Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients

Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach's alpha (C - α ) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations. Results: Myocardial classification results against ground-truth were Dice = 0.89 , APD = 2.4    mm , and accuracy = 97 % for the validation set and Dice = 0.90 , APD = 2.5    mm , and accuracy = 97 % for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were CI = - 1.36 % to 2.42%, p-value < 0.001 for LVEF (58 ± 5 % versus 57 ± 6 % ), and CI = - 0.71 % to 0.63%, p-value < 0.001 for longitudinal strain (- 15 ± 2 % versus - 15 ± 3 % ). Conclusions: The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.

Can global longitudinal strain (GLS) with magnetic resonance prognosticate early cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients, a prospective study?

PURPOSE

To determine if Artificial Intelligence-based computation of global longitudinal strain (GLS) from left ventricular (LV) MRI is an early prognostic factor of cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients. The main hypothesis based on the patients receiving antineoplastic chemotherapy treatment was CTRCD risk analysis with GLS that was independent of LV ejection fraction (LVEF).

METHODS

Displacement Encoding with Stimulated Echoes (DENSE) MRI was acquired on 32 breast cancer patients at baseline and 3- and 6-month follow-ups after chemotherapy. Two DeepLabV3+ Fully Convolutional Networks (FCNs) were deployed to automate image segmentation for LV chamber quantification and phase-unwrapping for 3D strains, computed with the Radial Point Interpolation Method. CTRCD risk (cardiotoxicity and adverse cardiac events) was analyzed with Cox Proportional Hazards (PH) models with clinical and contractile prognostic factors.

RESULTS

GLS worsened from baseline to the 3- and 6-month follow-ups (-19.1 ± 2.1%, -16.0 ± 3.1%, -16.1 ± 3.0%; P < 0.001). Univariable Cox regression showed the 3-month GLS significantly associated as an agonist (hazard ratio [HR]-per-SD: 2.1; 95% CI: 1.4-3.1; P < 0.001) and LVEF as a protector (HR-per-SD: 0.8; 95% CI: 0.7-0.9; P = 0.001) for CTRCD occurrence. Bivariable regression showed the 3-month GLS (HR-per-SD: 2.0; 95% CI: 1.2-3.4; P = 0.01) as a CTRCD prognostic factor independent of other covariates, including LVEF (HR-per-SD: 1.0; 95% CI: 0.9-1.2; P = 0.9).

CONCLUSIONS

The end-point analyses proved the hypothesis that GLS is an early, independent prognosticator of incident CTRCD risk. This novel GLS-guided approach to CTRCD risk analysis could improve antineoplastic treatment with further validation in a larger clinical trial.

Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network

This study's purpose was to develop a direct MRI-based, deep-learning semantic segmentation approach for computing global longitudinal strain (GLS), a known metric for detecting left-ventricular (LV) cardiotoxicity in breast cancer. Displacement Encoding with Stimulated Echoes cardiac image phases acquired from 30 breast cancer patients and 30 healthy females were unwrapped via a DeepLabV3 + fully convolutional network (FCN). Myocardial strains were directly computed from the unwrapped phases with the Radial Point Interpolation Method. FCN-unwrapped phases of a phantom's rotating gel were validated against quality-guided phase-unwrapping (QGPU) and robust transport of intensity equation (RTIE) phase-unwrapping. FCN performance on unwrapping human LV data was measured with F1 and Dice scores versus QGPU ground-truth. The reliability of FCN-based strains was assessed against RTIE-based strains with Cronbach's alpha (C-α) intraclass correlation coefficient. Mean squared error (MSE) of unwrapping the phantom experiment data at 0 dB signal-to-noise ratio were 1.6, 2.7 and 6.1 with FCN, QGPU and RTIE techniques. Human data classification accuracies were F1 = 0.95 (Dice = 0.96) with FCN and F1 = 0.94 (Dice = 0.95) with RTIE. GLS results from FCN and RTIE were -16 ± 3% vs. -16 ± 3% (C-α = 0.9) for patients and -20 ± 3% vs. -20 ± 3% (C-α = 0.9) for healthy subjects. The low MSE from the phantom validation demonstrates accuracy of phase-unwrapping with the FCN and comparable human subject results versus RTIE demonstrate GLS analysis accuracy. A deep-learning methodology for phase-unwrapping in medical images and GLS computation was developed and validated in a heterogeneous cohort.

