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1.
Nat Med ; 2020 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-32572267

RESUMO

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice.

2.
J Am Acad Dermatol ; 82(3): 622-627, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31306724

RESUMO

BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. OBJECTIVE: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. METHODS: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. RESULTS: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. LIMITATIONS: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. CONCLUSION: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

3.
Lancet Oncol ; 20(7): 938-947, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31201137

RESUMO

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06-7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9-12·9 vs 3·6%, 0·8-6·3; p<0·0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.

4.
Semin Cutan Med Surg ; 38(1): E38-E42, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-31051022

RESUMO

In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural language processing, image recognition, object detection, facial recognition, and action categorization. Indeed, in many of these applications, accuracy has reached or exceeded human levels of performance. Subsequently, a multitude of studies have begun to examine the application of these technologies to health care, and in particular, medical image analysis. Perhaps the most difficult subdomain involves skin imaging because of the lack of standards around imaging hardware, technique, color, and lighting conditions. In addition, unlike radiological images, skin image appearance can be significantly affected by skin tone as well as the broad range of diseases. Furthermore, automated algorithm development relies on large high-quality annotated image data sets that incorporate the breadth of this circumstantial and diagnostic variety. These issues, in combination with unique complexities regarding integrating artificial intelligence systems into a clinical workflow, have led to difficulty in using these systems to improve sensitivity and specificity of skin diagnostics in health care networks around the world. In this article, we summarize recent advancements in machine learning, with a focused perspective on the role of public challenges and data sets on the progression of these technologies in skin imaging. In addition, we highlight the remaining hurdles toward effective implementation of technologies to the clinical workflow and discuss how public challenges and data sets can catalyze the development of solutions.


Assuntos
Algoritmos , Inteligência Artificial , Benchmarking , Dermatologia , Humanos , Aprendizado de Máquina
5.
IEEE J Biomed Health Inform ; 23(2): 474-478, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30703051

RESUMO

Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors (lack of publicly available datasets, open-source software, computational power, etc.), the field progressed rather slowly in its first two decades. With the release of a large public dataset by the International Skin Imaging Collaboration in 2016, development of open-source software for convolutional neural networks, and the availability of inexpensive graphics processing units, dermoscopy image analysis has recently become a very active research field. In this paper, we present a brief overview of this exciting subfield of medical image analysis, primarily focusing on three aspects of it, namely, segmentation, feature extraction, and classification. We then provide future directions for researchers.


Assuntos
Dermoscopia , Interpretação de Imagem Assistida por Computador , Humanos , Melanoma/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem
6.
J Cardiovasc Magn Reson ; 21(1): 1, 2019 01 07.
Artigo em Inglês | MEDLINE | ID: mdl-30612574

RESUMO

BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS: A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS: Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). CONCLUSION: Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.


Assuntos
Aorta/diagnóstico por imagem , Valva Aórtica/diagnóstico por imagem , Cardiopatias/diagnóstico por imagem , Hemodinâmica , Aprendizado de Máquina , Imagem Cinética por Ressonância Magnética , Imagem de Perfusão do Miocárdio/métodos , Idoso , Aorta/fisiopatologia , Valva Aórtica/fisiopatologia , Automação , Velocidade do Fluxo Sanguíneo , Feminino , Cardiopatias/fisiopatologia , Humanos , Interpretação de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudo de Prova de Conceito , Estudos Prospectivos , Reprodutibilidade dos Testes , Estudos Retrospectivos , Estados Unidos
7.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 3414-3417, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30441121

RESUMO

This work presents the first segmentation study of both diseased and healthy skin in standard camera photographs from a clinical environment. Challenges arise from varied lighting conditions, skin types, backgrounds, and pathological states. For study, 400 clinical photographs (with skin segmentation masks) representing various pathological states of skin are retrospectively collected from a primary care network. 100 images are used for training and fine-tuning, and 300 are used for evaluation. This distribution between training and test partitions is chosen to reflect the difficulty in amassing large quantities of labeled data in this domain. A deep learning approach is used, and 3 public segmentation datasets of healthy skin are collected to study the potential benefits of pretraining. Two variants of U-Net are evaluated: U-Net and Dense Residual U-Net. We find that Dense Residual U-Nets have a 7.8% improvement in Jaccard, compared to classical U-Net architectures (0.55 vs. 0.51 Jaccard), for direct transfer, where fine-tuning data is not utilized. However, U-Net outperforms Dense Residual U-Net for both direct training (0.83 vs. 0.80) and fine-tuning (0.89 vs. 0.88). The stark performance improvement with fine-tuning compared to direct transfer and direct training emphasizes both the need for adequate representative data of diseased skin, and the utility of other publicly available data sources for this task.


