A framework to measure myocardial extracellular volume fraction using dual-phase low dose CT images

Reproducibility of myocardial extracellular volume quantification using dual-energy computed tomography in patients with cardiac amyloidosis

BACKGROUND

Quantifying myocardial extracellular volume (ECV) using computed tomography (CT) has been shown to be useful in the evaluation of cardiac amyloidosis. However, the reproducibility of CT measurements for myocardial ECV, is not well-established in patients with proven cardiac amyloidosis.

METHODS

This prospective single-center study enrolled cardiac amyloidosis patients to undergo dual-energy CT for myocardial fibrosis assessment. Delayed imaging at 7 and 8 ​min post-contrast and independent evaluations by two blinded cardiologists were performed for ECV quantification using 16-segment (ECVglobal) and septal sampling (ECVseptal). Inter- and intraobserver variability and test-retest reliability were measured using Spearman's rank correlation, Bland-Altman analysis, and intraclass correlation coefficients (ICC).

RESULTS

Among the 24 participants (median age ​= ​78, 67 ​% male), CT ECVglobal and ECVseptal showed median values of 53.6 ​% and 49.1 ​% at 7 ​min, and 53.3 ​% and 50.1 ​% at 8 ​min, respectively. Inter- and intraobserver variability and test-retest reliability for CT ECVglobal (ICC ​= ​0.798, 0.912, and 0.894, respectively) and ECVseptal (ICC ​= ​0.791, 0.898, and 0.852, respectively) indicated good reproducibility, with no evidence of systemic bias between observers or scans.

CONCLUSIONS

Dual-energy CT-derived ECV measurements demonstrated good reproducibility in patients with proven cardiac amyloidosis, suggesting potential utility as a repeatable imaging biomarker for this disease.

CT for the evaluation of myocardial extracellular volume with MRI as reference: a systematic review and meta-analysis

OBJECTIVE

Myocardial extracellular volume (ECV) fraction is an important imaging biomarker in clinical decision-making. CT-ECV is a potential alternative to MRI for ECV quantification. We conducted a meta-analysis to comprehensively assess the reliability of CT for ECV quantification with MRI as a reference.

METHODS

We systematically searched PubMed, EMBASE, and the Cochrane Library for relevant articles published since the establishment of the database in July 2022. The articles comparing CT-ECV with MRI as a reference were included. Meta-analytic methods were applied to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient (r) between CT-ECV and MRI-ECV.

RESULTS

Seventeen studies with a total of 459 patients and 2231 myocardial segments were included. The pooled mean difference (MD), LOA, and r for ECV quantification at the per-patient level was (0.07%; 95% LOA: - 0.42 to 0.55%) and 0.89 (95% CI: 0.86-0.91), respectively, while on the per-segment level was (0.44%; 95% LOA: 0.16-0.72%) and 0.84 (95% CI: 0.82-0.85), respectively. The pooled r from studies with the ECViodine method for ECV quantification was significantly higher compared to those with the ECVsub method (0.94 (95% CI: 0.91-0.96) vs. 0.84 (95% CI: 0.80-0.88), respectively, p = 0.03). The pooled r from septal segments was significantly higher than those from non-septal segments (0.88 (95% CI: 0.86-0.90) vs. 0.76 (95% CI: 0.71-0.90), respectively, p = 0.009).

CONCLUSION

CT showed a good agreement and excellent correlation with MRI for ECV quantification and is a potentially attractive alternative to MRI.

CLINICAL RELEVANCE STATEMENT

The myocardial extracellular volume fraction can be acquired using a CT scan, which is not only a viable alternative to myocardial extracellular volume fraction derived from MRI but is also less time-consuming and costly for patients.

KEY POINTS

• Noninvasive CT-ECV is a viable alternative to MRI-ECV for ECV quantification. • CT-ECV using the ECViodine method showed more accurate myocardial ECV quantification than ECVsub. • Septal myocardial segments showed lower measurement variability than non-septal segments for the ECV quantification.

Cardiac Computed Tomography for Quantification of Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis

BACKGROUND

Extracellular volume (ECV) is a quantitative measure of extracellular compartment expansion, and an increase in ECV is a marker of myocardial fibrosis. Although cardiac magnetic resonance (CMR) is considered the standard imaging tool for ECV quantification, cardiac computed tomography (CT) has also been used for ECV assessment.

OBJECTIVES

The aim of this meta-analysis was to evaluate the correlation and agreement in the quantification of myocardial ECV by CT and CMR.

METHODS

PubMed and Web of Science were searched for relevant publications reporting on the use of CT for ECV quantification compared with CMR as the reference standard. The authors employed a meta-analysis using the restricted maximum-likelihood estimator with a random-effects method to estimate summary correlation and mean difference. A subgroup analysis was performed to compare the correlation and mean differences between single-energy CT (SECT) and dual-energy CT (DECT) techniques for the ECV quantification.

RESULTS

Of 435 papers, 13 studies comprising 383 patients were identified. The mean age range was 57.3 to 82 years, and 65% of patients were male. Overall, there was an excellent correlation between CT-derived ECV and CMR-derived ECV (mean: 0.90 [95% CI: 0.86-0.95]). The pooled mean difference between CT and CMR was 0.96% (95% CI: 0.14%-1.78%). Seven studies reported correlation values using SECT, and 4 studies reported those using DECT. The pooled correlation from studies utilizing DECT for ECV quantification was significantly higher compared with those with SECT (mean: 0.94 [95% CI: 0.91-0.98] vs 0.87 [95% CI: 0.80-0.94], respectively; P = 0.01). There was no significant difference in pooled mean differences between SECT vs DECT (P = 0.85).

CONCLUSIONS

CT-derived ECV showed an excellent correlation and mean difference of <1% with CMR-derived ECV. However, the overall quality of the included studies was low, and larger, prospective studies are needed to examine the accuracy and diagnostic and prognostic utility of CT-derived ECV.

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R² = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R² = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.