T2* assessment of the three coronary artery territories of the left ventricular wall by different monoexponential truncation methods.

OBJECTIVES: This study aimed at evaluating left ventricular myocardial pixel-wise T2* using two truncation methods for different iron deposition T2* ranges and comparison of segmental T2* in different coronary artery territories. MATERIAL AND METHODS: Bright blood multi-gradient echo data of 30 patients were quantified by pixel-wise monoexponential T2* fitting with its R2 and SNR truncation. T2* was analyzed at different iron classifications. At low iron classification, T2* values were also analyzed by coronary artery territories. RESULTS: The right coronary artery has a significantly higher T2* value than the other coronary artery territories. No significant difference was found in classifying severe iron by the two truncation methods in any myocardial region, whereas in moderate iron, it is only apparent at septal segments. The R2 truncation produces a significantly higher T2* value than the SNR method when low iron is indicated. CONCLUSION: Clear T2* differentiation between the three coronary territories by the two truncation methods is demonstrated. The two truncation methods can be used interchangeably in classifying severe and moderate iron deposition at the recommended septal region. However, in patients with low iron indication, different results by the two truncation methods can mislead the investigation of early iron level progression.

Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters. MATERIALS AND METHODS: We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. RESULTS: DSC of RV segmentation of the RLA method exhibited a higher value than those of the conventional SA methods (0.84 vs. 0.61). Correlation coefficient with respect to manual RV strain measurements in the fully automatic RLA were superior to those in SA measurements (0.5-0.7 vs. 0.1-0.2). DISCUSSION: Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.

An improved method for automated calculation of the water-equivalent diameter for estimating size-specific dose in CT.

PURPOSE: The aim of this study is to propose an algorithm for the automated calculation of water-equivalent diameter (Dw ) and size-specific dose estimation (SSDE) from clinical computed tomography (CT) images containing one or more substantial body part. METHODS: All CT datasets were retrospectively acquired by the Toshiba Aquilion 128 CT scanner. The proposed algorithm consisted of a contouring stage for the Dw calculation, carried out by taking the six largest objects in the cross-sectional image of the patient's body, followed by the removal of the CT table depending on the center position (y-axis) of each object. Validation of the proposed algorithm used images of patients who had undergone chest examination with both arms raised up, one arm placed down and both arms placed down, images of the pelvic region consisting of one substantial object, and images of the lower extremities consisting of two separated areas. RESULTS: The proposed algorithm gave the same results for Dw and SSDE as the previous algorithm when images consisted of one substantial body part. However, when images consisted of more than one substantial body part, the new algorithm was able to detect all parts of the patient within the image. The Dw values from the proposed algorithm were 9.5%, 15.4%, and 39.6% greater than the previous algorithm for the chest region with one arm placed down, both arms placed down, and images with two legs, respectively. The SSDE values from the proposed algorithm were 8.2%, 11.2%, and 20.6% lower than the previous algorithm for the same images, respectively. CONCLUSIONS: We have presented an improved algorithm for automated calculation of Dw and SSDE. The proposed algorithm is more general and gives accurate results for both Dw and SSDE whether the CT images contain one or more than one substantial body part.

Cardiac T2 * mapping: Techniques and clinical applications.

Cardiac T2 * mapping is a noninvasive MRI method that is used to identify myocardial iron accumulation in several iron storage diseases such as hereditary hemochromatosis, sickle cell disease, and ß-thalassemia major. The method has improved over the years in terms of MR acquisition, focus on relative artifact-free myocardium regions, and T2 * quantification. Several improvement factors involved include blood pool signal suppression, the reproducibility of T2 * measurement as affected by scanner hardware, and acquisition software. Regarding the T2 * quantification, improvement factors include the applied curve-fitting method with or without truncation of the signals acquired at longer echo times and whether or not T2 * measurement focuses on multiple segmental regions or the midventricular septum only. Although already widely applied in clinical practice, data processing still differs between centers, contributing to measurement outcome variations. State of the art T2 * measurement involves pixelwise quantification providing better spatial iron loading information than region of interest-based quantification. Improvements have been proposed, such as on MR acquisition for free-breathing mapping, the generation of fast mapping, noise reduction, automatic myocardial contour delineation, and different T2 * quantification methods. This review deals with the pro and cons of different methods used to quantify T2 * and generate T2 * maps. The purpose is to recommend a combination of MR acquisition and T2 * mapping quantification techniques for reliable outcomes in measuring and follow-up of myocardial iron overload. The clinical application of cardiac T2 * mapping for iron overload's early detection, monitoring, and treatment is addressed. The prospects of T2 * mapping combined with different MR acquisition methods, such as cardiac T1 mapping, are also described. Level of Evidence: 4 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019.