Assuntos
Atenção Primária à Saúde , Pele , Aprendizado Profundo , Processamento de Imagem Assistida por Computador , Estudos Retrospectivos
8.
J Am Acad Dermatol ; 78(2): 270-277.e1, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-28969863

RESUMO

BACKGROUND: Computer vision may aid in melanoma detection. OBJECTIVE: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. METHODS: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. RESULTS: The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). LIMITATIONS: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. CONCLUSION: Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.


Assuntos
Algoritmos , Dermatologistas , Dermoscopia , Lentigo/diagnóstico por imagem , Melanoma/diagnóstico , Nevo/diagnóstico por imagem , Neoplasias Cutâneas/diagnóstico por imagem , Congressos como Assunto , Estudos Transversais , Diagnóstico por Computador , Humanos , Aprendizado de Máquina , Melanoma/patologia , Curva ROC , Neoplasias Cutâneas/patologia
9.
JACC Cardiovasc Imaging ; 9(5): 505-15, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-26476503

RESUMO

OBJECTIVES: The goal of this study was to determine the prevalence of post-myocardial infarction (MI) left ventricular (LV) thrombus in the current era and to develop an effective algorithm (predicated on echocardiography [echo]) to discern patients warranting further testing for thrombus via delayed enhancement (DE) cardiac magnetic resonance (CMR). BACKGROUND: LV thrombus affects post-MI management. DE-CMR provides thrombus tissue characterization and is a well-validated but an impractical screening modality for all patients after an MI. METHODS: A same-day echo and CMR were performed according to a tailored protocol, which entailed uniform echo contrast (irrespective of image quality) and dedicated DE-CMR for thrombus tissue characterization. RESULTS: A total of 201 patients were studied; 8% had thrombus according to DE-CMR. All thrombi were apically located; 94% of thrombi occurred in the context of a left anterior descending (LAD) infarct-related artery. Although patients with thrombus had more prolonged chest pain and larger MI (p ≤ 0.01), only 18% had aneurysm on echo (cine-CMR 24%). Noncontrast (35%) and contrast (64%) echo yielded limited sensitivity for thrombus on DE-CMR. Thrombus was associated with stepwise increments in basal → apical contractile dysfunction on echo and quantitative cine-CMR; the echo-measured apical wall motion score was higher among patients with thrombus (p < 0.001) and paralleled cine-CMR decrements in apical ejection fraction and peak ejection rates (both p < 0.005). Thrombus-associated decrements in apical contractile dysfunction were significant even among patients with LAD infarction (p < 0.05). The echo-based apical wall motion score improved overall performance (area under the curve 0.89 ± 0.44) for thrombus compared with ejection fraction (area under the curve 0.80 ± 0.61; p = 0.01). Apical wall motion partitions would have enabled all patients with LV thrombus to be appropriately referred for DE-CMR testing (100% sensitivity and negative predictive value), while avoiding further testing in more than one-half (56% to 63%) of patients. CONCLUSIONS: LV thrombus remains common, especially after LAD MI, and can occur even in the absence of aneurysm. Although DE-CMR yielded improved overall thrombus detection, apical wall motion on a noncontrast echocardiogram can be an effective stratification tool to identify patients in whom DE-CMR thrombus assessment is most warranted. (Diagnostic Utility of Contrast Echocardiography for Detection of LV Thrombi Post ST Elevation Myocardial Infarction; NCT00539045).