Abstract P345: A MRI Displacement-based Deep-learning Semantic Segmentation Tool For Left-ventricular Longitudinal Strain Analysis In Cardiotoxicity

Global longitudinal strain (GLS) computed in the left-ventricle (LV) is an established metric for detecting cardiotoxicity in breast cancer patients treated with antineoplastic agents. The purpose of this study was to develop a novel, MRI-based, deep-learning semantic segmentation tool that automates the phase-unwrapping for LV displacement computation in GLS. Strain analysis via phase-unwrapping was conducted on 30 breast cancer patients and 30 healthy females acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence. A ResNet-50 deep convolutional neural network (DCNN) architecture for automated phase-unwrapping, a previously validated ResNet-50 DCNN for chamber quantification and the Radial Point Interpolation Method were used for GLS computation (Figure 1). The DCNN's performance was measured with F1 and Dice scores, and validated in comparison to the robust transport of intensity equation (RTIE) and quality guided phase-unwrapping (QGPU) conventional algorithms. The three techniques were compared by intraclass correlation coefficient with Cronbach’s alpha (C-alpha) index. Classification accuracy with the DCNN was F1 score of 0.92 and Dice score of 0.89. The GLS results from RTIE, QGPU and DCNN were -16.0 ± 2%, -16.1 ± 3% and -15.9 ± 3% (C-alpha = 0.89) for patients and -18.9 ± 3%, -19.0 ± 4% and -18.9 ± 3% (C-alpha = 0.92) for healthy subjects. Comparable validation results from the three techniques show the feasibility of a DCNN-based approach to LV displacement and GLS analysis. The dissimilarities between patients and healthy subjects demonstrate that DCNN-based GLS computation may detect LV abnormalities related to cardiotoxicity.

Life during the pandemic: an international photo-elicitation study with medical students

BACKGROUND

The SARS-CoV-2 (COVID-19) pandemic is a global event with unprecedented impact on individuals and communities around the world. The purpose of this study is to use a modified photo-elicitation methodology to examine the impact of the COVID-19 pandemic on the lives of medical students and their communities around the world.

METHODS

Participating medical students were asked to take photographs for 14 days. In lieu of an interview, which is customary for photo-elicitation projects, participants were asked to share a reflection (a paragraph or two) for each photograph they contributed to the study.

RESULTS

Between April 27th, 2020 and May 11th, 2020 26 students from 19 medical schools across 13 countries shared photographs and reflections. Qualitative analysis of written reflections revealed that medical students felt the impact of the pandemic on several levels 1) individual, 2) interpersonal, 3) educational, and 4) societal.

CONCLUSIONS

The COVID-19 pandemic has impacted the lives of medical students on multiple levels. As individuals, students felt emotional distress but found resilience through physical activity and the establishment of new routines. Many students felt isolated as their interpersonal relationships were confined due to social distancing measures. These feelings could be combated with new educational initiatives focused on group collaboration. Lastly, students reflecting on the larger societal implications were concerned with the economic ramifications of the virus and its impact on their future. This study brought together students from several different countries to engage in an applied learning program as a model for equitable global health research.

Society for Cardiovascular Magnetic Resonance 2019 Case of the Week series

The Society for Cardiovascular Magnetic Resonance (SCMR) is an international society focused on the research, education, and clinical application of cardiovascular magnetic resonance (CMR). The SCMR web site ( https://www.scmr.org ) hosts a case series designed to present case reports demonstrating the unique attributes of CMR in the diagnosis or management of cardiovascular disease. Each clinical presentation is followed by a brief discussion of the disease and unique role of CMR in disease diagnosis or management guidance. By nature, some of these are somewhat esoteric, but all are instructive. In this publication, we provide a digital archive of the 2019 Case of the Week series as a means of further enhancing the education of those interested in CMR and as a means of more readily identifying these cases using a PubMed or similar search engine.