Early detection of heart function abnormality by native T1: a comparison of two T1 quantification methods.

OBJECTIVE: To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods. METHODS: Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed. RESULTS: Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001-0.040) by median quantification compared with six (p < 0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053). CONCLUSION: Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment. KEY POINTS: • Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points. • Median quantification is more sensitive to early heart function abnormality compared with mean quantification. • The new method yields significant native T1 value differentiation between the three coronary artery territories.

Semi-automated myocardial segmentation of bright blood multi-gradient echo images improves reproducibility of myocardial contours and T2* determination.

OBJECTIVES: Early detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing. MATERIALS AND METHODS: Fifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment. RESULTS: Compared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%). CONCLUSION: Application of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.

Contrast-optimized composite image derived from multigradient echo cardiac magnetic resonance imaging improves reproducibility of myocardial contours and T2* measurement.

OBJECTIVES: Reproducibility of myocardial contour determination in cardiac magnetic resonance imaging is important, especially when determining T2* values per myocardial segment as a prognostic factor of heart failure or thalassemia. A method creating a composite image with contrasts optimized for drawing myocardial contours is introduced and compared with the standard method on a single image. MATERIALS AND METHODS: A total of 36 short-axis slices from bright-blood multigradient echo (MGE) T2* scans of 21 patients were acquired at eight echo times. Four observers drew free-hand myocardial contours on one manually selected T2* image (method 1) and on one image composed by blending three images acquired at TEs providing optimum contrast-to-noise ratio between the myocardium and its surrounding regions (method 2). RESULTS: Myocardial contouring by method 2 met higher interobserver reproducibility than method 1 (P < 0.001) with smaller Coefficient of variance (CoV) of T2* values in the presence of myocardial iron accumulation (9.79 vs. 15.91%) and in both global myocardial and mid-ventricular septum regions (12.29 vs. 16.88 and 5.76 vs. 8.16%, respectively). CONCLUSION: The use of contrast-optimized composite images in MGE data analysis improves reproducibility of myocardial contour determination, leading to increased consistency in the calculated T2* values enhancing the diagnostic impact of this measure of iron overload.

Influence of the choice of software package on the outcome of semiquantitative MR myocardial perfusion analysis.

PURPOSE: To assess the repeatability and reproducibility of semiquantitative magnetic resonance (MR) perfusion analysis performed by using different software packages. MATERIALS AND METHODS: The study protocol was approved by the institutional ethics committee. Informed consent was obtained from each patient. Semiquantitative perfusion analysis was performed twice by two independent observers using four dedicated software packages. MR perfusion datasets originated from eight patients with known single-vessel disease who were scheduled for percutaneous coronary intervention (PCI) on the basis of coronary angiography findings. Each patient underwent two examinations: 1 day before and 1 day after PCI. Repeatability (intra- and interobserver agreements) and reproducibility (intersoftware agreement) were evaluated for perfusion upslope and myocardial perfusion reserve index with Student t test and Bland-Altman analyses. RESULTS: Intra- and interobserver agreements were good and comparable for repeated measurements within each individual software platform (mean differences < 6%, intraclass correlation coefficient [ICC] ≥ 0.68). However, the intersoftware variability was significant (limits of agreement ≥ 65%, ICC ≤ 0.67) such that the values produced with the different software packages are not interchangeable. CONCLUSION: The results indicate high repeatability within individual software but low reproducibility between different software packages, suggesting that within-group and/or sequential observation of semiquantitative perfusion parameters must be performed with the same software platform. Before semiquantitative perfusion analysis can be incorporated reliably into clinical studies, it is important to resolve the differences between the software packages.

Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation.

Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the noninvasive assessment of cardiac functions. Deep neural networks have achieved considerable progress in overcoming various challenges in cine MRI analysis. However, deep learning models cannot be used for classification because limited cine MRI data are available. To overcome this problem, features from cine image settings are derived by handcrafting and addition of other clinical features to the classical machine learning approach for ensuring the model fits the MRI device settings and image parameters required in the analysis. In this study, a novel method was proposed for classifying heart disease (cardiomyopathy patient groups) using only segmented output maps. In the encoder-decoder network, the fully convolutional EfficientNetB5-UNet was modified to perform the semantic segmentation of the MRI image slice. A two-dimensional thickness algorithm was used to combine the segmentation outputs for the 2D representation of images of the end-diastole (ED) and end-systole (ES) cardiac volumes. The thickness images were subsequently used for classification by using a few-shot model with an adaptive subspace classifier. Model performance was verified by applying the model to the 2017 MICCAI Medical Image Computing and Computer-Assisted Intervention dataset. High segmentation performance was achieved as follows: the average Dice coefficients of segmentation were 96.24% (ED) and 89.92% (ES) for the left ventricle (LV); the values for the right ventricle (RV) were 92.90% (ED) and 86.92% (ES). The values for myocardium were 88.90% (ED) and 90.48% (ES). An accuracy score of 92% was achieved in the classification of various cardiomyopathy groups without clinical features. A novel rapid analysis approach was proposed for heart disease diagnosis, especially for cardiomyopathy conditions using cine MRI based on segmented output maps.

Impact of ROI Size on the Accuracy of Noise Measurement in CT on Computational and ACR Phantoms.

Background: The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied. Objective: This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms. Material and Methods: In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3 × 3 to 41 × 41 pixels, with an increased size of 2 × 2 pixels. Results: The use of a minimum ROI size of 21 × 21 pixels shows noise in the range of ± 5% ground truth noise. The measured noise increases above the ± 5% range if the used ROI is smaller than 21 × 21 pixels. Conclusion: A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21 × 21 pixels.

Intermodel agreement of myocardial blood flow estimation from stress-rest myocardial perfusion magnetic resonance imaging in patients with coronary artery disease.

OBJECTIVES: The aim of this study was to assess the intermodel agreement of different magnetic resonance myocardial perfusion models and evaluate their correspondence to stenosis diameter. MATERIALS AND METHODS: In total, 260 myocardial segments were analyzed from rest and adenosine stress first-pass myocardial perfusion magnetic resonance images (1.5 T, 0.050 ± 0.005 mmol/kg body weight gadolinium; 122 segments in rest, 138 in stress) in 10 patients with suspected or known coronary artery disease. Signal intensity curves were calculated per myocardial segment, of which the contours were traced with QMASS MR V.7.6 (Medis, Leiden, the Netherlands), and exported to Matlab. Myocardial blood flow quantification was performed with distributed parameter, extended Toft, Patlak, and Fermi parametric models (in-house programs; Matlab R2013a; Mathworks Inc, Natick, MA). Modeling was applied after the signal intensity curves were corrected for spatial magnetic field inhomogeneity and contrast saturation. Overall and grouped perfusion values based on presence of coronary stenosis (>50% diameter reduction) at coronary computed tomography angiography at second generation dual-source computed tomography were compared between the perfusion models. RESULTS: Rest and stress myocardial perfusion estimates for all models were significantly related to each other (P < 0.001). The highest correlation coefficients were found between the extended Toft and Fermi models (R = 0.89-0.91) and low correlation coefficients between the distributed parameter and Patlak models (R = 0.66-0.68). The models resulted in significantly different perfusion estimates in stress (P = 0.03), but not in rest (P = 0.74). The differences in perfusion estimates in stress were caused by differences between the distributed parameter and Patlak models and between the Patlak and Fermi models (both P < 0.001). Significantly lower perfusion estimates were found for myocardial segments subtended by coronary arteries with versus without significant stenosis, but only for estimations produced by the extended Toft model (P = 0.04) and Fermi model (P = 0.01). There were no significant differences in rest perfusion values between models. CONCLUSIONS: Quantitative myocardial perfusion values in stress depend on the modeling method used to calculate the perfusion estimate. The difference in myocardial perfusion estimate with or without stenosis in the subtending coronary artery is most pronounced when the extended Toft or Fermi model is used.