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Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 8571

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Special Issue Editors

Dr. Rafał Obuchowicz

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Department of Radiology, Jagiellonian University Medical College, 19 Kopernika Street, 31-501 Cracow, Poland
Interests: diagnostic imaging; image analysis; shape detection; image quality
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adam Piórkowski

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Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland
Interests: medical image processing; preprocessing; segmentation; texture analysis; image feature extraction
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Prof. Dr. Michał Strzelecki

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Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Łódź, Poland
Interests: medical imaging; analysis of biomedical images; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In modern medicine of our days magnetic resonance (MR) and computed tomography (CT) become fundamentals of the diagnostic process. Computed radiography (CR) is still the first diagnostic modality used in certain areas of medicine as emergency or skeletal system evaluation (including dentistry). The rapid increase of imaging hardware and incorporation of MR and CT into the standard clinical diagnostic procedures substantially enlarged the number of performed medical images in the last decades. The incorporation of diagnostic imaging procedures into the clinical diagnostic process forced imaging units to provide more exams also with the use of radiation (as CT or CR). A side consequence of the technical development and popularization of the imaging techniques are possible reduction of imaging time ( trend observed in MRI) and a possible decrease of the effective dose in CT. All aforementioned factors may negatively influence the quality of diagnostic images. Various factors present during acquisition, processing transmission and signal compression may also result in different adverse phenomena in the obtained medical images. The quality of the diagnostic image has an impact on the radiologist's decision-making process and hence patient treatment. Therefore image assessment and meticulous control of diagnostic images have to be performed routinely. Subjective image quality perception although effective in hands of experienced readers cannot be widely used and standardized. Therefore there is a need for the development of universal measures applicable to contemporary imaging techniques such as MR and CT and CR. Full reference methods (comparison to the reference image), reduced reference (comparison to certain image features) and non–reference evaluation (without reference applied) are present. Systems designed for monitoring image quality based on the different computational approaches are highly desirable by the medical community.

With this Special Issue, we aim to present a set of works that presents different aspects and approaches to image assessment in the MR, CT and CR. We strongly believe that such a collection of knowledge will have substantial benefits for readers involved in the technical and medical aspects of medical image creation and interpretation.

Dr. Rafał Obuchowicz
Prof. Dr. Adam Piorkowski
Prof. Dr. Michał Strzelecki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

CR
MRI
CT
image quality
preprocessing and postprocessing
texture analysis
artificial intelligence
quality control

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Published Papers (7 papers)

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Research

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19 pages, 13192 KiB

Open AccessArticle

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology

by Hajin Kim, Sewon Lim, Minji Park, Kyuseok Kim, Seong-Hyeon Kang and Youngjin Lee

Diagnostics 2024, 14(15), 1589; https://doi.org/10.3390/diagnostics14151589 - 23 Jul 2024

Viewed by 363

Abstract

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10⁻² mm² on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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15 pages, 4872 KiB

Open AccessArticle

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling

by Jackson Cooper, Jacky C. K. Chow and Derek Lichti

Diagnostics 2024, 14(5), 567; https://doi.org/10.3390/diagnostics14050567 - 6 Mar 2024

Viewed by 911

Abstract

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system’s components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83–85% improvement in terms of image point precision (model fit) and 85–95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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15 pages, 5334 KiB

Open AccessArticle

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography

by Chulkyun Ahn and Jong Hyo Kim

Diagnostics 2024, 14(1), 96; https://doi.org/10.3390/diagnostics14010096 - 31 Dec 2023

Cited by 2 | Viewed by 1113

Abstract

Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed to predict spurious structural components embedded in the residual noise from DL denoising models in low-dose CT and assess its feasibility for auditing the behavior of DL denoising models. We created a paired set of structure-embedded and pure noise images and trained AntiHalluciNet to predict spurious structures in the structure-embedded noise images. The performance of AntiHalluciNet was evaluated by using a newly devised residual structure index (RSI), which represents the prediction confidence based on the presence of structural components in the residual noise image. We also evaluated whether AntiHalluciNet could assess the image fidelity of a denoised image by using only a noise component instead of measuring the SSIM, which requires both reference and test images. Then, we explored the potential of AntiHalluciNet for auditing the behavior of DL denoising models. AntiHalluciNet was applied to three DL denoising models (two pre-trained models, RED-CNN and CTformer, and a commercial software, ClariCT.AI [version 1.2.3]), and whether AntiHalluciNet could discriminate between the noise purity performances of DL denoising models was assessed. AntiHalluciNet demonstrated an excellent performance in predicting the presence of structural components. The RSI values for the structural-embedded and pure noise images measured using the 50% low-dose dataset were 0.57 ± 31 and 0.02 ± 0.02, respectively, showing a substantial difference with a p-value < 0.0001. The AntiHalluciNet-derived RSI could differentiate between the quality of the degraded denoised images, with measurement values of 0.27, 0.41, 0.48, and 0.52 for the 25%, 50%, 75%, and 100% mixing rates of the degradation component, which showed a higher differentiation potential compared with the SSIM values of 0.9603, 0.9579, 0.9490, and 0.9333. The RSI measurements from the residual images of the three DL denoising models showed a distinct distribution, being 0.28 ± 0.06, 0.21 ± 0.06, and 0.15 ± 0.03 for RED-CNN, CTformer, and ClariCT.AI, respectively. AntiHalluciNet has the potential to predict the structural components embedded in the residual noise from DL denoising models in low-dose CT. With AntiHalluciNet, it is feasible to audit the performance and behavior of DL denoising models in clinical environments where only residual noise images are available. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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20 pages, 8324 KiB