Abstract 224: A Deep Learning Approach to Left-Ventricular Chamber Quantification for Fully Automated Three Dimensional Strain Analysis in Cardiotoxicity

This study investigated MRI and semantic segmentation-based deep-learning (SSDL) automation for left-ventricular chamber quantifications (LVCQ) and low longitudinal strain (LLS) determination, thus eliminating user-bias by providing an automated tool to detect cardiotoxicity (CT) in breast cancer patients treated with antineoplastic agents. Displacement Encoding with Stimulated Echoes-based (DENSE) myocardial images from 26 patients were analyzed with the tool’s Convolution Neural Network with underlying Resnet-50 architecture. Quantifications based on the SSDL tool’s output were for LV end-diastolic diameter (LVEDD), ejection fraction (LVEF), and mass (LVM) (see figure for phase sequence). LLS was analyzed with Radial Point Interpolation Method (RPIM) with DENSE phase-based displacements. LVCQs were validated by comparison to measurements obtained with an existing semi-automated vendor tool (VT) and strains by 2 independent users employing Bland-Altman analysis (BAA) and interclass correlation coefficients estimated with Cronbach’s Alpha (C-Alpha) index. F1 score for classification accuracy was 0.92. LVCQs determined by SSDL and VT were 4.6 ± 0.5 vs 4.6 ± 0.7 cm (C-Alpha = 0.93 and BAA = 0.5 ± 0.5 cm) for LVEDD, 58 ± 5 vs 58 ± 6 % (0.90, 1 ± 5%) for LVEF, 119 ± 17 vs 121 ± 14 g (0.93, 5 ± 8 g) for LV mass, while LLS was 14 ± 4 vs 14 ± 3 % (0.86, 0.2 ± 6%). Hence, equivalent LV dimensions, mass and strains measured by VT and DENSE imaging validate our unique automated analytic tool. Longitudinal strains in patients can then be analyzed without user bias to detect abnormalities for the indication of cardiotoxicity and the need for therapeutic intervention even if LVEF is not affected.

Fully automated and comprehensive MRI-based left-ventricular contractility analysis in post-chemotherapy breast cancer patients

OBJECTIVE

This study investigated the occurrence of cardiotoxicity-related left-ventricular (LV) contractile dysfunction in breast cancer patients following treatment with antineoplastic chemotherapy agents.

METHODS

A validated and automated MRI-based LV contractility analysis tool consisting of quantization-based boundary detection, unwrapping of image phases and the meshfree Radial Point Interpolation Method was used toward measuring LV chamber quantifications (LVCQ), three-dimensional strains and torsions in patients and healthy subjects. Data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence on 21 female patients and 21 age-matched healthy females. Estimates of patient LVCQs from DENSE acquisitions were validated in comparison to similar steady-state free precession measurements and their strain results validated via Bland-Altman interobserver agreements. The occurrence of LV abnormalities was investigated via significant differences in contractility measurements (LVCQs, strains and torsions) between patients and healthy subjects.

RESULTS

Repeated measures analysis showed similarities between LVCQ measurements from DENSE and steady-state free precession, including cardiac output (4.7 ± 0.4 L, 4.6 ± 0.4 L, p = 0.8), and LV ejection fractions (59±6%, 58±5%, p = 0.2). Differences found between patients and healthy subjects included enlarged basal diameter (5.0 ± 0.5 cm vs 4.4 ± 0.5 cm, p < 0.01), apical torsion (6.0 ± 1.1° vs 9.7 ± 1.4°, p < 0.001) and global longitudinal strain (-0.15 ± 0.02 vs. -0.21 ± 0.04, p < 0.001), but not LV ejection fraction (59±6% vs. 63±6%, p = 0.1).

CONCLUSION

The results from the statistical analysis reveal the possibility of LV abnormalities in the post-chemotherapy patients via enlarged basal diameter and reduced longitudinal strain and torsion, in comparison to healthy subjects.

