IU Indianapolis ScholarWorks :: Browsing by Author "Dharmakumar, Rohan"

Browsing by Author "Dharmakumar, Rohan"

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A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion
(IEEE, 2021) Youssef, Khalid; Heydari, Bobby; Rivero, Luis Zamudio; Beaulieu, Taylor; Cheema, Karandeep; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of Medicine
Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for objective evaluation of ischemic heart disease in patients with suspected coronary artery disease. State-of-the-art MBF quantification techniques use constrained deconvolution and are highly sensitive to noise and motion-induced errors, which can lead to unreliable outcomes in the setting of high-resolution MBF mapping. To overcome these limitations, recent iterative approaches incorporate spatial-smoothness constraints to tackle pixel-wise MBF mapping. However, such iterative methods require a computational time of up to 30 minutes per acquired myocardial slice, which is a major practical limitation. Furthermore, they cannot enforce robustness to residual nonrigid motion which can occur in clinical stress/rest studies of patients with arrhythmia. We present a non-iterative patch-wise deep learning approach for pixel-wise MBF quantification wherein local spatio-temporal features are learned from a large dataset of myocardial patches acquired in clinical stress/rest cMRI studies. Our approach is scanner-independent, computationally efficient, robust to noise, and has the unique feature of robustness to motion-induced errors. Numerical and experimental results obtained using real patient data demonstrate the effectiveness of our approach.Clinical Relevance- The proposed patch-wise deep learning approach significantly improves the reliability of high-resolution myocardial blood flow quantification in cMRI by improving its robustness to noise and nonrigid myocardial motion and is up to 300-fold faster than state-of-the-art iterative approaches.
Accurate Intramyocardial Hemorrhage Assessment with Fast, Free-running, Cardiac Quantitative Susceptibility Mapping
(Radiological Society of North America, 2024) Huang, Yuheng; Guan, Xingmin; Zhang, Xinheng; Yoosefian, Ghazal; Ho, Hao; Huang, Li-Ting; Lin, Hsin-Yao; Anthony, Gregory; Lee, Hsu-Lei; Bi, Xiaoming; Han, Fei; Chan, Shing Fai; Vora, Keyur P.; Sharif, Behzad; Singh, Dhirendra P.; Youssef, Khalid; Li, Debiao; Han, Hui; Christodoulou, Anthony G.; Dharmakumar, Rohan; Yang, Hsin-Jung; Medicine, School of Medicine
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. Results: Phantom studies confirmed HDR-QSM’s accurate iron content quantification and artifact mitigation ability by revealing a strong linear relationship between iron concentration and QSM values (R2, 0.98). In in vivo studies, HDR-QSM showed significantly improved image quality and susceptibility homogeneity in nonaffected myocardium by alleviating motion and off-resonance artifacts (HDR-QSM vs R2*: coefficient of variation, 0.31 ± 0.16 [SD] vs 0.73 ± 0.36 [P < .001]; image quality score [five-point Likert scale:], 3.58 ± 0.75 vs 2.87 ± 0.51 [P < .001]). Comparison between in vivo susceptibility maps and ex vivo measurements showed higher performance of HDR-QSM compared with R2* mapping for IMH detection (area under the receiver operating characteristic curve, 0.96 vs 0.75; P < .001) and iron content quantification (R2, 0.71 vs 0.14). Conclusion: In a canine model of IMH, the fast and free-running cardiac QSM technique accurately detected IMH and quantified intramyocardial iron content of the entire heart within 5 minutes without requiring breath holding.
Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement
(American Heart Association, 2022-11-15) Zhang, Qiang; Burrage, Matthew K.; Shanmuganathan, Mayooran; Gonzales, Ricardo A.; Lukaschuk, Elena; Thomas, Katharine E.; Mills, Rebecca; Pelado, Joana Leal; Nikolaidou, Chrysovalantou; Popescu, Iulia A.; Lee, Yung P.; Zhang, Xinheng; Dharmakumar, Rohan; Myerson, Saul G.; Rider, Oliver; Oxford Acute Myocardial Infarction (OxAMI) Study; Channon, Keith M.; Neubauer, Stefan; Piechnik, Stefan K.; Ferreira, Vanessa M.; Medicine, School of Medicine
Background: Myocardial scar is currently assessed non-invasively using cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) as an imaging gold-standard. However, a contrast-free approach would provide many advantages, including a faster and cheaper scan, without contrast-associated problems. Methods: Virtual Native Enhancement (VNE) is a novel technology that can produce virtual LGE-like images, without the need for contrast. VNE combines cine imaging and native T1-maps to produce LGE-like images using artificial intelligence (AI). VNE was developed for patients with prior myocardial infarction on 4271 datasets (912 patients), where each dataset is comprised of slice position-matched cine, T1-maps and LGE images. After quality control, 3002 datasets (775 patients) were used for development, and 291 datasets (68 patients) for testing. The VNE generator was trained using generative adversarial networks, employing two adversarial discriminators to improve the image quality. The left ventricle was contoured semi-automatically. Myocardial scar volume was quantified using the full width at half maximum method. Scar transmurality was measured using the centerline chord method and visualized on bull’s eye plots. Lesion quantification by VNE and LGE were compared using linear regression, Pearson correlation (R) and intraclass correlation coefficients (ICC). Proof-of-principle histopathological comparison of VNE in a porcine model of myocardial infarction was also performed. Results: VNE provided significantly better image quality than LGE on blinded analysis by 5 independent operators on 291 datasets (all p<0.001). VNE correlated strongly with LGE in quantifying scar size (R=0.89, ICC=0.94) and transmurality (R=0.84, ICC=0.90) in 66 patients (277 test datasets). Two CMR experts reviewed all test image slices and reported an overall accuracy of 84% of VNE in detecting scar when compared with LGE, with specificity of 100% and sensitivity of 77%. VNE also showed excellent visuospatial agreement with histopathology in 2 cases of a porcine model of myocardial infarction. Conclusions: VNE demonstrated high agreement with LGE-CMR for myocardial scar assessment in patients with prior myocardial infarction in visuospatial distribution and lesion quantification, with superior image quality. VNE is a potentially transformative AI-based technology, with promise to reduce scan times and costs, increase clinical throughput, and improve the accessibility of CMR in the very-near future.
Assessment of intramyocardial hemorrhage with dark-blood T2*-weighted cardiovascular magnetic resonance
(Elsevier, 2021-07-15) Guan, Xingmin; Chen, Yinyin; Yang, Hsin‑Jung; Zhang, Xinheng; Ren, Daoyuan; Sykes, Jane; Butler, John; Han, Hui; Zeng, Mengsu; Prato, Frank S.; Dharmakumar, Rohan; Medicine, School of Medicine
Background: Intramyocardial hemorrhage (IMH) within myocardial infarction (MI) is associated with major adverse cardiovascular events. Bright-blood T2*-based cardiovascular magnetic resonance (CMR) has emerged as the reference standard for non-invasive IMH detection. Despite this, the dark-blood T2*-based CMR is becoming interchangeably used with bright-blood T2*-weighted CMR in both clinical and preclinical settings for IMH detection. To date however, the relative merits of dark-blood T2*-weighted with respect to bright-blood T2*-weighted CMR for IMH characterization has not been studied. We investigated the diagnostic capacity of dark-blood T2*-weighted CMR against bright-blood T2*-weighted CMR for IMH characterization in clinical and preclinical settings. Materials and methods: Hemorrhagic MI patients (n = 20) and canines (n = 11) were imaged in the acute and chronic phases at 1.5 and 3 T with dark- and bright-blood T2*-weighted CMR. Imaging characteristics (Relative signal-to-noise (SNR), Relative contrast-to-noise (CNR), IMH Extent) and diagnostic performance (sensitivity, specificity, accuracy, area-under-the-curve, and inter-observer variability) of dark-blood T2*-weighted CMR for IMH characterization were assessed relative to bright-blood T2*-weighted CMR. Results: At both clinical and preclinical settings, compared to bright-blood T2*-weighted CMR, dark-blood T2*-weighted images had significantly lower SNR, CNR and reduced IMH extent (all p < 0.05). Dark-blood T2*-weighted CMR also demonstrated weaker sensitivity, specificity, accuracy, and inter-observer variability compared to bright-blood T2*-weighted CMR (all p < 0.05). These observations were consistent across infarct age and imaging field strengths. Conclusion: While IMH can be visible on dark-blood T2*-weighted CMR, the overall conspicuity of IMH is significantly reduced compared to that observed in bright-blood T2*-weighted images, across infarct age in clinical and preclinical settings at 1.5 and 3 T. Hence, bright-blood T2*-weighted CMR would be preferable for clinical use since dark-blood T2*-weighted CMR carries the potential to misclassify hemorrhagic MIs as non-hemorrhagic MIs.
