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Lemaire R, Raboutet C, Leleu T, Jaudet C, Dessoude L, Missohou F, Poirier Y, Deslandes PY, Lechervy A, Lacroix J, Moummad I, Bardet S, Thariat J, Stefan D, Corroyer-Dulmont A. Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments. Cancer Radiother 2024; 28:251-257. [PMID: 38866650 DOI: 10.1016/j.canrad.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 11/28/2023] [Indexed: 06/14/2024]
Abstract
PURPOSE MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™, can increase the resolution of acquired images. The objective of this prospective study was to evaluate the impact of this algorithm that halves the acquisition time on the detectability of brain lesions in radiology and radiotherapy. MATERIAL AND METHODS The T1/T2 MRI of 33 patients with brain metastases or meningiomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pixel intensity and lesions size. RESULTS The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for radiology and radiotherapy respectively. Undetected lesions on the IA image are lesions with a diameter less than 4mm and statistically low average gadolinium-enhancement contrast. CONCLUSION It is possible to reduce MRI acquisition times by half using the commercial algorithm to restore the characteristics of the image and obtain good specificity and sensitivity for lesions with a diameter greater than 4mm.
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Affiliation(s)
- R Lemaire
- Medical Physics Department, centre François-Baclesse, 14000 Caen, France
| | - C Raboutet
- Radiology Department, centre François-Baclesse, 14000 Caen, France
| | - T Leleu
- Radiotherapy Department, centre François-Baclesse, 14000 Caen, France
| | - C Jaudet
- Medical Physics Department, centre François-Baclesse, 14000 Caen, France
| | - L Dessoude
- Radiotherapy Department, centre François-Baclesse, 14000 Caen, France
| | - F Missohou
- Radiotherapy Department, centre François-Baclesse, 14000 Caen, France
| | - Y Poirier
- Radiology Department, centre François-Baclesse, 14000 Caen, France
| | - P-Y Deslandes
- Informatics Department, centre François-Baclesse, 14000 Caen, France
| | - A Lechervy
- UMR Greyc, Normandie Université, UniCaen, EnsiCaen, CNRS, 14000 Caen, France
| | - J Lacroix
- Radiology Department, centre François-Baclesse, 14000 Caen, France
| | - I Moummad
- Medical Physics Department, centre François-Baclesse, 14000 Caen, France; IMT Atlantique, Lab-Sticc, UMR CNRS 6285, 29238 Brest, France
| | - S Bardet
- Nuclear Medicine Department, centre François-Baclesse, 14000 Caen, France
| | - J Thariat
- Radiotherapy Department, centre François-Baclesse, 14000 Caen, France
| | - D Stefan
- Radiotherapy Department, centre François-Baclesse, 14000 Caen, France
| | - A Corroyer-Dulmont
- Medical Physics Department, centre François-Baclesse, 14000 Caen, France; Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, 14000 Caen, France.
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Brzostowski K, Obuchowicz R. Combining variational mode decomposition with regularisation techniques to denoise MRI data. Magn Reson Imaging 2024; 106:55-76. [PMID: 37972800 DOI: 10.1016/j.mri.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/11/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.
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Affiliation(s)
- Krzysztof Brzostowski
- Department of Computer Science and Systems Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Wrocław 50-370, Poland.
| | - Rafał Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, Kraków 31-501, Poland
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Kang HJ, Lee JM, Park SJ, Lee SM, Joo I, Yoon JH. Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction. Curr Med Imaging 2024; 20:e250523217310. [PMID: 37231764 DOI: 10.2174/1573405620666230525104809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/23/2023] [Accepted: 04/06/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. OBJECTIVES To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). AIMS This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. METHOD In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. RESULTS The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. CONCLUSION DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.