ADVANCES IN KNOWLEDGE

This study shows that subclinical LV abnormalities in post-chemotherapy breast cancer patients can be detected with an automated technique for the comprehensive analysis of contractile parameters.

Abstract 174: Investigating Subclinical Cardiotoxicity in Long-term Breast Cancer Survivors Following Chemotherapy With DENSE-based 3D Strain Analysis

This study investigated if left ventricular (LV) mechanical contractile parameters in addition to LV ejection fraction (LVEF) indicated cardiac remodeling and fibrosis in patients exposed to cardiotoxic chemotherapy agents (CCA). Cardiac deformation data were obtained using single-scan acquisitions with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence. Contractile analysis consisted of automated myocardial boundary detection and unwrapping 3D DENSE phase images for intra-myocardial displacements, and followed by analyzing torsion and 3D strains with the meshfree Radial Point Interpolation Method (RPIM). Data were acquired on 13 CCA-exposed patients who were undergoing chemotherapy and/or care for cardiac complications. DENSE LVEF measurements in patients were validated against steady-state free precession (SSFP) MRI data, all contractility computations were compared to healthy subjects and Bland-Altman agreements established between strains computed by independent observers. A significant difference was not found between DENSE and SSFP LVEF computations (52 ± 11% vs 48 ± 15%, p=0.33). Significant differences were seen with enlarged LV diameters in patients versus healthy subjects (6.0 ± 1.1 cm vs 4.9 ± 0.7 cm, p<0.001) and similarly for torsion and longitudinal strain but not in LVEF results (p > 0.05). Bland-Altman agreements were 0.01 ± 0.06 for longitudinal strain and 0.1 ± 1.9 ° for torsion. Statistical analysis confirm changed LV size and function in patients and indicate remodeling that is otherwise not demonstrated by LVEF measurements, which may precede clinically apparent heart failure development and worsened morbidity.

Can post-chemotherapy cardiotoxicity be detected in long-term survivors of breast cancer via comprehensive 3D left-ventricular contractility (strain) analysis?

PURPOSE

This study applied a novel and automated contractility analysis tool to investigate possible cardiotoxicity-related left-ventricular (LV) dysfunction in breast cancer patients following treatment with anti-neoplastic chemotherapy agents (CTA). Subclinical dysfunction otherwise undetected via LV ejection fraction (LVEF) was determined.

METHODS

Deformation data were acquired with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence on 16 female patients who had CTA-based treatment. The contractility analysis tool consisting of image quantization-based boundary detection and the meshfree Radial Point Interpolation Method was used to compare chamber quantifications, 3D regional strains and torsion between patients and healthy subjects (N = 26 females with N = 14 age-matched). Quantifications of patient LVEFs from DENSE and Steady-State Free Precession (SSFP) acquisitions were compared, Bland-Altman interobserver agreements measured on their strain results and differences in contractile parameters with healthy subjects determined via Student's t-tests.

RESULTS

A significant difference was not found between DENSE and SSFP-based patient LVEFs at 58 ± 7% vs 57 ± 9%, p = 0.6. Bland-Altman agreements were - 0.01 ± 0.05 for longitudinal strain and 0.1 ± 1.3° for torsion. Differences in basal diameter indicating enlargement, 5.2 ± 0.5 cm vs 4.5 ± 0.5 cm, p < 0.01, and torsion, 4.7 ± 1.0° vs 8.1 ± 1.1°, p < 0.001 in the mid-ventricle and 5.9 ± 1.2° vs 10.2 ± 0.9°, p < 0.001 apically, were seen between patients and age-matched healthy subjects and similarly in longitudinal strain, but not in LVEF.

CONCLUSIONS

Results from the statistical analysis reveal the likelihood of LV remodeling in this patient subpopulation otherwise not indicated by LVEF measurements.

Swelling of Transported Smoke from Savanna fires over the Southeast Atlantic Ocean

We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.

