a single value. If you used multiple VOIs or paint regions multiple regions can be created. 9. Flip this image in the vertical axis. Utilities Reorientation change Y axis to Posterior to Anterior OK. Creates a new image. This is needed to make the orientation compatible with MIDAS. 10. Save the new image (Ctrl-shift-S) in the MRI directory as other.img. Other notes: Set the memory allocation in MIPAV to as much as you have available. Restart MIPAV each time you start new processing. It has problems with memory leaks. If you see error messages start appearing in the command window then save your data and restart. 2

3 Step 2: Segmentation After creating the lesion mask image the segmentation is done using 4 tissue classes. In batch mode the function call is: SEG_FSL_T1_4Tissue. In GUI mode you have to use FSL4.1 and set the Additional flag for FSL as --class=4". Step 3: The SINorm program The Fixed Values signal normalization method uses assumed values of the water density and T1 in each tissue and CSF, from which a bias-field correction image is derived. If a lesion is present in the image then the water density and/or T1 values for the other tissue region generally need to be manually adjusted. While both T1 and water density are included in the calculation, these have an equivalent effect and in practice it is found to be sufficient to just change the T1 value using the following procedure. In the SINORM program, load the data and proc file, then check the Display only. Then run the program, and an image display window will appear as shown below: 3

4 This display shows several slices of the water reference SI through the center of the head. In this example a lesion can be seen on the SI_Ref image shown on the top row. What the program aims to do is generate a simulated water reference image (2 nd row) that matches as closely as possible to the actual data. From this result an image is calculated that is equivalent to an object with 100% water content in every voxel (4 th row). This result is then smoothed and the slowly varying image features (5 th row) are taken to be the bias field, which is then corrected for in the metabolite image data. In this example shown above the T1 in the lesion has been underestimated, resulting in a clearly visible lesion in the difference image. The processing therefore needs to be run with different T1 values (or water density) for the other tissue, to minimize any structural features in the 100% H2O equivalent image. In the following example the T1 has been set too long: 4

5 On the following image the T1 is about right (1200 ms): Almost all lesions are heterogeneous and include regions of edema, so this procedure will rarely be correct for all regions; however, by applying heavy smoothing the impact of local differences is minimized. Nevertheless, this remains a limitation of this approach for these types of studies and the TIMO normalization method is preferred. Once a suitable T1 value has been found the Display only box should be unchecked and the processing rerun. 5