Healthcare Diagnostic Imaging Metadata

This dataset provides comprehensive, structured metadata for simulated diagnostic medical images, including technical, clinical, and workflow attributes. It supports machine learning model development for image processing, anomaly detection, and workflow automation, with detailed fields for modality, patient, study, annotation, and device information. The dataset is ideal for benchmarking, model training, and workflow optimization in medical imaging.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Training and benchmarking machine learning models for medical image analysis

Sample rows

preview · 8 of 201 rows · all 24 columns
image_idstringmodalitystringpixel_spacing_xfloatcontrast_usedbooleanbody_part_examinedstringstudy_idstringpatient_idstringimage_datedateimage_timestringinstitution_namestringmanufacturerstringdevice_modelstringimage_widthintegerimage_heightintegerpixel_spacing_yfloatslice_thicknessfloatimage_orientationstringanomaly_presentbooleananomaly_typestringannotation_statusstringannotator_idstringworkflow_statusstringimage_file_pathstringnotesstring
IMGctS9X_001CT0.7trueChestSTUDY1001PAT10012022-03-1509:42:15Metro General HospitalSiemensSomatom Force204820480.70.5AxialtrueTumorAnnotatedRAD101In Progress/images/ct/chest/IMGctS9X_001.dcmLarge upper lobe mass, possible neoplasm. Further workup recommended.
IMGctS9X_002CT0.8trueChestSTUDY1001PAT10012022-03-1509:45:20Metro General HospitalSiemensDefinition Edge204820480.80.6AxialfalseblankUnreviewedblankPending/images/ct/chest/IMGctS9X_002.dcmRoutine chest CT, no acute abnormality detected.
IMGctS9X_003CT0.6falseAbdomenSTUDY1002PAT10022022-03-1611:00:01Metro General HospitalCanonAquilion Prime SP409620480.60.3CoronalfalseblankUnreviewedblankPending/images/ct/abdomen/IMGctS9X_003.dcmNormal abdominal CT without contrast. Organs unremarkable.
IMGctS9X_004CT0.9falseSinusSTUDY1003PAT10032022-03-1615:30:45Metro General HospitalSiemensSomatom Force102410240.90.2AxialtrueSinusitisVerifiedRAD102Completed/images/ct/sinus/IMGctS9X_004.dcmEvidence of maxillary sinus opacification consistent with sinusitis.
IMGctS9X_005CT0.5trueAbdomenSTUDY1002PAT10022022-03-1611:01:22Metro General HospitalCanonAquilion Prime SP204840960.50.7CoronalfalseblankAnnotatedRAD103In Progress/images/ct/abdomen/IMGctS9X_005.dcmContrast-enhanced CT abdomen. No evidence of mass or abnormality.
MRIb1K_001MRI1trueBrainSTUDY1004PAT10042022-04-1008:22:35NeuroScan CenterGE HealthcareSigna Pioneer51251215CoronaltrueLesionAnnotatedRAD104Completed/mri/brain/MRIb1K_001.dcmRight frontal lobe lesion detected. Recommend further evaluation.
MRIb1K_002MRI1.1falseBrainSTUDY1004PAT10042022-04-1008:26:00NeuroScan CenterGE HealthcareSigna Pioneer51210241.16SagittalfalseblankUnreviewedblankIn Progress/mri/brain/MRIb1K_002.dcmMRI brain without contrast. No acute abnormality.
MRIb1K_003MRI1.2falseSpineSTUDY1005PAT10052022-04-1113:14:44Cardiology Advanced CenterPhilipsIngenia 1.5T10245121.24.5ObliquefalseblankVerifiedRAD105Flagged/mri/spine/MRIb1K_003.dcmMRI spine unremarkable. No disc herniation or abnormal enhancement.

What the 201 rows show

from the 201-row sample

PET modality stands out: mean pixel_spacing_x is 3.5, against 0.64 for the rest.

