FLORA software: semi-automatic LGE-CMR analysis tool for cardiac lesions identification and characterization

Pradella, Silvia; Mazzoni, Lorenzo Nicola; Letteriello, Mayla; Tortoli, Paolo; Bettarini, Silvia; Amicis, Cristian De; Grazzini, Giulia; Busoni, S.; Palumbo, Pierpaolo; Belli, Giacomo; Miele, Vittorio

doi:10.1007/s11547-022-01491-8

Cited by 5 publications

(6 citation statements)

References 69 publications

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“…For example, using a lower SD threshold of the distal myocardium leads to a significantly lower threshold for encompassing the extent of delayed enhancement, resulting in underestimation [13]. The SD values, including SD Myo , SD MDEA , and SD BG of the LGE DL images, showed [12], the P area−DL did not statistically differ from the P area−O values when the FWHM method was used, as the technique only results in noise reduction without altering information fidelity on LGE DL images. It yields highly reproducible and consistent enhanced areas regardless of the underlying etiologies for assessing the severity and extent of MI and other myocardial diseases [13,16,26,32,33].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, timely and accurate UMI identification and assessment are fundamental for patient stratification and therapeutic planning [4,5,11]. In practice, despite many applications of standard deviation (SD) and full width at half maximum (FWHM) techniques, no consensus exists for quantifying scars on LGE images; this challenge persists across different cardiac diseases [12][13][14]. Obviously, a gap exists in current diagnostic frameworks for analyzing myocardium delayed enhancement.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction

Lu,

Liu,

Yan

et al. 2024

BMC Med Imaging

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Background The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. Methods This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD–5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. Results The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea−DL was elevated compared to Parea−O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea−DL and Parea−O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea−DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. Conclusions STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction

Lu,

Liu,

Yan

et al. 2024

BMC Med Imaging

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“…In recent years, technological innovation has caused CTA to undergo enormous implementation as well [ 31 ]. Thanks to the development of innovative software using automated quantification techniques, there is now the possibility to identify the total plaque volume and its characteristics, i.e., to highlight low-attenuation plaque (<30 HU), the so-called high-risk plaque that is the strongest predictor of future myocardial infarction regardless of CAC, clinical risk factors or severity of stenosis, the percent volume of the atheroma (the proportion of the total volume of the vessel wall occupied by atherosclerotic plaque), as well as the volume of plaque indexed to the size of the coronary artery or patient.…”

Section: Cardiovascular Disease In Women: Sex-associated Differencesmentioning

confidence: 99%

Gender Medicine in Clinical Radiology Practice

Giacobbe

Granata

Trovato

et al. 2023

JPM

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Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

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“…Whenever there is a pathological process, the kinetics vary, and the wash-out is reduced, resulting in longer persistence of the myocardial tissue [ 51 ]. This is due to every process that causes a decreasing ratio of normal myocytes to extracellular space, such as the ruptures of the membranes’ cells, as in an infarcted lesion, or a process that expands the interstitial space such as tissue fibrosis or inflammation [ 52 ].…”

Section: Cardiac Magnetic Resonancementioning

confidence: 99%

Practical Guide to Interpreting Cardiac Magnetic Resonance in Patients with Cardiac Masses

Grazzini

Pradella

Rossi

et al. 2023

JCDD

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It is common for a cardiac mass to be discovered accidentally during an echocardiographic examination. Following the relief of a cardiac mass, being able to evaluate and characterize it using non-invasive imaging methods is critical. Echocardiography, computed tomography (CT), cardiac magnetic resonance imaging (CMR), and positron emission tomography (PET) are the main imaging modalities used to evaluate cardiac masses. Although multimodal imaging often allows for a better assessment, CMR is the best technique for the non-invasive characterization of tissues, as the different MR sequences help in the diagnosis of cardiac masses. This article provides detailed descriptions of each CMR sequence employed in the evaluation of cardiac masses, underlining the potential information it can provide. The description in the individual sequences provides useful guidance to the radiologist in performing the examination.

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FLORA software: semi-automatic LGE-CMR analysis tool for cardiac lesions identification and characterization

Cited by 5 publications

References 69 publications

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction

Gender Medicine in Clinical Radiology Practice

Practical Guide to Interpreting Cardiac Magnetic Resonance in Patients with Cardiac Masses

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