Introduction to a mechanism for automated myocardium boundary detection with displacement encoding with stimulated echoes (DENSE)

Objective:

Displacement ENcoding with Stimulated Echoes (DENSE) is an MRI technique developed to encode phase related to myocardial tissue displacements, and the displacement information directly applied towards detecting left-ventricular (LV) myocardial motion during the cardiac cycle. The purpose of this study is to present a novel, three-dimensional (3D) DENSE displacement-based and magnitude image quantization-based, semi-automated detection technique for myocardial wall motion, whose boundaries are used for rapid and automated computation of 3D myocardial strain.

Methods:

The architecture of this boundary detection algorithm is primarily based on pixelwise spatiotemporal increments in LV tissue displacements during the cardiac cycle and further reinforced by radially searching for pixel-based image gradients in multithreshold quantized magnitude images. This spatiotemporal edge detection methodology was applied to all LV partitions and their subsequent timeframes that lead to full 3D LV reconstructions. It was followed by quantifications of 3D chamber dimensions and myocardial strains, whose rapid computation was the primary motivation behind developing this algorithm. A pre-existing two-dimensional (2D) semi-automated contouring technique was used in parallel to validate the accuracy of the algorithm and both methods tested on DENSE data acquired in (N = 14) healthy subjects. Chamber quantifications between methods were compared using paired t-tests and Bland–Altman analysis established regional strain agreements.

Results:

There were no significant differences in the results of chamber quantifications between the 3D semi-automated and existing 2D boundary detection techniques. This included comparisons of ejection fractions, which were 0.62 ± 0.04 vs 0.60 ± 0.06 (p = 0.23) for apical, 0.60 ± 0.04 vs 0.59 ± 0.05 (p = 0.76) for midventricular and 0.56 ± 0.04 vs 0.58 ± 0.05 (p = 0.07) for basal segments, that were quantified using the 3D semi-automated and 2D pre-existing methodologies, respectively. Bland–Altman agreement between regional strains generated biases of 0.01 ± 0.06, –0.01 ± 0.01 and 0.0 ± 0.06 for the radial, circumferential and longitudinal directions, respectively.

Conclusion:

A new, 3D semi-automated methodology for contouring the entire LV and rapidly generating chamber quantifications and regional strains is presented that was validated in relation to an existing 2D contouring technique.

Advances in knowledge:

This study introduced a scientific tool for rapid, semi-automated generation of clinical information regarding shape and function in the 3D LV.

Enhancements to the caliop aerosol subtyping and lidar ratio selection algorithms for level II version 4

Several enhancements have been implemented for the version 4 aerosol subtyping and lidar ratio selection algorithms of Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP). Version 4 eliminates the confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions used to identify smoke aerosols over the ocean. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the marine boundary layer, moderately depolarizing aerosols are no longer modeled as mixtures of dust and smoke (polluted dust) but rather as mixtures of dust and seasalt (dusty marine). Some lidar ratios have been updated in the version 4 algorithms. In particular, the dust lidar ratios have been adjusted to reflect the latest measurements and model studies.

Preliminary investigation of multiparametric strain Z-score (MPZS) computation using displacement encoding with simulated echoes (DENSE) and radial point interpretation method (RPIM)

PURPOSE

To describe and assess an automated normalization method for identifying sentinel (septal) regions of myocardial dysfunction in nonischemic, nonvalvular dilated cardiomyopathy (DCM), using an unprecedented combination of the navigator-gated 3D spiral displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI), radial point interpolation (RPIM) and multiparametric strain z-score (MPZS).

MATERIALS AND METHODS

Navigator-gated 3D spiral DENSE, in a 1.5T MRI machine, was used for acquiring the displacement encoded complex images, MR Analytical Software System (MASS) for automated boundary detection and automated meshfree RPIM for left-ventricular (LV) myocardial strain computation to analyze MPZS in 36 subjects (with n = 17 DCM patients). Pearson's r correlation established relations between global/sentinel MPZS and ejection fraction (EF). The time taken for combined RPIM-MPZS computations was recorded.