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Affiliation(s)
- Hyo-Jin Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Sae Jin Park
- Department of Radiology, G&E alphadom medical center, Seongnam, Korea
| | - Sang Min Lee
- Department of Radiology, Cha Gangnam Medical Center, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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Klingebiel M, Weiland E, Boschheidgen M, Ullrich T, Arsov C, Radtke JP, Benkert T, Nickel M, Strecker R, Wittsack HJ, Albers P, Antoch G, Schimmöller L. Improved diffusion-weighted imaging of the prostate: Comparison of readout-segmented and zoomed single-shot imaging. Magn Reson Imaging 2023; 98:55-61. [PMID: 36649807 DOI: 10.1016/j.mri.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/15/2023]
Abstract
OBJECTIVES Diffusion weighted imaging (DWI) is the most important sequence for detection and grading prostate cancer (PCa), but it is considerably prone to artifacts. New approaches like zoomed single-shot imaging (z-EPI) with advanced image processing or multi-shot readout segmentation (rs-EPI) try to improve DWI quality. This study evaluates objective and subjective image quality (IQ) of rs-EPI and z-EPI with and without advanced processing. MATERIALS AND METHODS Fifty-six consecutive patients (67 ± 8 years; median PSA 8.3 ng/ml) with mp-MRI performed at 3 Tesla between February and October 2019 and subsequently verified PCa by targeted plus systematic MRI/US-fusion biopsy were included in this retrospective single center cohort study. Rs-EPI and z-EPI were prospectively acquired in every patient. Signal intensities (SI) of PCa and benign tissue in ADC, b1000, and calculated high b-value images were analyzed. Endpoints were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), PCa contrast intensity (CI), and subjective IQ on a 5-point scale evaluated by three blinded readers. Wilcoxon signed rank test, Friedman test and Cohen's kappa coefficient was calculated. RESULTS SNR, CNR, and PCa CI of z-EPI with and without advanced processing was superior to rs-EPI (p < 0.01), whereas no significant differences were observed between z-EPI with and without advanced processing. Subjective IQ was significantly higher for z-EPI with advanced processing compared rs-EPI for ADC, b1000, and calculated high b-values (p < 0.01). Compared to z-EPI without advanced processing, z-EPI with advanced processing was superior for ADC and calculated high b-values (p < 0.01), but no significant differences were shown for b1000 images. CONCLUSIONS Z-EPI with and without advanced processing was superior to rs-EPI regarding objective imaging parameters and z-EPI with advanced processing was superior to rs-EPI regarding subjective imaging parameters for the detection of PCa.
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Affiliation(s)
- M Klingebiel
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
| | - E Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
| | - M Boschheidgen
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
| | - T Ullrich
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
| | - C Arsov
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany.
| | - J P Radtke
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany.
| | - T Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
| | - M Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
| | - R Strecker
- Siemens Healthcare GmbH, Europe, Middle East & Africa, Karlheinz-Kaske-Str. 2, 91052 Erlangen, Germany.
| | - H J Wittsack
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
| | - P Albers
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany.
| | - G Antoch
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
| | - L Schimmöller
- University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
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Arrigo A, Aragona E, Battaglia Parodi M, Bandello F. Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives. Prog Retin Eye Res 2023; 92:101111. [PMID: 35933313 DOI: 10.1016/j.preteyeres.2022.101111] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/16/2022] [Accepted: 07/19/2022] [Indexed: 02/01/2023]
Abstract
When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.
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Affiliation(s)
- Alessandro Arrigo
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy.
| | - Emanuela Aragona
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy
| | - Maurizio Battaglia Parodi
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy
| | - Francesco Bandello
- Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, via Olgettina 60, 20132, Milan, Italy
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Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging. J Clin Med 2022; 11:jcm11092526. [PMID: 35566657 PMCID: PMC9103884 DOI: 10.3390/jcm11092526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/12/2022] [Accepted: 04/26/2022] [Indexed: 12/10/2022] Open
Abstract
The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin’s correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.
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Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM 10 and PM 2.5 Particulate Matter. SENSORS 2021; 21:s21165483. [PMID: 34450925 PMCID: PMC8399617 DOI: 10.3390/s21165483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/04/2021] [Accepted: 08/11/2021] [Indexed: 12/03/2022]
Abstract
The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models’ quality for the test datasets equals 0.85 and 0.73 for PM10 and 0.63 for PM2.5. The quality of each classification model differs (0.86 and 0.73 for PM10, and 0.80 for PM2.5). The obtained results show that the created classification models could be used in PM10 and PM2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained.
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Bębas E, Borowska M, Derlatka M, Oczeretko E, Hładuński M, Szumowski P, Mojsak M. Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102446] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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George-Jones NA, Chkheidze R, Moore S, Wang J, Hunter JB. MRI Texture Features are Associated with Vestibular Schwannoma Histology. Laryngoscope 2020; 131:E2000-E2006. [PMID: 33300608 DOI: 10.1002/lary.29309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/17/2020] [Accepted: 11/29/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVES/HYPOTHESIS To determine if commonly used radiomics features have an association with histological findings in vestibular schwannomas (VS). STUDY DESIGN Retrospective case-series. METHODS Patients were selected from an internal database of those who had a gadolinium-enhanced T1-weighted MRI scan captured prior to surgical resection of VS. Texture features from the presurgical magnetic resonance image (MRI) were extracted, and pathologists examined the resected tumors to assess for the presence of mucin, lymphocytes, necrosis, and hemosiderin and used a validated computational tool to determine cellularity. Sensitivity, specificity, and positive likelihood ratios were also computed for selected features using the Youden index to determine the optimal cut-off value. RESULTS A total of 45 patients were included. We found significant associations between multiple MRI texture features and the presence of mucin, lymphocytes, hemosiderin, and cellularity. No significant associations between MRI texture features and necrosis were identified. We were able to identify significant positive likelihood ratios using Youden index cut-off values for mucin (2.3; 95% CI 1.2-4.3), hemosiderin (1.5; 95% CI 1.04-2.1), lymphocytes (3.8; 95% CI 1.2-11.7), and necrosis (1.5; 95% CI 1.1-2.2). CONCLUSIONS MRI texture features are associated with underlying histology in VS. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E2000-E2006, 2021.
