Fast processing of digital imaging and communications in medicine (DICOM) metadata using multiseries DICOM format

Understanding Scanner Utilization With Real-Time DICOM Metadata Extraction

Understanding system performance metrics ensures better utilization of the radiology resources with more targeted interventions. The images produced by radiology scanners typically follow the DICOM (Digital Imaging and Communications in Medicine) standard format. The DICOM images consist of textual metadata that can be used to calculate key timing parameters, such as the exact study durations and scanner utilization. However, hospital networks lack the resources and capabilities to extract the metadata from the images quickly and automatically compute the scanner utilization properties. Thus, they resort to using data records from the Radiology Information Systems (RIS). However, data acquired from RIS are prone to human errors, rendering many derived key performance metrics inadequate and inaccurate. Hence, there is motivation to establish a real-time image transfer from the Picture Archiving and Communication Systems (PACS) to receive the DICOM images from the scanners to research clusters to conduct such metadata processing to evaluate scanner utilization metrics efficiently and quickly. This paper analyzes the scanners' utilization by developing a real-time monitoring framework that retrieves radiology images into a research cluster using the DICOM networking protocol and then extracts and processes the metadata from the images. Our proposed approach facilitates a better understanding of scanner utilization across a vast healthcare network by observing properties such as study duration, the interval between the encounters, and the series count of studies. Benchmarks against using the RIS data indicate that our proposed framework based on real-time PACS data estimates the scanner utilization more accurately. Furthermore, our framework has been running stable and performing its computation for more than two years on our extensive healthcare network in pseudo real-time.

Evaluation of the application value of a three-dimensional digital model of the knee in clinical practice

Objective

To compare the knee shape and bone parameters between knee prosthesis products from overseas companies and a Chinese patient’s knee, and to apply the anatomical basis for Chinese knee prosthesis design.

Methods

Three-dimensional digital models were built, including prosthesis products for a normal adult. The relevant anatomy index was measured, and physical parameter, radiographic, geometric, knee kinematic, and distal geometry data were collected on the femur and tibia.

Results

The width of the femoral condyle (WFC), width of the medial femoral condyle (WMFC), width of the lateral femoral condyle (WLFC), depth of the intercondylar fossa (DICF), sagittal length of the medial femoral condyle (SLMFC), sagittal length of the lateral femoral condyle (SLLFC), angle of the medial femoral condyle (AMFC), and angle of the lateral femoral condyle (ALFC) in the femur and the transverse diameter (ML) and anteroposterior diameter (AP) of the tibial bone were measured. These parameters were significantly lower in the normal group compared with the prosthesis product model group.

Conclusion

When using an imported knee prosthesis, the osteotomy angle may not fit perfectly. Use of an imported prosthesis may be an important factor in the increasing failure of knee arthroplasty in China.

MR Imaging-Histology Correlation by Tailored 3D-Printed Slicer in Oncological Assessment

3D printing and reverse engineering are innovative technologies that are revolutionizing scientific research in the health sciences and related clinical practice. Such technologies are able to improve the development of various custom-made medical devices while also lowering design and production costs. Recent advances allow the printing of particularly complex prototypes whose geometry is drawn from precise computer models designed on in vivo imaging data. This review summarizes a new method for histological sample processing (applicable to e.g., the brain, prostate, liver, and renal mass) which employs a personalized mold developed from diagnostic images through computer-aided design software and 3D printing. Through positioning the custom mold in a coherent manner with respect to the organ of interest (as delineated by in vivo imaging data), the cutting instrument can be precisely guided in order to obtain blocks of tissue which correspond with high accuracy to the slices imaged. This approach appeared crucial for validation of new quantitative imaging tools, for an accurate imaging-histopathological correlation and for the assessment of radiogenomic features extracted from oncological lesions. The aim of this review is to define and describe 3D printing technologies which are applicable to oncological assessment and slicer design, highlighting the radiological and pathological perspective as well as recent applications of this approach for the histological validation of and correlation with MR images.

Comparison of Absolute Apparent Diffusion Coefficient (ADC) Values in ADC Maps Generated Across Different Postprocessing Software: Reproducibility in Endometrial Carcinoma

OBJECTIVE

Apparent diffusion coefficient (ADC) maps are usually generated by builtin software provided by the MRI scanner vendors; however, various open-source postprocessing software packages are available for image manipulation and parametric map generation. The purpose of this study is to establish the reproducibility of absolute ADC values obtained using different postprocessing software programs.

MATERIALS AND METHODS

DW images with three b values were obtained with a 1.5-T MRI scanner, and the trace images were obtained. ADC maps were automatically generated by the in-line software provided by the vendor during image generation and were also separately generated on postprocessing software. These ADC maps were compared on the basis of ROIs using paired t test, Bland-Altman plot, mountain plot, and Passing-Bablok regression plot.

RESULTS

There was a statistically significant difference in the mean ADC values obtained from the different postprocessing software programs when the same baseline trace DW images were used for the ADC map generation.

CONCLUSION

For using ADC values as a quantitative cutoff for histologic characterization of tissues, standardization of the postprocessing algorithm is essential across processing software packages, especially in view of the implementation of vendor-neutral archiving.