RESULTS

Maximum MPZS differences were seen between anteroseptal and posterolateral regions in the base (2.0 ± 0.3 vs. 0.9 ± 0.5) and the mid-wall (2.1 ± 0.4 vs. 1.0 ± 0.4). These regional differences were found to be consistent with historically documented septal injury in nonischemic DCM. Correlations were 0.6 between global MPZS and EF, and 0.7 between sentinel MPZS and EF. The time taken for combined RPIM-MPZS computations per subject was 18.9 ± 5.9 seconds.

CONCLUSION

Heterogeneous contractility found in the sentinel regions with the current automated MPZS computation scheme and the correlation found between MPZS and EF may lead to the creation of a new clinical metric in LV DCM surveillance. J. MAGN. RESON. IMAGING 2016;44:993-1002.

Early left ventricular regional contractile impairment in chronic mitral regurgitation occurs in a consistent, heterogeneous pattern

OBJECTIVES

The clinical guidelines for asymptomatic patients with chronic mitral regurgitation (MR) use the ejection fraction (EF) to trigger surgical referral. We hypothesized that the EF is not sensitive enough to detect the earliest contractile injury in chronic MR and that the injury associated with chronic MR is not global but heterogeneous, occurring regionally and predictably, before the onset of global left ventricular (LV) dysfunction.

METHODS

Fifteen patients with chronic MR and normal LVEF by echocardiography underwent cardiac magnetic resonance imaging with tissue tagging. Point-specific comparisons (at 15,300 LV grid points) of multiple strain parameters to a normal human strain database allowed normalization of patient-specific regional contractile function. Data were mapped over patient-specific 3-dimensional geometry and averaged across 6 LV regions.

RESULTS

Global LV longitudinal and circumferential myocardial strains were normal for all 15 patients with MR compared with normal controls (P>.05). Despite preserved global function, the anteroseptum and posteroseptum demonstrated significantly worse contractile function compared with other LV regions (P=.003 and P=.035, respectively). Hypercontractile regions (lateral walls) appeared to compensate (P=.002) for the reduced septal contractile function, masking injury detection by global indexes.

CONCLUSIONS

The earliest contractile injury seen in patients with MR is heterogeneous and consistently distributed along the LV septum. Compensatory responses include hypercontractility of other regions. These data suggest that rather than relying on global LV contractile metrics, which cannot detect early injury, patients might be better served by undergoing directed surveillance of "sentinel" LV regions (LV septum) with high-resolution metrics of regional contractile function.

Three-dimensional regional strain computation method with displacement encoding with stimulated echoes (DENSE) in non-ischemic, non-valvular dilated cardiomyopathy patients and healthy subjects validated by tagged MRI

PURPOSE

Fast cine displacement encoding with stimulated echoes (DENSE) MR has higher spatial resolution and enables rapid postprocessing. Thus we compared the accuracy of regional strains computation by DENSE with tagged MR in healthy and non-ischemic, non-valvular dilated cardiomyopathy (DCM) subjects.

MATERIALS AND METHODS

Validation of three-dimensional regional strains computed with DENSE was conducted in reference to standard tagged MRI (TMRI) in healthy subjects and patients with DCM. Additional repeatability studies in healthy subjects were conducted to increase confidence in DENSE. A meshfree multiquadrics radial point interpolation method (RPIM) was used for computing Lagrange strains in sixteen left ventricular segments. Bland-Altman analysis and Student's t-tests were conducted to observe similarities in regional strains between sequences and in DENSE repeatability studies.

RESULTS

Regional circumferential strains ranged from -0.21 ± 0.07 (Lateral-Apex) to -0.11 ± 0.05 (Posterorseptal-Base) in healthy subjects and -0.15 ± 0.04 (Anterior-Apex) to -0.02 ± 0.08 (Posterorseptal-Base) in DCM patients. Computed mean differences in regional circumferential strain from the DENSE-TMRI comparison study was 0.01 ± 0.03 (95% limits of agreement) in normal subjects, -0.01 ± 0.06 in DCM patients and 0.0 ± 0.02 in repeatability studies, with similar agreements in longitudinal and radial strains.