Assuntos
Algoritmos , Ecocardiografia/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imagem Cinética por Ressonância Magnética , Infarto do Miocárdio/diagnóstico por imagem , Trombose/diagnóstico por imagem , Adulto , Idoso , Meios de Contraste/administração & dosagem , Feminino , Aneurisma Cardíaco/diagnóstico por imagem , Aneurisma Cardíaco/epidemiologia , Humanos , Masculino , Pessoa de Meia-Idade , Infarto do Miocárdio/epidemiologia , Infarto do Miocárdio/fisiopatologia , Valor Preditivo dos Testes , Prevalência , Prognóstico , Estudos Prospectivos , Encaminhamento e Consulta , Reprodutibilidade dos Testes , Volume Sistólico , Trombose/epidemiologia , Trombose/fisiopatologia , Procedimentos Desnecessários , Função Ventricular Esquerda
10.
Conf Proc IEEE Eng Med Biol Soc ; 2016: 1361-1364, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-28268578

RESUMO

This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new images. The classifier outputs are fused at pixel level to build probability maps which represent lesion saliency maps. In the next step, Otsu thresholding is applied to convert the saliency maps to binary masks, which determine the border of the lesions. We compared our proposed method with existing lesion segmentation methods proposed in the literature using two dermoscopy data sets (International Skin Imaging Collaboration and Pedro Hispano Hospital) which demonstrates the superiority of our method with Dice Coefficient of 0.91 and accuracy of 94%.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Neoplasias Cutâneas/diagnóstico por imagem , Pele/diagnóstico por imagem , Algoritmos , Bases de Dados Factuais , Dermoscopia/métodos , Humanos , Aprendizado de Máquina , Nevo/diagnóstico por imagem , Nevo/patologia , Pele/patologia , Neoplasias Cutâneas/patologia
11.
Biomed Res Int ; 2015: 367583, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25738153

RESUMO

CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST). An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within -1.6 ± 8.7 mL, -1.4 ± 7.8 mL, and 1.0 ± 5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle.


Assuntos
Ventrículos do Coração/diagnóstico por imagem , Processamento de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Miocárdio , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Radiografia
12.
Med Image Comput Comput Assist Interv ; 17(Pt 2): 487-95, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25485415

RESUMO

In this work, we present a framework for medical image modality recognition based on a fusion of both visual and text classification methods. Experiments are performed on the public ImageCLEF 2013 medical image modality dataset, which provides figure images and associated fulltext articles from PubMed as components of the benchmark. The presented visual-based system creates ensemble models across a broad set of visual features using a multi-stage learning approach that best optimizes per-class feature selection while simultaneously utilizing all available data for training. The text subsystem uses a pseudoprobabilistic scoring method based on detection of suggestive patterns, analyzing both the figure captions and mentions of the figures in the main text. Our proposed system yields state-of-the-art performance in all 3 categories of visual-only (82.2%), text-only (69.6%), and fusion tasks (83.5%).


Assuntos
Documentação/métodos , Interpretação de Imagem Assistida por Computador/métodos , Armazenamento e Recuperação da Informação/métodos , Processamento de Linguagem Natural , Reconhecimento Automatizado de Padrão/métodos , PubMed , Sistemas de Informação em Radiologia , Algoritmos , Inteligência Artificial , Imagem Multimodal/métodos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
13.
J Hypertens ; 31(10): 2069-76, 2013 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-24107735

RESUMO

OBJECTIVES: Left-ventricular mass (LVM) is widely used to guide clinical decision-making. Cardiac magnetic resonance (CMR) quantifies LVM by planimetry of contiguous short-axis images, an approach dependent on reader-selection of images to be contoured. Established methods have applied different binary cut-offs using circumferential extent of left-ventricular myocardium to define the basal left ventricle (LV), omitting images containing lesser fractions of left-ventricular myocardium. This study tested impact of basal slice variability on LVM quantification. METHODS: CMR was performed in patients and laboratory animals. LVM was quantified with full inclusion of left-ventricular myocardium, and by established methods that use different cut-offs to define the left-ventricular basal-most slice: 50% circumferential myocardium at end diastole alone (ED50), 50% circumferential myocardium throughout both end diastole and end systole (EDS50). RESULTS: One hundred and fifty patients and 10 lab animals were studied. Among patients, fully inclusive LVM (172.6±42.3g) was higher vs. ED50 (167.2±41.8g) and EDS50 (150.6±41.1g; both P<0.001). Methodological differences yielded discrepancies regarding proportion of patients meeting established criteria for left-ventricular hypertrophy and chamber dilation (P<0.05). Fully inclusive LVM yielded smaller differences with echocardiography (Δ=11.0±28.8g) than did ED50 (Δ=16.4±29.1g) and EDS50 (Δ=33.2±28.7g; both P<0.001). Among lab animals, ex-vivo left-ventricular weight (69.8±13.2g) was similar to LVM calculated using fully inclusive (70.1±13.5g, P=0.67) and ED50 (69.4±13.9g; P=0.70) methods, whereas EDS50 differed significantly (67.9±14.9g; P=0.04). CONCLUSION: Established CMR methods that discordantly define the basal-most LV produce significant differences in calculated LVM. Fully inclusive quantification, rather than binary cut-offs that omit basal left-ventricular myocardium, yields smallest CMR discrepancy with echocardiography-measured LVM and non-significant differences with necropsy-measured left-ventricular weight.