  • 34%contrast_used = true
  • 0.77median pixel_spacing_x
  • 3annotation statuses
  • 4workflow statuses
  • 5image orientations
  • 10institution names
Mean pixel_spacing_x by modality200 rows
0240.69CT35 ro…1.1MRI34 ro…0.70X-ray29 ro…0.21Ultr…27 ro…3.5PET30 ro…0.18Mamm…18 ro…0.67Other27 ro…
pixel_spacing_x200 rows, in bands of 0.5
03570654954235755502.55pixel_spacing_x →

Median 0.77, from 0.0 to 5.0.

body_part_examined200 rows with a value · 1 left blank
  1. Abdomen20
  2. Brain19
  3. Breast18
  4. Liver14
  5. Spine13
  6. Chest12
  7. Sinus11
  8. Hand11
  9. Heart10
  10. Foot10
24 columns by typefrom the column list below
  • string 16
  • integer 2
  • float 3
  • date 1
  • boolean 2

Columns

24 columns in four groups
blueprint · 24 columns
columntypedescriptionexample
Text 16 columns
image_idstringUnique identifier for each diagnostic image.uniqueIMGctS9X_001
study_idstringUnique identifier for the imaging study to which this image belongs.STUDY1001
patient_idstringAnonymized unique identifier for the patient associated with the image.PAT1001
modalitystringType of imaging modality used (e.g., CT, MRI, X-ray, Ultrasound).7 valuesCT
body_part_examinedstringBody part or region examined in the image (e.g., Chest, Abdomen, Brain).Chest
image_timestringTime when the image was acquired (24-hour HH:MM:SS format).optional09:42:15
institution_namestringName of the institution or facility where the image was acquired.10 names · optionalMetro General Hospital
manufacturerstringName of the equipment manufacturer.10 manufacturers · optionalSiemens
device_modelstringModel of the imaging device used.optionalSomatom Force
image_orientationstringOrientation of the image (e.g., Axial, Sagittal, Coronal, Oblique).Axial · Sagittal · Coronal · Oblique · Unknown · optionalAxial
anomaly_typestringType of anomaly detected or annotated (e.g., Tumor, Fracture, Lesion); blank if none.optionalTumor
annotation_statusstringStatus of expert annotation (e.g., Unreviewed, Annotated, Verified).Unreviewed · Annotated · Verified · optionalAnnotated
annotator_idstringAnonymized identifier for the radiologist or expert who annotated the image.optionalRAD101
workflow_statusstringCurrent workflow status for the image (e.g., Pending, In Progress, Completed, Flagged).Pending · In Progress · Completed · FlaggedIn Progress
image_file_pathstringRelative or absolute file path or URI to the image file./xray/hand/XRyT5_001.dcm
notesstringFree-text notes or comments related to the image or annotation.optionalLarge upper lobe mass, po…
Numbers 5 columns
image_widthintegerWidth of the image in pixels.1 or more2048
image_heightintegerHeight of the image in pixels.1 or more2048
pixel_spacing_xfloatPhysical distance in millimeters between the centers of adjacent pixels along the x-axis.0 or more · optional0.7
pixel_spacing_yfloatPhysical distance in millimeters between the centers of adjacent pixels along the y-axis.0 or more · optional0.7
slice_thicknessfloatThickness of the image slice in millimeters (for 3D modalities).0 or more · optional0.5
Dates and times 1 column
image_datedateDate when the image was acquired.2022-03-15
True or false 2 columns
contrast_usedbooleanIndicates whether contrast agent was used for the image.optionaltrue
anomaly_presentbooleanIndicates if an anomaly is present in the image (as per ground truth or annotation).optionaltrue

Use it for

  • contrast used34%68 of 200 rowsmean pixel spacing x …0.69CT1.1MRI0.70X-ray0.21Ultr…

    A healthcare dashboard

    The contrast_used rate, pixel_spacing_x by modality and a breakdown of body_part_examined. Excel, Power BI or Tableau.

  • Why do the 30 PET rows have a mean pixel_spacing_x of 3.5?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable images with study_id, patient_id and modality to fill a screen in front of a buyer.

Not quite right?

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This dataset201 rows24 columns
Yours1,000 rows24 columns

blueprint · healthcare-diagnostic-imaging-metadata

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 24 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Image types include MRI, CT, X-ray
  • Patient age/gender influences exam type
  • Abnormalities labeled per imaging protocol
  • Acquisition device metadata standardized
  • Report turnaround time tracked
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
healthcare-diagnostic-imaging-metadata

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