CONCLUSION

We found agreement between DENSE and tagged MR in patients and volunteers in terms of evaluation of regional strains.

A validation of two-dimensional in vivo regional strain computed from displacement encoding with stimulated echoes (DENSE), in reference to tagged magnetic resonance imaging and studies in repeatability

Fast cine displacement encoding with stimulated echoes (DENSE) has comparative advantages over tagged MRI (TMRI) including higher spatial resolution and faster post-processing. This study computed regional radial and circumferential myocardial strains with DENSE displacements and validated it in reference to TMRI, according to American Heart Association (AHA) guidelines for standardized segmentation of regions in the left ventricle (LV). This study was therefore novel in examining agreement between the modalities in 16 AHA recommended LV segments. DENSE displacements were obtained with spatiotemporal phase unwrapping and TMRI displacements obtained with a conventional tag-finding algorithm. A validation study with a rotating phantom established similar shear strain between modalities prior to in vivo studies. A novel meshfree nearest node finite element method (NNFEM) was used for rapid computation of Lagrange strain in both phantom and in vivo studies in both modalities. Also novel was conducting in vivo repeatability studies for observing recurring strain patterns in DENSE and increase confidence in it. Comprehensive regional strain agreements via Bland-Altman analysis between the modalities were obtained. Results from the phantom study showed similar radial-circumferential shear strains from the two modalities. Mean differences in regional in vivo circumferential strains were -0.01 ± 0.09 (95% limits of agreement) from comparing the modalities and -0.01 ± 0.07 from repeatability studies. Differences and means from comparison and repeatability studies were uncorrelated (p > 0.05) indicating no increases in differences with increased strain magnitudes. Bland-Altman analysis and similarities in regional strain distribution within the myocardium showed good agreements between DENSE and TMRI and show their interchangeability. NNFEM was also established as a common framework for computing strain in both modalities.

OptDesign: extending optimizable k-dissimilarity selection to combinatorial library design

Optimizable k-dissimilarity (OptiSim) selection entails drawing a series of subsamples of size k from a population and choosing the "best" candidate from each such subsample for inclusion in the selection set. By varying the size of the subsample, one can control the balance between representativeness and diversity in the selection set obtained. In the original formulation, a uniform random sampling from among valid candidates was used to draw the subsamples from a single target population. Here we describe in detail two key modifications that serve to extend the OptiSim methodology to vector selection for interdependent variables, specifically as applied to the design of combinatorial sublibraries. The first modification involves pivoting between variables: subsamples are drawn from each reagent pool in turn, with the viability of each candidate being evaluated in isolation as well as in terms of the products it will produce from complementary reagents already selected. The filters applied may be static or dynamic in nature, with molecular weight and hydrophobicity being examples of the former and structural diversity with respect to reagents already selected being an example of the latter. The second key modification is adding the ability to bias the selection of candidate reagents for inclusion in the subsamples. Taken together, these modifications support the efficient generation of multiblock and other sparse matrix designs that are both representative and diverse, and for which "backfilling" of designs edited to remove undesirable reagents or products is straightforward. The method is intrinsically fast and efficient, since enumeration of the full combinatorial is not required- only those candidates actually considered for inclusion need be evaluated. Moreover, because the subsample selection step is separate from the diversity-based selection of the "best" candidate, incorporating such bias in favor of a competing criterion such as low price provides a "natural," nonparametric mechanism for generating designs that are likely to be "good" in a double-objective, Pareto sense.

A triaxial-measurement shear-test device for soft biological tissues

A novel shear-test device for soft biological tissue, capable of applying simple shear deformations simultaneously in two orthogonal directions while measuring the resulting forces generated in three axes, is described. We validated the device using a synthetic gel, the properties of which were ascertained from independent tensile and rotational shear tests. Material parameters for the gel were fitted using neo-Hookean analytical solutions to the independent test data, and these matched the results from the device. Preliminary results obtained with rat septal myocardium are also presented to demonstrate the feasibility of the apparatus in determining the shear characteristics of living tissue.