Assuntos
Ventrículos do Coração/patologia , Hipertrofia Ventricular Esquerda/patologia , Imagem por Ressonância Magnética , Infarto do Miocárdio/patologia , Miocárdio/patologia , Idoso , Diástole , Ecocardiografia , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Sístole
14.
Circ Cardiovasc Imaging ; 5(1): 137-46, 2012 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-22104165

RESUMO

BACKGROUND: Cardiac magnetic resonance (CMR) typically quantifies LV mass (LVM) by means of manual planimetry (MP), but this approach is time-consuming and does not account for partial voxel components--myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. METHODS AND RESULTS: LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (AS(PV)) and full voxel (AS(FV)) measurements. Methods were independently compared with LVM quantified on echocardiography (echo) and an ex vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time versus MP (0:21±0:04 versus 4:18±1:02 minutes; P<0.001). AS(FV) mass (136±35 g) was slightly lower than MP (139±35; Δ=3±9 g, P<0.001). Both methods yielded similar proportions of patients with LV remodeling (P=0.73) and hypertrophy (P=1.00). Regarding partial voxel segmentation, AS(PV) yielded higher LVM (159±38 g) than MP (Δ=20±10 g) and AS(FV) (Δ=23±6 g, both P<0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between AS(PV) and AS(FV) correlated with larger voxel size (partial r=0.37, P<0.001) even after controlling for LV chamber volume (r=0.28, P=0.002) and total LVM (r=0.19, P=0.03). Among patients, AS(PV) yielded better agreement with echo (Δ=20±25 g) than did AS(FV) (Δ=43±24 g) or MP (Δ=40±22 g, both P<0.001). Among laboratory animals, AS(PV) and ex vivo results were similar (Δ=1±3 g, P=0.3), whereas AS(FV) (6±3 g, P<0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy. CONCLUSIONS: Automated segmentation of myocardial partial voxels yields a 14-17% increase in LVM versus full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.


Assuntos
Algoritmos , Ventrículos do Coração/patologia , Hipertrofia Ventricular Esquerda/patologia , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Remodelação Ventricular , Animais , Cães , Feminino , Ventrículos do Coração/diagnóstico por imagem , Humanos , Hipertrofia Ventricular Esquerda/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Suínos , Ultrassonografia , Função Ventricular Esquerda
15.
NMR Biomed ; 24(7): 844-54, 2011 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-21834008

RESUMO

A generalized autocalibrating partially parallel acquisition (GRAPPA) method for radial k-space sampling is presented that calculates GRAPPA weights without synthesized or acquired calibration data. Instead, GRAPPA weights are fitted to the undersampled data as if they were the calibration data. Because the relative k-space shifts associated with these GRAPPA weights vary for a radial trajectory, new GRAPPA weights can be resampled for arbitrary shifts through interpolation, which are then used to generate missing projections between the acquired projections. The method is demonstrated in phantoms and in abdominal and brain imaging. Image quality is similar to radial GRAPPA using fully sampled calibration data, and improved relative to a previously described self-calibrated radial GRAPPA technique.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Adulto , Algoritmos , Encéfalo/anatomia & histologia , Mapeamento Encefálico/métodos , Calibragem , Feminino , Humanos , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Masculino , Pessoa de Meia-Idade , Imagens de Fantasmas , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Adulto Jovem
16.
J Cardiovasc Magn Reson ; 12: 46, 2010 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-20673372

