In all patients with ST-segment elevation myocardial infarction, risk stratification should be performed before discharge. The measurement of therapy efficiency with magnetic resonance imaging has ...been proposed as part of the risk assessment, but it has not been adopted widely. This meta-analysis was conducted to summarize published data on the prognostic value of the proportion of salvaged myocardium inside previously ischemic myocardium (myocardial salvage index) measured by T2-weighted and T1-weighted late gadolinium enhancement magnetic resonance imaging after ST-segment elevation myocardial infarction. Random and mixed effects models were used for analyzing the data of 10 studies with 2,697 patients. The pooled myocardial salvage index, calculated as the proportion of non-necrotic myocardium inside edematous myocardium measured by T2-weighted and T1-weighted late gadolinium enhancement MRI, was 43.0% (95% confidence interval: 37.4, 48.6). The pooled length of follow-up was 12.3 months (95% confidence interval: 7.0, 17.6). The pooled incidence of major cardiac events during follow-up, defined as cardiac death, nonfatal myocardial infarction, or admission for heart failure, was 10.6% (95% confidence interval: 5.7, 15.5). The applied mixed effects model showed an absolute decrease of 1.7% in the incidence of major cardiac events during follow-up (95% confidence interval: 1.6, 1.9) with every 1% of increase in the myocardial salvage index. The heterogeneity between studies was considerable (τ = 21.3). Analysis of aggregated follow-up data after ST-segment elevation myocardial infarction suggests that the myocardial salvage index measured by T2-weighted and T1-weighted late gadolinium enhancement magnetic resonance imaging provides prognostic information on the risk of major cardiac events, but considerable heterogeneity exists between studies.
Objective
To present a deep learning–based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network ...(CNN).
Methods
Two hundred patients with a total of 318 lesions for which histological correlation was available were analyzed. A novel CNN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted, apparent diffusion coefficient (ADC), diffusion-weighted images, and
K
-trans) and the effect of different sequences on the network’s performance was tested and discussed. The particular choice of modeling approach was justified by testing all relevant data combinations. The model was trained and validated using eightfold cross-validation.
Results
In terms of detection of significant prostate cancer defined by biopsy results as the reference standard, the 3D CNN achieved an area under the curve (AUC) of the receiver operating characteristics ranging from 0.89 (88.6% and 90.0% for sensitivity and specificity respectively) to 0.91 (81.2% and 90.5% for sensitivity and specificity respectively) with an average AUC of 0.897 for the ADC, DWI, and
K
-trans input combination. The other combinations scored less in terms of overall performance and average AUC, where the difference in performance was significant with a
p
value of 0.02 when using T2w and
K
-trans; and 0.00025 when using T2w, ADC, and DWI. Prostate cancer classification performance is thus comparable to that reported for experienced radiologists using the prostate imaging reporting and data system (PI-RADS). Lesion size and largest diameter had no effect on the network’s performance.
Conclusion
The diagnostic performance of the 3D CNN in detecting clinically significant prostate cancer is characterized by a good AUC and sensitivity and high specificity.
Key Points
• Prostate cancer classification using a deep learning model is feasible and it allows direct processing of MR sequences without prior lesion segmentation.
• Prostate cancer classification performance as measured by AUC is comparable to that of an experienced radiologist.
• Perfusion MR images (K-trans), followed by DWI and ADC, have the highest effect on the overall performance; whereas T2w images show hardly any improvement.
Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the ...prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools-DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. The network successfully detects the prostate region and segments the gland and its zones. Compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of Formula: see text% vs. Formula: see text %, and for the peripheral zone of 78.1± 2.5% vs. Formula: see text%. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones.
Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the ...clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.
Objectives
To introduce a novel hypothesis and method to characterise pathomechanisms underlying myocardial ischemia in chronic ischemic heart disease by local fractal analysis (FA) of the ischemic ...myocardial transition region in perfusion imaging.
Methods
Vascular mechanisms to compensate ischemia are regulated at various vascular scales with their superimposed perfusion pattern being hypothetically self-similar. Dedicated FA software (“FraktalWandler”) has been developed. Fractal dimensions during first-pass (FD
first-pass
) and recirculation (FD
recirculation
) are hypothesised to indicate the predominating pathomechanism and ischemic severity, respectively.
Results
Twenty-six patients with evidence of myocardial ischemia in 108 ischemic myocardial segments on magnetic resonance imaging (MRI) were analysed. The 40th and 60th percentiles of FD
first-pass
were used for pathomechanical classification, assigning lesions with FD
first-pass
≤ 2.335 to predominating coronary microvascular dysfunction (CMD) and ≥2.387 to predominating coronary artery disease (CAD). Optimal classification point in ROC analysis was FD
first-pass
= 2.358. FD
recirculation
correlated moderately with per cent diameter stenosis in invasive coronary angiography in lesions classified CAD (
r
= 0.472,
p
= 0.001) but not CMD (
r
= 0.082,
p
= 0.600).
Conclusions
The ischemic transition region may provide information on pathomechanical composition and severity of myocardial ischemia. FA of this region is feasible and may improve diagnosis compared to traditional noninvasive myocardial perfusion analysis.
Key Points
•
A novel hypothesis and method is introduced to pathophysiologically characterise myocardial ischemia.
•
The ischemic transition region appears a meaningful diagnostic target in perfusion imaging.
•
Fractal analysis may characterise pathomechanical composition and severity of myocardial ischemia.
Objectives
To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography ...(CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application.
Methods
A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science.
Results
Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis.
Conclusions
Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting.
Key Points
•
Fractal analysis of perfusion images can be successfully performed.
•
Tumour, pulmonary, myocardial, renal, skeletal muscle and cerebral perfusion have already been examined.
•
Clinical applications of fractal analysis include tumour and brain perfusion assessment.
•
Fractal analysis is a suitable method for quantifying perfusion heterogeneity.
•
Fractal analysis requires further research concerning the development of clinical applications.
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