Automatic trending and analysis of SPECT quality assurance with artificial intelligence optical character recognition

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-03 DOI:10.1002/mp.18083

Shanli Ding, Rachel M. Barbee, Osama Mawlawi, Tinsu Pan

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引用次数: 0

Abstract

Background

To guarantee high-quality patient scans, thorough quality assurance (QA) of SPECT or gamma cameras, including performance, review, and documentation, is essential.

Purpose

We developed a novel Nuclear Medicine Quality Assurance server (NMQA) with an AI deep learning (AIDL) optical character recognition (OCR) system to automate QA data retrieval and review from SPECT and gamma cameras. The system extracts and compares daily and weekly QA data against specifications. Our goal was to improve the efficiency of QA reviews and facilitate trending, storage, and auditing of QA data across our large hospital network.

Methods

The NMQA Server was implemented in a Linux system using open-source Python as the programming language, DICOM tool kit DCMTK for query of QA data, and Pydicom for managing DICOM images and Structured Query Language (SQL) for interacting with a relational MySQL database. The MySQL database stores numerical results for intrinsic and extrinsic floods, MHR, and COR, along with pointers to the image database facilitating trending analysis of numerical values and flood data evaluation. It also streamlines the review through the server's web interface, accessible on iPhones, iPads, and computers. The AIDL OCR is structured into three stages: feature extraction, sequence labeling, and transcription. The OCR comprises two steps: region of interest (ROI) extraction and character recognition. The AIDL OCR was benchmarked for both accuracy and speed against four common OCRs of Tesseract, OCRopus, PhotoOCR, and EasyOCR on a QA dataset, consisting of 60 flood and 6 COR images without post-processing, and evaluated for accuracy on 3459 flood-scans with post-processing.

Results

The new NMQA server can automatically query QA data, avoid the frequent mistake of typographical errors in naming the QA data, extract the numerical values of the QA data, and build a QA database for trending and analysis of the QA data. It takes about 3 min to complete a query of QA data from all 14 scanners and subsequent postprocessing. The web design facilitated review of flood images over days. The time to review the QA data on PACS without the NMQA server was about 60 min and has been reduced to several minutes using the new NMQA server web page on iPhones, iPads, or computers. The AIDL OCR outperformed Tesseract, OCRopus, PhotoOCR, and EasyOCR in speed and accuracy, maintaining CPU-friendly performance with a processing speed of just 0.3 s per image and accuracy of 93.53%. The AIDL OCR achieved an accuracy of 99.9% in recognizing numerical values in the Arial font, with sizes ranging from 10 to 14, specific to the two different kinds of scanners utilized in this study.

Conclusion

The NMQA server automatically queries QA data to avoid the frequent mistake of typographical errors in naming the QA data, eliminates manual checking of the numerical values against the manufacturers’ specifications, improves the efficiency of review of the daily flood images and weekly bar resolution phantom images, enables trending and analysis of the QA data for quality assurance and improvement, and documents the QA data and review for auditing.

Abstract Image

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基于人工智能光学字符识别的SPECT质量保证自动趋势分析

为了保证高质量的患者扫描，SPECT或伽马相机的全面质量保证（QA），包括性能，审查和文件，是必不可少的。我们开发了一种新型的核医学质量保证服务器（NMQA），该服务器具有人工智能深度学习（AIDL）光学字符识别（OCR）系统，可以自动检索和审查来自SPECT和伽马相机的质量保证数据。系统根据规格提取每日和每周的QA数据并进行比较。我们的目标是提高QA审查的效率，并促进我们大型医院网络中QA数据的趋势、存储和审计。方法在Linux操作系统上，使用开源Python作为编程语言，使用DICOM工具包DCMTK对QA数据进行查询，使用Pydicom管理DICOM图像，并使用结构化查询语言（SQL）与关系型MySQL数据库进行交互，实现NMQA服务器。MySQL数据库存储了内部和外部洪水、MHR和COR的数值结果，以及指向图像数据库的指针，方便了数值趋势分析和洪水数据评估。它还通过服务器的web界面简化了审查，可以在iphone、ipad和电脑上访问。AIDL OCR分为三个阶段：特征提取，序列标记和转录。OCR包括两个步骤：感兴趣区域提取和字符识别。在QA数据集上，AIDL OCR与Tesseract、OCRopus、PhotoOCR和EasyOCR这四种常见OCR进行了精度和速度的基准测试，该数据集由60张洪水和6张未经后处理的COR图像组成，并在经过后处理的3459张洪水扫描上评估了准确性。结果新型NMQA服务器能够自动查询QA数据，避免频繁出现的QA数据命名中出现的排版错误，提取QA数据的数值，建立QA数据库，对QA数据进行趋势分析。完成对所有14台扫描仪的QA数据的查询和随后的后处理大约需要3分钟。网页设计方便了浏览几天的洪水图片。在没有NMQA服务器的情况下，在PACS上查看QA数据的时间约为60分钟，而在iphone、ipad或电脑上使用新的NMQA服务器网页后，时间缩短到了几分钟。AIDL OCR在速度和精度方面优于Tesseract， OCRopus， PhotoOCR和EasyOCR，保持cpu友好性能，每张图像的处理速度仅为0.3 s，精度为93.53%。针对本研究中使用的两种不同类型的扫描仪，AIDL OCR在识别Arial字体（大小范围从10到14）中的数值时达到了99.9%的准确率。NMQA服务器自动查询QA数据，避免了在命名QA数据时经常出现的排版错误，消除了手工核对厂商规格的数值，提高了每日洪水图像和每周条形分辨率幻象图像的审查效率，实现了QA数据的趋势分析，以保证质量和改进，并将QA数据和审查记录为审计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.