RESUMO

OBJECTIVES: To examine relationships between severity of echocardiography (echo) -evidenced diastolic dysfunction (DD) and volumetric filling by automated processing of routine cine cardiovascular magnetic resonance (CMR). BACKGROUND: Cine-CMR provides high-resolution assessment of left ventricular (LV) chamber volumes. Automated segmentation (LV-METRIC) yields LV filling curves by segmenting all short-axis images across all temporal phases. This study used cine-CMR to assess filling changes that occur with progressive DD. METHODS: 115 post-MI patients underwent CMR and echo within 1 day. LV-METRIC yielded multiple diastolic indices - E:A ratio, peak filling rate (PFR), time to peak filling rate (TPFR), and diastolic volume recovery (DVR80 - proportion of diastole required to recover 80% stroke volume). Echo was the reference for DD. RESULTS: LV-METRIC successfully generated LV filling curves in all patients. CMR indices were reproducible (< or = 1% inter-reader differences) and required minimal processing time (175 +/- 34 images/exam, 2:09 +/- 0:51 minutes). CMR E:A ratio decreased with grade 1 and increased with grades 2-3 DD. Diastolic filling intervals, measured by DVR80 or TPFR, prolonged with grade 1 and shortened with grade 3 DD, paralleling echo deceleration time (p < 0.001). PFR by CMR increased with DD grade, similar to E/e' (p < 0.001). Prolonged DVR80 identified 71% of patients with echo-evidenced grade 1 but no patients with grade 3 DD, and stroke-volume adjusted PFR identified 67% with grade 3 but none with grade 1 DD (matched specificity = 83%). The combination of DVR80 and PFR identified 53% of patients with grade 2 DD. Prolonged DVR80 was associated with grade 1 (OR 2.79, CI 1.65-4.05, p = 0.001) with a similar trend for grade 2 (OR 1.35, CI 0.98-1.74, p = 0.06), whereas high PFR was associated with grade 3 (OR 1.14, CI 1.02-1.25, p = 0.02) DD. CONCLUSIONS: Automated cine-CMR segmentation can discern LV filling changes that occur with increasing severity of echo-evidenced DD. Impaired relaxation is associated with prolonged filling intervals whereas restrictive filling is characterized by increased filling rates.


Assuntos
Imagem Cinética por Ressonância Magnética , Infarto do Miocárdio/complicações , Disfunção Ventricular Esquerda/diagnóstico , Disfunção Ventricular Esquerda/fisiopatologia , Idoso , Automação , Diástole , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Infarto do Miocárdio/fisiopatologia , Índice de Gravidade de Doença , Disfunção Ventricular Esquerda/etiologia
17.
Magn Reson Med ; 63(5): 1230-7, 2010 May.
Artigo em Inglês | MEDLINE | ID: mdl-20432294

RESUMO

A respiratory and cardiac self-gated free-breathing three-dimensional cine steady-state free precession imaging method using multiecho hybrid radial sampling is presented. Cartesian mapping of the k-space center along the slice encoding direction provides intensity-weighted position information, from which both respiratory and cardiac motions are derived. With in plan radial sampling acquired at every pulse repetition time, no extra scan time is required for sampling the k-space center. Temporal filtering based on density compensation is used for radial reconstruction to achieve high signal-to-noise ratio and contrast-to-noise ratio. High correlation between the self-gating signals and external gating signals is demonstrated. This respiratory and cardiac self-gated, free-breathing, three-dimensional, radial cardiac cine imaging technique provides image quality comparable to that acquired with the multiple breath-hold two-dimensional Cartesian steady-state free precession technique in short-axis, four-chamber, and two-chamber orientations. Functional measurements from the three-dimensional cardiac short axis cine images are found to be comparable to those obtained using the standard two-dimensional technique.


