Standardized medical image registration for radiological identification of decedents based on paranasal sinuses
Introduction
The first advances in image registration algorithms occurred several decades ago1, 2, 3, 4, 5; however, even with the implementation of combined positron emission tomography (PET) - computed tomography (CT) scanners in the clinical field for almost two decades, accurate image alignment between the modalities by using medical image registration technologies have evolved constantly.6,7 Currently, image registration has become a common approach for follow-up diagnosis, planning radiation treatment, angiography or dual-energy CT.8, 9, 10, 11, 12, 13 Thus, software tools for image registration have found widespread application in clinical medicine.
Since the implementation of forensic radiology in the field of legal medicine more than a decade ago,14, 15, 16, 17 the use of postmortem CT (PMCT) to supplement autopsy has increased continuously worldwide.18 Furthermore, PMCT has become a suitable method for the radiological identification of decedents (RadID)19,20; PMCT has even been recommended for disaster victim identification (DVI).21,22 Esteemed members of the International Society of Forensic Radiology and Imaging (ISFRI) have published recommendations how PMCT should be used for DVI.23 The increased use of CT head examinations in clinical radiology24 has made the technique of comparing paranasal sinuses in three-dimensional (3D) antemortem CT (AMCT) datasets and 3D PMCT datasets for RadID more practicable.24,25 The development of the frontal sinus is complete until the age of 15–20 years.26 From then on, the anatomy of the frontal sinus remains unchanged until old age, when the likelihood of the occurrence of progressive pneumatisation due to atrophic changes increases.27
Here, we present the use of a standard medical image registration tool for RadID that is based on comparing paranasal sinuses.
Section snippets
Materials and methods
Post-processing, including image registration and fusion, was performed using standard radiological imaging software for 3D reading and advanced visualization (MM Reading, syngo.via, Version VB10B HF03, Siemens Healthcare GmbH, Forchheim, Germany). This post-processing workflow enables image the alignment, registration and fusion of two 3D datasets from various modalities taken at different times. AMCT datasets of the head were acquired using various CT scanners from different vendors. PMCT
Results
No false positive matches or false negative matches (missed positive matches) were made by any rater. Thus, sensitivity, specificity and accuracy revealed 100% for each rater and the intraclass correlation was excellent (ICC = 1.00).
The measured times from starting the workflow until the identification of a positive or negative match in each case per rater were listed in Table 1. Time was indicated in minutes and seconds [mm:ss]. Statistical analyses revealed good intraclass correlations
Discussion
In this study, we aimed to present the use of a standard medical image registration technique for comparing AMCT and PMCT datasets based on paranasal sinuses as an easy-to-use, time-efficient and reliable approach for RadID. The simplicity of the presented approach is demonstrated by raters 2 and 3, who had no previous experience with the applied software or identification procedures at all. Importantly, none of the raters made false positive or false negative identifications. Despite the small
Conclusion
Standardized medical image registration looks suitable for RadID based on paranasal sinuses, and this simple method may present a quick and easy method for RadID.
Conflicts of interest
The authors have no conflict of interest to report.
Ethical approval
This study was performed with human cadavers. Ethical approval was waived by the responsible ethics committee of the Canton of Zurich (waiver number: 2015-0686). This article does not contain any studies with (living) human participants or animals performed by any of the authors.
Acknowledgement
The authors acknowledge Niklaus Zölch for his participation in this study. The authors express their gratitude to Emma Louise Kessler, MD, for her generous donation to the Zurich Institute of Forensic Medicine, University of Zurich, Switzerland.
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2021, Pattern Recognition LettersCitation Excerpt :Image annotation is an important research point for image content analysis. The quality of the annotation will directly affect the performance of the image application [1–2]. In the background of increasingly mature medical radiological image processing technology, medical radiological images are used for clinical diagnosis and treatment to improve the accuracy of clinical examinations [3].