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Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions.

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Τίτλος: Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions.
Συγγραφείς: Héroux A; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada., Destrempes F; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada., Rafati I; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada., Maxime B; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.; Department of Radiology, University of Montreal Hospital, Montréal, Québec, Canada., Karam E; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.; Department of Radiology, University of Montreal Hospital, Montréal, Québec, Canada., Fohlen A; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.; Department of Radiology, University of Montreal Hospital, Montréal, Québec, Canada., Nguyen BN; Department of Pathology, University of Montreal Hospital, Montréal, Québec, Canada., Castel H; Departments of Hepatology and Liver Transplantation, University of Montreal Hospital, Montréal, Québec, Canada., Tang A; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.; Department of Radiology, University of Montreal Hospital, Montréal, Québec, Canada., Cloutier G; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.; Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada.
Πηγή: Physics in medicine and biology [Phys Med Biol] 2026 Jul 02; Vol. 71 (13). Date of Electronic Publication: 2026 Jul 02.
Τύπος έκδοσης: Journal Article; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: Bristol : IOP Publishing
Ιατρικοί όροι (MeSH): Liver Neoplasms*/diagnostic imaging , Liver Neoplasms*/pathology , Image Processing, Computer-Assisted*/methods , Scattering, Radiation*, Humans ; Ultrasonography ; Signal-To-Noise Ratio ; Phantoms, Imaging
Περίληψη: Objective.To compare four attenuation-compensation methods for backscatter coefficient (BSC) estimation, assessment of contrast, and classification of focal liver lesions (FLL).Approach. Ninety-seven patients with 100 FLL were scanned to collect radiofrequency ultrasound images. BSC methods relied on a reference phantom for system and operator-settings independent estimations. Method #1 employeda prioritissue layer segmentation and documented attenuation coefficients (AC) of each layer. Method #2 used a fixed total AC (0.85 dB/cm/MHz). Method #3 used local AC compensation. Method #4 jointly estimated total AC and BSC with a power law frequency model (bfη). BSC@3 MHz,b,η, total AC slope, and total AC were computed within segmented lesions. Lesion contrast was assessed with the contrast-to-noise ratio (CNR) and classification performances were evaluated with the area under the receiver operating characteristic curve (AUC). The composite clinical reference standard was a combination of MRI and histopathology.Main results.The study included 30 primary and 26 secondary cancers, and 44 benign nodules. Parameterbprovided the highest CNRs among BSC parameters (p< 0.0001) and gave higher CNRs than B-mode images (p< 0.0001). Method #3 was unsuitable with out-of-range values. Methods #1, #2, and #4 showed no significant differences forb,η, total AC slope, and total AC, whereas BSC@3 MHz showed overestimations with Method #4 compared with Methods #1 and #2 (p< 0.001 andp< 0.0001, respectively). For differentiating benign and malignant lesions,ηprovided the highest AUC of 0.73 (95% confidence interval (CI): 0.62-0.82). For differentiating primary and secondary cancers, BSC@3 MHz provided the highest AUC of 0.71 (95% CI: 0.55-0.83). Classification AUCs did not differ between Methods #1, #2 and #4.Significance. BSC imaging improved lesion contrast compared to B-mode and could classify FLL. Method #4 emerged as the most practical as it does not require anya prioriAC or segmentation.
(Creative Commons Attribution license.)
Contributed Indexing: Keywords: attenuation compensation; backscatter coefficient; focal liver lesion; hepatocellular carcinoma; quantitative ultrasound; ultrasound imaging
Entry Date(s): Date Created: 20260612 Date Completed: 20260702 Latest Revision: 20260702
Update Code: 20260702
DOI: 10.1088/1361-6560/ae7cd1
PMID: 42285137
Βάση Δεδομένων: MEDLINE
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  Data: Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions.