Assuntos
Algoritmos , Técnicas de Imagem de Sincronização Cardíaca/métodos , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Imagem Cinética por Ressonância Magnética/métodos , Técnicas de Imagem de Sincronização Respiratória/métodos , Adulto , Feminino , Humanos , Masculino , Reprodutibilidade dos Testes , Mecânica Respiratória , Sensibilidade e Especificidade
18.
J Magn Reson Imaging ; 31(4): 845-53, 2010 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-20373428

RESUMO

PURPOSE: To evaluate the clinical performance of a novel automated left ventricle (LV) segmentation algorithm (LV-METRIC) that involves no geometric assumptions. MATERIALS AND METHODS: LV-METRIC and manual tracing (MT) were used independently to quantify LV volumes and LVEF (ejection fraction) for 151 consecutive patients who underwent cine-CMR (steady-state free precession). Phase contrast imaging was used to independently measure stroke volume. RESULTS: LV-METRIC was successful in all cases. Mean LVEF was within 1 point of MT (Delta 0.6 +/- 2.3%, P < 0.05), with smaller differences among patients with (0.5 +/- 2.5%) versus those without (0.9 +/- 2.3%; P = 0.01) advanced systolic dysfunction (LVEF

Assuntos
Ventrículos do Coração/patologia , Imagem por Ressonância Magnética/métodos , Miocárdio/patologia , Função Ventricular Esquerda , Adulto , Idoso , Algoritmos , Automação , Feminino , Ventrículos do Coração/anatomia & histologia , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes
19.
IEEE Trans Biomed Eng ; 57(4): 905-13, 2010 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-19203875

RESUMO

An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 +/- 6.2 mL (mean +/- standard deviation) and -0.9 +/- 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 +/- 6.9 mL and -1.0 +/- 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.


Assuntos
Algoritmos , Ventrículos do Coração/anatomia & histologia , Processamento de Imagem Assistida por Computador/métodos , Imagem Cinética por Ressonância Magnética/métodos , Modelos Cardiovasculares , Idoso , Volume Sanguíneo , Volume Cardíaco , Feminino , Coração/anatomia & histologia , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos
20.
Circ Cardiovasc Imaging ; 2(6): 476-84, 2009 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-19920046

RESUMO

BACKGROUND: Cardiac magnetic resonance (CMR) is established for assessment of left ventricular (LV) systolic function but has not been widely used to assess diastolic function. This study tested performance of a novel CMR segmentation algorithm (LV-METRIC) for automated assessment of diastolic function. METHODS AND RESULTS: A total of 101 patients with normal LV systolic function underwent CMR and echocardiography (echo) within 7 days. LV-METRIC generated LV filling profiles via automated segmentation of contiguous short-axis images (204+/-39 images, 2:04+/-0:53 minutes). Diastolic function by CMR was assessed via early:atrial filling ratios, peak diastolic filling rate, time to peak filling rate, and a novel index-diastolic volume recovery (DVR), calculated as percent diastole required for recovery of 80% stroke volume. Using an echo standard, patients with versus without diastolic dysfunction had lower early:atrial filling ratios, longer time to peak filling rate, lower stroke volume-adjusted peak diastolic filling rate, and greater DVR (all P<0.05). Prevalence of abnormal CMR filling indices increased in relation to clinical symptoms classified by New York Heart Association functional class (P=0.04) or dyspnea (P=0.006). Among all parameters tested, DVR yielded optimal performance versus echo (area under the curve: 0.87+/-0.04, P<0.001). Using a 90% specificity cutoff, DVR yielded 74% sensitivity for diastolic dysfunction. In multivariate analysis, DVR (odds ratio, 1.82; 95% CI, 1.13 to 2.57; P=0.02) was independently associated with echo-evidenced diastolic dysfunction after controlling for age, hypertension, and LV mass (chi(2)=73.4, P<0.001). CONCLUSIONS: Automated CMR segmentation can provide LV filling profiles that may offer insight into diastolic dysfunction. Patients with diastolic dysfunction have prolonged diastolic filling intervals, which are associated with echo-evidenced diastolic dysfunction independent of clinical and imaging variables.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Função Ventricular Esquerda/fisiologia , Idoso , Automação , Distribuição de Qui-Quadrado , Diástole , Ecocardiografia Doppler , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Curva ROC , Sensibilidade e Especificidade , Volume Sistólico
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