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MedicalDataHandler, a research‐oriented graphical user interface for DICOM data management.

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Title: MedicalDataHandler, a research‐oriented graphical user interface for DICOM data management.
Authors: Maniscalco, Austen1,2 (AUTHOR), Park, Yang Kyun1,2 (AUTHOR), Godley, Andrew1,2 (AUTHOR), Lin, Mu‐Han1,2 (AUTHOR), Jiang, Steve1,2 (AUTHOR), Nguyen, Dan1,2 (AUTHOR) Dan.Nguyen@UTSouthwestern.edu
Source: Medical Physics. Jan2026, Vol. 53 Issue 1, p1-12. 12p.
Subjects: Dicom (Computer network protocol), Data management, Descriptor systems, Cancer radiotherapy, Deep learning, Graphical user interfaces, Electronic data processing
Abstract: Background: Processing DICOM datasets for research and education is challenging due to the format's complexity and frequent patient‐specific workflow exceptions. Proper handling demands substantial technical expertise and meticulous care to ensure data fidelity in downstream applications. Purpose: We developed MedicalDataHandler to streamline the reading, visualization, and processing of DICOM data. By consolidating essential tasks into a user‐friendly environment, it minimizes reliance on advanced coding skills and promotes reproducible data handling without custom scripting. Methods: Implemented in Python with the third‐party Dear PyGui toolkit, MedicalDataHandler organizes DICOM files by patient identifiers and groups each patient's radiation therapy (RT) images, structure sets, plans, and doses based on mutual unique identifiers (UIDs). A comprehensive table of patient data enables metadata inspection, data visualization, and data processing. The GUI supports interactive visualization in axial, coronal, and sagittal views, with intuitive scrolling, zooming, panning, and window width/level adjustments. Segmentation labels, colors, and data orientation can be modified on the fly, and hovering over a voxel reveals its image/dose values and relevant segmented structures. Multithreading and multiprocessing enable rapid data reading and conversion to the deep‐learning‐friendly Nearly Raw Raster Data (NRRD) format. Additional features include metadata inspection, voxel grid resampling, Hounsfield‐Unit‐to‐Relative‐Electron‐Density mapping, plan‐sum dose creation, and partial or bulk data saving options. Results: We validated MedicalDataHandler with an end‐to‐end testing approach. DICOM data from 61 radiotherapy patients were processed, and the resulting dataset was used to train a deep‐learning‐based dose prediction model. MedicalDataHandler streamlined the workflow by eliminating the need for complex, patient‐specific code and accelerating the preparation of a research‐ready dataset. Conclusion: MedicalDataHandler streamlines DICOM data management and accelerates preprocessing, serving as a valuable tool for researchers and trainees. Its intuitive interface, flexible editing, and rapid data conversion empower a broader audience to manage DICOM data efficiently and consistently in research and education settings. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Background: Processing DICOM datasets for research and education is challenging due to the format's complexity and frequent patient‐specific workflow exceptions. Proper handling demands substantial technical expertise and meticulous care to ensure data fidelity in downstream applications. Purpose: We developed MedicalDataHandler to streamline the reading, visualization, and processing of DICOM data. By consolidating essential tasks into a user‐friendly environment, it minimizes reliance on advanced coding skills and promotes reproducible data handling without custom scripting. Methods: Implemented in Python with the third‐party Dear PyGui toolkit, MedicalDataHandler organizes DICOM files by patient identifiers and groups each patient's radiation therapy (RT) images, structure sets, plans, and doses based on mutual unique identifiers (UIDs). A comprehensive table of patient data enables metadata inspection, data visualization, and data processing. The GUI supports interactive visualization in axial, coronal, and sagittal views, with intuitive scrolling, zooming, panning, and window width/level adjustments. Segmentation labels, colors, and data orientation can be modified on the fly, and hovering over a voxel reveals its image/dose values and relevant segmented structures. Multithreading and multiprocessing enable rapid data reading and conversion to the deep‐learning‐friendly Nearly Raw Raster Data (NRRD) format. Additional features include metadata inspection, voxel grid resampling, Hounsfield‐Unit‐to‐Relative‐Electron‐Density mapping, plan‐sum dose creation, and partial or bulk data saving options. Results: We validated MedicalDataHandler with an end‐to‐end testing approach. DICOM data from 61 radiotherapy patients were processed, and the resulting dataset was used to train a deep‐learning‐based dose prediction model. MedicalDataHandler streamlined the workflow by eliminating the need for complex, patient‐specific code and accelerating the preparation of a research‐ready dataset. Conclusion: MedicalDataHandler streamlines DICOM data management and accelerates preprocessing, serving as a valuable tool for researchers and trainees. Its intuitive interface, flexible editing, and rapid data conversion empower a broader audience to manage DICOM data efficiently and consistently in research and education settings. [ABSTRACT FROM AUTHOR]
ISSN:00942405
DOI:10.1002/mp.70240