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  Data: &lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22H&#233;roux+A%22&quot;&gt;H&#233;roux A&lt;/searchLink&gt;; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Institute of Biomedical Engineering, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Destrempes+F%22&quot;&gt;Destrempes F&lt;/searchLink&gt;; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Rafati+I%22&quot;&gt;Rafati I&lt;/searchLink&gt;; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Maxime+B%22&quot;&gt;Maxime B&lt;/searchLink&gt;; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Karam+E%22&quot;&gt;Karam E&lt;/searchLink&gt;; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Fohlen+A%22&quot;&gt;Fohlen A&lt;/searchLink&gt;; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Nguyen+BN%22&quot;&gt;Nguyen BN&lt;/searchLink&gt;; Department of Pathology, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Castel+H%22&quot;&gt;Castel H&lt;/searchLink&gt;; Departments of Hepatology and Liver Transplantation, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Tang+A%22&quot;&gt;Tang A&lt;/searchLink&gt;; Clinical Laboratory of Image Processing, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, University of Montreal Hospital, Montr&#233;al, Qu&#233;bec, Canada.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Cloutier+G%22&quot;&gt;Cloutier G&lt;/searchLink&gt;; Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montr&#233;al, Qu&#233;bec, Canada.; Institute of Biomedical Engineering, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.; Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montreal, Montr&#233;al, Qu&#233;bec, Canada.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%220401220%22&quot;&gt;Physics in medicine and biology&lt;/searchLink&gt; [Phys Med Biol] 2026 Jul 02; Vol. 71 (13). &lt;i&gt;Date of Electronic Publication: &lt;/i&gt;2026 Jul 02.
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– Name: Abstract
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  Data: Objective.To compare four attenuation-compensation methods for backscatter coefficient (BSC) estimation, assessment of contrast, and classification of focal liver lesions (FLL).Approach. Ninety-seven patients with 100 FLL were scanned to collect radiofrequency ultrasound images. BSC methods relied on a reference phantom for system and operator-settings independent estimations. Method #1 employeda prioritissue layer segmentation and documented attenuation coefficients (AC) of each layer. Method #2 used a fixed total AC (0.85 dB/cm/MHz). Method #3 used local AC compensation. Method #4 jointly estimated total AC and BSC with a power law frequency model (bfη). BSC@3 MHz,b,η, total AC slope, and total AC were computed within segmented lesions. Lesion contrast was assessed with the contrast-to-noise ratio (CNR) and classification performances were evaluated with the area under the receiver operating characteristic curve (AUC). The composite clinical reference standard was a combination of MRI and histopathology.Main results.The study included 30 primary and 26 secondary cancers, and 44 benign nodules. Parameterbprovided the highest CNRs among BSC parameters (p&amp;lt; 0.0001) and gave higher CNRs than B-mode images (p&amp;lt; 0.0001). Method #3 was unsuitable with out-of-range values. Methods #1, #2, and #4 showed no significant differences forb,η, total AC slope, and total AC, whereas BSC@3 MHz showed overestimations with Method #4 compared with Methods #1 and #2 (p&amp;lt; 0.001 andp&amp;lt; 0.0001, respectively). For differentiating benign and malignant lesions,ηprovided the highest AUC of 0.73 (95% confidence interval (CI): 0.62-0.82). For differentiating primary and secondary cancers, BSC@3 MHz provided the highest AUC of 0.71 (95% CI: 0.55-0.83). Classification AUCs did not differ between Methods #1, #2 and #4.Significance. BSC imaging improved lesion contrast compared to B-mode and could classify FLL. Method #4 emerged as the most practical as it does not require anya prioriAC or segmentation.&lt;br /&gt; (Creative Commons Attribution license.)
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  Data: &lt;i&gt;Keywords: &lt;/i&gt;attenuation compensation; backscatter coefficient; focal liver lesion; hepatocellular carcinoma; quantitative ultrasound; ultrasound imaging
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  Data: &lt;i&gt;Date Created: &lt;/i&gt;20260612 &lt;i&gt;Date Completed: &lt;/i&gt;20260702 &lt;i&gt;Latest Revision: &lt;/i&gt;20260702
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