Q-Bot: automatic DICOM metadata monitoring for the next level of quality management in nuclear medicine

Nagy, Ferenc; Krizsán, Áron Krisztián; Kukuts, Kornél; Szolikova, Melinda; Hascsi, Zsolt; Barna, Sándor; Acs, Antonietta; Szabó, Péter; Trón, Lajos; Balkay, László; Dahlbom, Magnus; Zentai, Mihaly; Forgács, Attila; Garai, Ildikó

doi:10.1186/s40658-021-00371-w

Cited by 3 publications

(2 citation statements)

References 27 publications

(37 reference statements)

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“…This information can be used in different ways to assess the quality of imaging and the radiation doses received by patients. [3][4][5][6] As part of the TMIST QC program, images from trial participants are de-identified, stripped of the actual breast image and the header of each image is captured.…”

Section: Introductionmentioning

confidence: 99%

“…Each mammographic and tomosynthesis image contains a header in which information (“metadata”) on many parameters of the image acquisition are recorded. This information can be used in different ways to assess the quality of imaging and the radiation doses received by patients 3–6 . As part of the TMIST QC program, images from trial participants are de‐identified, stripped of the actual breast image and the header of each image is captured.…”

Section: Introductionmentioning

confidence: 99%

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Audit of data from examination image headers collected for quality assurance in the ECOG‐ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST)

Maki,

Mawdsley,

Mainprize

et al. 2023

Medical Physics

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PurposeA comprehensive, centrally‐monitored physics quality control (QC) program was developed for the Tomosynthesis Imaging Screening Trial (TMIST), a randomized controlled trial of digital breast tomosynthesis (TM) versus digital mammography (DM) for cancer screening. As part of the program, in addition to a set of phantom‐based tests, de‐identified data on image acquisition and processing parameters were captured from the DICOM headers of all individual patient images in the trial. These data were analyzed to assess the potential usefulness of header data from digital mammograms and tomosynthesis images of patients for quality assurance in breast imaging.MethodsData were automatically extracted from the headers of all de‐identified patient mammograms and tomosynthesis images in the TMIST study. Image acquisition parameters and estimated radiation doses were tracked for individual sites, systems and across system types. These parameters included (among others) kV, target/filter use, number of acquired views per examination, AEC mode, compression thickness and force and detector temperature. Consistency of manually entered study data parameters (subject ID, screening time‐point) from TMIST was evaluated. Preliminary observations from the program are presented.ResultsWe report on data from 812 651 images from 135 525 examinations acquired between October, 2017 and December, 2022. Data came from 6 system models from 3 manufacturers. There was greater variability both in the number of views used and in the estimated (proxy) doses received in DM exams compared to TM. Mean proxy doses per examination varied among manufacturers from 2.76–4.54 mGy for DM and 3–4.84 mGy for the tomosynthesis component in the TM arm with maximum examination proxy doses of 20 and 26 mGy for DM and TM respectively. Mean proxy doses per examination for the combination examination in TM (tomosynthesis plus digital mammography) varied from 6.6 to 7.6 mGy among manufacturers with a maximum of 44.5 mGy.ConclusionsOverall, modern digital mammography and tomosynthesis systems used in TMIST have operated very reliably. Doses vary considerably due to variation in the number of views per examination, thickness and fibro‐glandularity of the breast, and choices in the use of synthesized versus actual 2D mammography in the TM examination. These data may also be useful in predicting equipment problems. Header information is valuable not only for automated QC, but also for cross‐checking accuracy and consistency of data in a clinical study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audit of data from examination image headers collected for quality assurance in the ECOG‐ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST)

Maki,

Mawdsley,

Mainprize

et al. 2023

Medical Physics

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Robustness analysis of denoising neural networks for bone scintigraphy

Kovács

Bükki²,

Légràdi³

et al. 2022

Nuclear Instruments and Methods in Physics Research Section A:

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Focal Increased Radiopharmaceutical Uptake Differentiation Using Quantitative Indices

Sivasubramaniyan¹,

Venkataramaniah²

2022

Radiopharmaceuticals - Current Research for Better Diagnosis and Therapy

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Focal increased radiopharmaceutical uptake in a lesion results in focal Hot Spots in the scans. This can occur in benign infective or inflammatory disorders and cancerous diseases as well. Comparison between malignant and benign lesions is important. The Hot spots can be classified into benign and malignant lesions by Spatial Scintimetry or Temporal Scintimetry. Spatial Scintimetry compares the uptake in the region of interest with the adjacent tissue or the unaffected contralateral site. The quantitative indices are lesion/non lesion ratio, lesion/background activity and lesion to Bone ratio etc. The Temporal Scintimetry relies on the changes in the counts or uptake in the Hotspot lesion with reference to the dual point time of acquisition. The Hotspot in the bone scan can be classified using the quantitative index of retention ratio by Dr. V. Siva and Israel. In PET studies the focal hot spots can be differentiated into benign and malignant lesion using the dual phase PETCT evaluation using the Rong’s Retention ratio and Dr. V. Siva’s modified RRI values.

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Q-Bot: automatic DICOM metadata monitoring for the next level of quality management in nuclear medicine

Cited by 3 publications

References 27 publications

Audit of data from examination image headers collected for quality assurance in the ECOG‐ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST)

Audit of data from examination image headers collected for quality assurance in the ECOG‐ACRIN EA1151 tomosynthesis mammographic imaging screening trial (TMIST)

Robustness analysis of denoising neural networks for bone scintigraphy

Focal Increased Radiopharmaceutical Uptake Differentiation Using Quantitative Indices

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