PET SUV factor and rescale slope

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1

I am working on PET images and I’d like to convert DICOM PET images into SUV factorised images on the data that took into account rescale slope and intercept. I can’t find any sure evidence that the SUV calculated with PETDICOMExtension uses the data AFTER the rescale slope and intercept calculation. Except a mention here which is not obvious enough to me :

Other than that, the extension does the job for me for one or few images. If I want to convert few hundreds of them, how do you recommend to proceed? I can’t seem to find a proper python library for this.

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See how to run CLI modules from Python here: https://www.slicer.org/wiki/Documentation/Nightly/Developers/Python_scripting#Running_a_CLI_from_Python

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That extension is using ITK reader to read the DICOM dataset, and then applies SUV correction to the voxel intensities. ITK reader will apply rescale/slope, so you won’t see it in the code of the extension. That’s the way I think it works. @chribaue can you comment?

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@fedorov. You are right, the standard ITK readers already apply rescale slope and intercept.

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Thank you, I am glad to have this confirmed.

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I’ve looked into using CLI from Python, but it does not seem to be straightforward. I am going to try Nipet for this purpose.
Thank you for the answer!

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The CLI does not seem straightforward to me, so I’ve recoded my Python SUV factor calculation according to this pseudo-code https://qibawiki.rsna.org/index.php/Standardized_Uptake_Value_(SUV). Well, for some examples it gives just the same results as I had before with a simpler DICOM tags verification.

My trouble is that my suv image is not corresponding to the one with PETDICOM extension in Slicer. For the reference, I am using DCM2Niix to convert my dicoms to Nifti or to Analyze, Nibabel to extract the data. Does anyone have an idea what might be wrong?

Also, I can’t jsut extract the SUV factor for the whole image? Otherwise I have to measure the statistics from the image to compare the results.

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@pocolya I also intend to programmatically calculate the SUV_bw values of DICOM PET images. My efforts so far are not successful. Could you kindly share with me your progress? Thank you!

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@pocolya can you tell us more precisely - what does not seem straightforward to you about the Slicer SUV calculator CLI? Is it about using CLI from command line, running it programmatically, specifying arguments, interpreting output, understanding documentation?

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@fedorov Could you kindly show me an example on programmatically (i) load a few PET DICOM series, (ii) call the SUV calculator CLI to compute them in the batch mode (as mentioned in http://qiicr.org/tool/PETDICOM/), and (iii) save them as NIFTI files?

Thank you very much!

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Do you want to do it in a Slicer module, or in another batch script where you want to minimize dependencies on Slicer?

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Thank you for your replies.

I would like to do it efficiently for many patients.

CLI didn’t seem straightforward to me as I would need to create an environment as in Slicer, considering I already had a python code, I preferred not to invest my time right now in making it work this way. However, I might need using PETSUV conversion module in future. I will check it next week, but it seemed like it does not give the same SUV values as my code written according to the above cited link.

One thing I noticed when applying a mask on a SUV image, and then converting it label image is that I would loose the original image dimension. I found how to get over it (by exporting from the Segmentation module), however I just notice that there are so many not straightforward things with Slicer. Like, when editing a segmentation (in segmentation editor), for some reason, some parts are not accessible for painting, even though I checked using all the visible parts and so on. Any idea of how to overcome it?

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If you want my Python SUV code, write me a private message with your email.

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Based on your question, I think you want to do it outside of Slicer. If this is indeed the case, here are the steps, which will not depend on Slicer environment or any Slicer-specific python functionality.

Step 1: Sort your DICOM files into series, and identify PET series. I recommend you use dicomsort for this task. You can run it, for example, with the following parameters to include modality in the name of the sorted series folder:

$ python dicomsort.py <input folder>  <sorted_folder>/%PatientID/%StudyDate/%SeriesNumber-%Modality-%SeriesDescription/%SOPInstanceUID.dcm

Step 2: Run SUV calculator command line tool. You can install it as an extension, and then navigate to the folder with the installed extensions to locate it. Instructions how to run a Slicer CLI will vary depending on the operating system you use. On Mac, you can run the CLI executable directly. CLI will take as input directory with the DICOM PET files, and will output a DICOM Real World Value Mapping IOD instance (the macro that contains the SUV factor is described here) saved into a new file and a text file with the computed SUV factor(s):

$ /Applications/Slicer.app/Contents/Extensions-28390/PETDICOMExtension/lib/Slicer-4.11/cli-modules/SUVFactorCalculator -p sorted/QIN-HEADNECK-01-0366/19860303/UnknownSeriesNumber-PT-PET_WB_0 -r . --returnparameterfile output_parameters.txt
saving numbers to
Input parameters okay...
ATTN/DECAY correction detected.
Decay correction START detected.
                  INJECTED DOSE: 3.8591e+08
                  RAD. START TIME: 39180
                  SERIES TIME: 44254.7
                  DECAYED DOSE: 226226
Warning: No patient height detected.  Cannot determine SUVbsa, SUVlbm, and SUVibw conversion factors.
227 files total
Inserted: QIN-HEADNECK-01-0366
Inserted: QIN-HEADNECK-01-0366
Inserted: M
Inserted: YES
Inserted: DCM:113100/113105/113107/113108/113109/113111
Inserted: 1.3.6.1.4.1.14519.5.2.1.2744.7002.103466790444463274538008985123
Inserted: 19860303
Inserted: 120336.292000
Inserted:
Inserted: 2076699673350889
Inserted: Thorax^1HEAD_NECK_PETCT
Inserted: 059Y
Inserted: 63.2
saving to ./1.2.276.0.7230010.3.1.4.0.86172.1563548276.147671.dcm
0 0.000279366 0 0

The last line of the console will actually output various SUV factors calculated by the CLI (and I should add more descriptive printout on the console).

