Publications by authors named "Rohan Dharmakumar"

Purpose To evaluate the performance of a delayed-phase dynamic contrast-enhanced (dDCE) MRI model in quantitative assessment of myocardial tissue physiology and late gadolinium enhancement (LGE) within 5 minutes after contrast material injection in reperfused myocardial infarction (MI). Materials and Methods This animal study included 11 canines (seven female) with reperfused MI. A dDCE model using dynamic postcontrast T1 maps was adopted to depict an unbiased contrast material washout process.

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Background: Advances in acute ST-elevation myocardial infarction (STEMI) care have substantially decreased in-hospital mortality; however, in absolute terms, in-hospital mortality still remains high. Reperfusion injury, particularly intramyocardial hemorrhage following primary percutaneous coronary intervention (PCI), is a major predictor of adverse cardiovascular outcomes in the long term, but whether it contributes to in-hospital mortality is not known.

Methods: We performed a multicenter study to investigate the use of post-PCI high-sensitivity cardiac troponin I (hs-cTn-I) as a diagnostic tool to identify hemorrhagic myocardial infarction (MI) by determining hourly hs-cTn-I thresholds (every hour up to 12 hours, and at 16, 20, 24, and 48 hours post-PCI).

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Purpose: Magnetic susceptibility differences at the heart-lung interface introduce B-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B shimming pipeline to mitigate them.

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The Canadian Cardiovascular Society recently put forth a new classification of acute reperfused myocardial infarction (MI) based on stages of myocardial injury. Backed by more than 5 decades of intense investigation in the field, the key message of this new classification is that not all MIs are the same and that the type and extent of myocardial injury should be considered in diagnosing and treating MI. We review the literature with the goal of highlighting the progressive advances that enabled the synthesis of the Canadian Cardiovascular Society classification into 4 distinct stages of tissue injury.

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Background: Hemorrhagic myocardial infarction (hMI) can rapidly diminish the benefits of reperfusion therapy and direct the heart toward chronic heart failure. T2∗ cardiac magnetic resonance (CMR) is the reference standard for detecting hMI. However, the lack of clarity around the earliest time point for detection, time-dependent changes in hemorrhage volume, and the optimal methods for detection can limit the development of strategies to manage hMI.

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Purpose To evaluate the performance of a high-dynamic-range quantitative susceptibility mapping (HDR-QSM) cardiac MRI technique to detect intramyocardial hemorrhage (IMH) and quantify iron content using phantom and canine models. Materials and Methods A free-running whole-heart HDR-QSM technique for IMH assessment was developed and evaluated in calibrated iron phantoms and 14 IMH female canine models. IMH detection and iron content quantification performance of this technique was compared with the conventional iron imaging approaches, R2*(1/T2*) maps, using measurements from ex vivo imaging as the reference standard.

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The peptide hormone ghrelin is produced in cardiomyocytes and acts through the myocardial growth hormone secretagogue receptor (GHSR) to promote cardiomyocyte survival. Administration of ghrelin may have therapeutic effects on post-myocardial infarction (MI) outcomes. Therefore, there is a need to develop molecular imaging probes that can track the dynamics of GHSR in health and disease to better predict the effectiveness of ghrelin-based therapeutics.

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Article Synopsis
  • Caveolin-1 (Cav-1) acts as both a tumor suppressor and promoter, but its specific role in lung metastasis from breast cancer is not well understood.
  • Researchers found that knocking out Cav-1 in mammary epithelial cells led to significantly reduced lung metastasis in mouse models of breast cancer.
  • The study revealed that Cav-1 influences metastasis through the regulation of integrin α3 (ITGα3), linking it to changes in cell migration and extracellular vesicle secretion.
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Article Synopsis
  • Fully automatic analysis of myocardial perfusion MRI helps in quick and objective reporting for patients suspected of ischemic heart disease. The study focuses on overcoming challenges related to varied training data and differences in MRI software and hardware.
  • The research involved datasets from 150 subjects across three medical centers, utilizing a deep neural network (DNN) approach known as Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis for effective image segmentation.
  • Results showed that DAUGS analysis performed similarly to established methods on internal data but significantly outperformed them on external datasets, indicating its robustness in diverse clinical settings.
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Background: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge.

Methods: Datasets from three medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2).

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Purpose To investigate whether infarct-to-remote myocardial contrast can be optimized by replacing generic fitting algorithms used to obtain native T1 maps with a data-driven machine learning pixel-wise approach in chronic reperfused infarct in a canine model. Materials and Methods A controlled large animal model (24 canines, equal male and female animals) of chronic myocardial infarction with histologic evidence of heterogeneous infarct tissue composition was studied. Unsupervised clustering techniques using self-organizing maps and -distributed stochastic neighbor embedding were used to analyze and visualize native T1-weighted pixel-intensity patterns.

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Background: The ubiquitin-proteasome system regulates protein degradation and the development of pulmonary arterial hypertension (PAH), but knowledge about the role of deubiquitinating enzymes in this process is limited. UCHL1 (ubiquitin carboxyl-terminal hydrolase 1), a deubiquitinase, has been shown to reduce AKT1 (AKT serine/threonine kinase 1) degradation, resulting in higher levels. Given that AKT1 is pathological in pulmonary hypertension, we hypothesized that UCHL1 deficiency attenuates PAH development by means of reductions in AKT1.

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Microvascular injury immediately following reperfusion therapy in acute myocardial infarction (MI) has emerged as a driving force behind major adverse cardiovascular events in the postinfarction period. Although postmortem investigations and animal models have aided in developing early understanding of microvascular injury following reperfusion, imaging, particularly serial noninvasive imaging, has played a central role in cultivating critical knowledge of progressive damage to the myocardium from the onset of microvascular injury to months and years after in acute MI patients. This review summarizes the pathophysiological features of microvascular injury and downstream consequences, and the contributions noninvasive imaging has imparted in the development of this understanding.

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Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable.

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Purpose: Widely used conventional 2D T * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T * technique that enables rapid whole-heart coverage was developed and validated.

Methods: A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T * imaging.

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Purpose: Studies have shown that double-inversion-recovery (DIR) prepared dark-blood T2*-weighted images result in lower SNR, CNR and diagnostic accuracy for intramyocardial hemorrhage (IMH) detection compared to non-DIR-prepared (bright-blood) T2*-weighted images; however, the mechanism contributing to this observation has not been investigated and explained in detail. This work tests the hypothesis that the loss of SNR on dark-blood cardiac T2*-weighted images of IMH stems from spin-relaxation during the long RF pulses in double inversion preparation, as a result, compromising image contrast for intramyocardial hemorrhage detection.

Methods: Phantom and in-vivo animal studies were performed to test the hypothesis of the study.

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Myocardial infarction (MI) remains a leading cause of morbidity and mortality. In atherothrombotic MI (ST-elevation MI and type 1 non-ST-elevation MI), coronary artery occlusion leads to ischemia. Subsequent cardiomyocyte necrosis evolves over time as a wavefront within the territory at risk.

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Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable.

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