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Affiliation(s)
- Nicholas A George-Jones
- Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Rati Chkheidze
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Samantha Moore
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
| | - Jacob B Hunter
- Department of Otolaryngology-Head and Neck Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A
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Klingebiel M, Ullrich T, Quentin M, Bonekamp D, Aissa J, Mally D, Arsov C, Albers P, Antoch G, Schimmöller L. Advanced diffusion weighted imaging of the prostate: Comparison of readout-segmented multi-shot, parallel-transmit and single-shot echo-planar imaging. Eur J Radiol 2020; 130:109161. [DOI: 10.1016/j.ejrad.2020.109161] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 06/30/2020] [Indexed: 01/21/2023]
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Shape and Enhancement Analysis as a Useful Tool for the Presentation of Blood Hemodynamic Properties in the Area of Aortic Dissection. J Clin Med 2020; 9:jcm9051330. [PMID: 32370301 PMCID: PMC7290319 DOI: 10.3390/jcm9051330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/19/2020] [Accepted: 04/28/2020] [Indexed: 11/17/2022] Open
Abstract
The aim of this study was to create a mathematical approach for blood hemodynamic description with the use of brightness analysis. Medical data was collected from three male patients aged from 45 to 65 years with acute type IIIb aortic dissection that started proximal to the left subclavian artery and involved the renal arteries. For the recognition of wall dissection areas Digital Imaging and Communications in Medicine (DICOM) data were applied. The distance from descending aorta to the diaphragm was analyzed. Each time Feret (DF) and Hydraulic (DHy) diameter were calculated. Moreover, an average brightness (BAV) was analyzed. Finally, to describe blood hemodynamic in the area of aortic wall dissection, mathematical function combining difference in brightness value and diameter for each computed tomography (CT) scan was calculated. The results indicated that DF described common duct more accurately compare to DHy. While, DHy described more accurately true and false ducts. Each time when connection of true and false duct appeared, true duct had lower brightness compare to common duct and false duct. Moreover, false duct characterized with higher brightness compare to common duct. In summary, the proposed algorithm mimics changes in brightness value for patients with acute type IIIb aortic dissection.
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Does image normalization and intensity resolution impact texture classification? Comput Med Imaging Graph 2020; 81:101716. [PMID: 32222685 DOI: 10.1016/j.compmedimag.2020.101716] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 03/04/2020] [Accepted: 03/05/2020] [Indexed: 11/23/2022]
Abstract
Image texture is a very important component in many types of images, including medical images. Medical images are often corrupted by noise and affected by artifacts. Some of the texture-based features that should describe the structure of the tissue under examination may also reflect, for example, the uneven sensitivity of the scanner within the tissue region. This in turn may lead to an inappropriate description of the tissue or incorrect classification. To limit these phenomena, the analyzed regions of interest are normalized. In texture analysis methods, image intensity normalization is usually followed by a reduction in the number of levels coding the intensity. The aim of this work was to analyze the impact of different image normalization methods and the number of intensity levels on texture classification, taking into account noise and artifacts related to uneven background brightness distribution. Analyses were performed on four sets of images: modified Brodatz textures, kidney images obtained by means of dynamic contrast-enhanced magnetic resonance imaging, shoulder images acquired as T2-weighted magnetic resonance images and CT heart and thorax images. The results will be of use for choosing a particular method of image normalization, based on the types of noise and distortion present in the images.
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Bielecka M, Bielecki A, Obuchowicz R, Piórkowski A. Universal Measure for Medical Image Quality Evaluation Based on Gradient Approach. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7303719 DOI: 10.1007/978-3-030-50423-6_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In this paper, a new universal measure of medical images quality is proposed. The measure is based on the analysis of the image by using gradient methods. The number of isolated peaks in the examined image, as a function of the threshold value, is the basis of the assessment of the image quality. It turns out that for higher quality images the curvature of the graph of the said function has a higher value for lower threshold values. On the basis of the observed property, a new method of no-reference image quality assessment has been created. The experimental verification confirmed the method efficiency. The correlation between the arrangement depending on the image quality done by an expert and by using the proposed method is equal to 0.74. This means that the proposed method gives a correlation of higher than the best methods described in the literature. The proposed measure is useful to maximize the image quality while minimizing the time of medical examination.
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