Open AccessArticle

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

by Rafsanjany Kushol, Alan H. Wilman, Sanjay Kalra and Yee-Hong Yang

Diagnostics 2023, 13(18), 2947; https://doi.org/10.3390/diagnostics13182947 - 14 Sep 2023

Cited by 2 | Viewed by 1569

Abstract

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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10 pages, 5511 KiB

Open AccessArticle

Potential of Unenhanced Ultra-Low-Dose Abdominal Photon-Counting CT with Tin Filtration: A Cadaveric Study

by Henner Huflage, Jan-Peter Grunz, Theresa Sophie Patzer, Pauline Pannenbecker, Philipp Feldle, Stephanie Tina Sauer, Bernhard Petritsch, Süleyman Ergün, Thorsten Alexander Bley and Andreas Steven Kunz

Diagnostics 2023, 13(4), 603; https://doi.org/10.3390/diagnostics13040603 - 7 Feb 2023

Cited by 2 | Viewed by 1640

Abstract

Objectives: This study investigated the feasibility and image quality of ultra-low-dose unenhanced abdominal CT using photon-counting detector technology and tin prefiltration. Materials and Methods: Employing a first-generation photon-counting CT scanner, eight cadaveric specimens were examined both with tin prefiltration (Sn 100 kVp) and polychromatic (120 kVp) scan protocols matched for radiation dose at three different levels: standard-dose (3 mGy), low-dose (1 mGy) and ultra-low-dose (0.5 mGy). Image quality was evaluated quantitatively by means of contrast-to-noise-ratios (CNR) with regions of interest placed in the renal cortex and subcutaneous fat. Additionally, three independent radiologists performed subjective evaluation of image quality. The intraclass correlation coefficient was calculated as a measure of interrater reliability. Results: Irrespective of scan mode, CNR in the renal cortex decreased with lower radiation dose. Despite similar mean energy of the applied x-ray spectrum, CNR was superior for Sn 100 kVp over 120 kVp at standard-dose (17.75 ± 3.51 vs. 14.13 ± 4.02), low-dose (13.99 ± 2.6 vs. 10.68 ± 2.17) and ultra-low-dose levels (8.88 ± 2.01 vs. 11.06 ± 1.74) (all p ≤ 0.05). Subjective image quality was highest for both standard-dose protocols (score 5; interquartile range 5–5). While no difference was ascertained between Sn 100 kVp and 120 kVp examinations at standard and low-dose levels, the subjective image quality of tin-filtered scans was superior to 120 kVp with ultra-low radiation dose (p < 0.05). An intraclass correlation coefficient of 0.844 (95% confidence interval 0.763–0.906; p < 0.001) indicated good interrater reliability. Conclusions: Photon-counting detector CT permits excellent image quality in unenhanced abdominal CT with very low radiation dose. Employment of tin prefiltration at 100 kVp instead of polychromatic imaging at 120 kVp increases the image quality even further in the ultra-low-dose range of 0.5 mGy. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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Review

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16 pages, 270 KiB

Open AccessReview

An Analysis of Computed Tomography Diagnostic Reference Levels in India Compared to Other Countries

by Maajid Mohi Ud Din Malik, Mansour Alqahtani, Ibrahim Hadadi, Abdullah G. M. AlQhtani and Abdullah Alqarni

Diagnostics 2024, 14(15), 1585; https://doi.org/10.3390/diagnostics14151585 - 23 Jul 2024

Viewed by 422

Abstract

Computed Tomography (CT) is vital for diagnosing and monitoring medical conditions. However, increased usage raises concerns about patient radiation exposure. Diagnostic Reference Levels (DRLs) aim to minimize radiation doses in CT imaging. This study examines CT DRLs in India compared to other countries to identify optimization opportunities. A literature review was conducted to gather data from published studies, guidelines, and regulatory authorities. Findings show significant international variations in CT DRLs, with differences up to 50%. In India, DRLs also vary significantly across states. For head CT exams, Indian DRLs are generally 20–30% lower than international standards (27–47 mGy vs. 60 mGy). Conversely, for abdominal CT scans, Indian DRLs are 10–15% higher (12–16 mGy vs. 13 mGy). Factors influencing DRL variations include equipment differences, imaging protocols, patient demographics, and regulatory conditions. Dose-optimization techniques like automatic exposure control and iterative reconstruction can reduce radiation exposure by 25–60% while maintaining image quality. Comparative data highlight best practices, such as the United Kingdom’s 30% reduction in CT doses from 1984 to 1995 via DRL implementation. This study suggests that adopting similar practices in India could reduce radiation doses by 20–40% for common CT procedures, promoting responsible CT usage and minimizing patient exposure. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

Other

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10 pages, 1008 KiB

Open AccessTechnical Note

Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT)

by Bogdan Kostyrko, Kerstin Rubarth, Christian Althoff, Miriam Zibell, Christina Ann Neizert, Franz Poch, Giovanni Federico Torsello, Bernhard Gebauer, Kai Lehmann, Stefan Markus Niehues, Jürgen Mews, Torsten Diekhoff and Julian Pohlan

Diagnostics 2023, 13(12), 2076; https://doi.org/10.3390/diagnostics13122076 - 15 Jun 2023

Cited by 3 | Viewed by 1234

Abstract

Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman’s test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3–6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process. Full article

(This article belongs to the Special Issue Image Quality Assessment Methods in Radiography, Computed Tomography and Magnetic Resonance)

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