The output_parameters.txt file will contain the following lines with the SUV factor(s):

SUVbwConversionFactor = 0.000279366
SUVlbmConversionFactor = 0
SUVbsaConversionFactor = 0
SUVibwConversionFactor = 0

The relevant part of the DICOM RWVM instance created by the CLI are below (output produced by the DCMTK dcmdump tool, also available via the dicom-dump package of Atom):

    (0040,9096) SQ (Sequence with undefined length #=1)     # u/l, 1 RealWorldValueMappingSequence
      (fffe,e000) na (Item with undefined length #=8)         # u/l, 1 Item
        (0028,3003) LO [Standardized Uptake Value body weight]  #  38, 1 LUTExplanation
        (0040,08ea) SQ (Sequence with undefined length #=1)     # u/l, 1 MeasurementUnitsCodeSequence
          (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
            (0008,0100) SH [{SUVbw}g/ml]                            #  12, 1 CodeValue
            (0008,0102) SH [UCUM]                                   #   4, 1 CodingSchemeDesignator
            (0008,0104) LO [Standardized Uptake Value body weight]  #  38, 1 CodeMeaning
          (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
        (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
        (0040,9210) SH [{SUVbw}g/ml]                            #  12, 1 LUTLabel
        (0040,9211) US 26403                                    #   2, 1 RealWorldValueLastValueMapped
        (0040,9216) SS 0                                        #   2, 1 RealWorldValueFirstValueMapped
        (0040,9220) SQ (Sequence with undefined length #=2)     # u/l, 1 QuantityDefinitionSequence
          (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
            (0040,a040) CS [CODE]                                   #   4, 1 ValueType
            (0040,a043) SQ (Sequence with undefined length #=1)     # u/l, 1 ConceptNameCodeSequence
              (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
                (0008,0100) SH [G-C1C6]                                 #   6, 1 CodeValue
                (0008,0102) SH [SRT]                                    #   4, 1 CodingSchemeDesignator
                (0008,0104) LO [Quantity]                               #   8, 1 CodeMeaning
              (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
            (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
            (0040,a168) SQ (Sequence with undefined length #=1)     # u/l, 1 ConceptCodeSequence
              (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
                (0008,0100) SH [126400]                                 #   6, 1 CodeValue
                (0008,0102) SH [DCM]                                    #   4, 1 CodingSchemeDesignator
                (0008,0104) LO [Standardized Uptake Value]              #  26, 1 CodeMeaning
              (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
            (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
          (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
          (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
            (0040,a040) CS [CODE]                                   #   4, 1 ValueType
            (0040,a043) SQ (Sequence with undefined length #=1)     # u/l, 1 ConceptNameCodeSequence
              (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
                (0008,0100) SH [G-C036]                                 #   6, 1 CodeValue
                (0008,0102) SH [SRT]                                    #   4, 1 CodingSchemeDesignator
                (0008,0104) LO [Measurement Method]                     #  18, 1 CodeMeaning
              (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
            (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
            (0040,a168) SQ (Sequence with undefined length #=1)     # u/l, 1 ConceptCodeSequence
              (fffe,e000) na (Item with undefined length #=3)         # u/l, 1 Item
                (0008,0100) SH [126410]                                 #   6, 1 CodeValue
                (0008,0102) SH [DCM]                                    #   4, 1 CodingSchemeDesignator
                (0008,0104) LO [SUV body weight calculation method]     #  34, 1 CodeMeaning
              (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
            (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
          (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
        (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem
        (0040,9224) FD 0                                        #   8, 1 RealWorldValueIntercept
        (0040,9225) FD 0.000279366                              #   8, 1 RealWorldValueSlope
      (fffe,e00d) na (ItemDelimitationItem)                   #   0, 0 ItemDelimitationItem
    (fffe,e0dd) na (SequenceDelimitationItem)               #   0, 0 SequenceDelimitationItem

The SUV factor is stored in the RealWorldValueSlope attribute, and the preceeding QuantityDefinitionSequence can be used to get the SUV calculation method and units. Other parts of the DICOM RWVM instance store patient information, references to the PET instances to which the SUV factor applies, etc.

Over-simplifying things, you can easily access the SUV factor (the same that you will find in the text file, but with a lot more detailed metadata) using pydicom as follows:

import pydicom
d = pydicom.read_file('<RWVM file name>')
suv = d.ReferencedImageRealWorldValueMappingSequence[0].RealWorldValueMappingSequence[0].RealWorldValueSlope

Step 3: you can use dcm2niix to generate a 3d PET volume from the files in the PET series folder you prepared in Step 1 by running dcm2niix . in the folder with the PET series files.

Step 4: you can load the reconstructed PET volume using itk-python or SimpleITK and apply the SUV factor by multiplying the image by the SUV constant. You can then save the resulting image as a new NIfTI file - see examples on file IO for SimpleITK here.

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Hi Andrey,

I want to it in a batch script with minimal Slicer dependencies. Really appeciate your step-by-step guidance and recommendations in your latest reply. Thank you very much!

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