Caveolin-1 knockout mitigates breast cancer metastasis to the lungs via integrin α3 dysregulation in 4T1-induced syngeneic breast cancer model
(Springer Nature, 2024) Singh, Dhirendra Pratap; Pathak, Rashmi; Chintalaramulu, Naveen; Pandit, Abhishek; Kumar, Avinash; Ebenezer, Philip J.; Kumar, Sanjay; Duplooy, Alexander; White, Mary Evelyn; Jambunathan, Nithya; Dharmakumar, Rohan; Francis, Joseph; Radiology and Imaging Sciences, School of Medicine
Caveolin-1 (Cav-1) is a critical lipid raft protein playing dual roles as both a tumor suppressor and promoter. While its role in tumorigenesis, progression, and metastasis has been recognized, the explicit contribution of Cav-1 to the onset of lung metastasis from primary breast malignancies remains unclear. Here, we present the first evidence that Cav-1 knockout in mammary epithelial cells significantly reduces lung metastasis in syngeneic breast cancer mouse models. In vitro, Cav-1 knockout in 4T1 cells suppressed extracellular vesicle secretion, cellular motility, and MMP secretion compared to controls. Complementing this, in vivo analyses demonstrated a marked reduction in lung metastatic foci in mice injected with Cav-1 knockout 4T1 cells as compared to wild-type cells, which was further corroborated by mRNA profiling of the primary tumor. We identified 21 epithelial cell migration genes exhibiting varied expression in tumors derived from Cav-1 knockout and wild-type 4T1 cells. Correlation analysis and immunoblotting further revealed that Cav-1 might regulate metastasis via integrin α3 (ITGα3). In silico protein docking predicted an interaction between Cav-1 and ITGα3, which was confirmed by co-immunoprecipitation. Furthermore, Cav-1 and ITGα3 knockdown corroborated its role in metastasis in the cell migration assay.
Chronic heart failure following hemorrhagic myocardial infarction: mechanism, treatment and outlook
(Shared Science Publishers, 2023-02-13) Chan, Shing Fai; Vora, Keyur; Dharmakumar, Rohan; Medicine, School of Medicine
Myocardial infarction (MI), the blockage of arterial blood supply of the heart, is among the most common causes of death worldwide. Even when patients receive immediate treatment by re-opening blocked arteries, they often develop chronic heart failure (CHF) in the aftermath of MI events. Yet, the factors that contribute to the development of MI-associated CHF are poorly understood. In our recent study (Nat Commun 13:6394), we link intramyocardial hemorrhage, an injury which can occur during reperfusion of areas affected by MI, to an increased risk of CHF. Mechanistically, our data suggest that an iron-induced adverse cascade of events after hemorrhagic MI drives fatty degeneration of infarcted tissue, which ultimately contributes to negative cardiac remodeling. In this Microreview, we discuss the implications of our findings regarding the molecular mechanism, more targeted treatment options as well as perspectives in the clinical care of CHF after hemorrhagic MI.
Clinical Outcomes of Patients With Acute Myocardial Infarction in Health Professional Shortage Areas in Indiana
(Elsevier, 2024-03-08) Gunderman, David J.; Kumar, Ashish; Munguia-Vazquez, Raymundo; Vora, Keyur; Shah, Chirag; Lambert, Nathan; Cavanaugh, Brendan; Dharmakumar, Rohan; Kalra, Ankur; Medicine, School of Medicine
Correction to: Quantification of myocardial hemorrhage using T2* cardiovascular magnetic resonance at 1.5 T with ex-vivo validation
(Elsevier, 2022-02-07) Chen, Yinyin; Ren, Daoyuan; Guan, Xingmin; Yang, Hsin‑Jung; Liu, Ting; Tang, Richard; Ho, Hao; Jin, Hang; Zeng, Mengsu; Dharmakumar, Rohan; Radiology and Imaging Sciences, School of Medicine
In the original publication [1] an error was introduced in the affiliations of Yinyin Chen due to a misunderstanding during the publication process. The incorrect and correct affiliations are listed below. The original article has been updated.
Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation
(IEEE, 2021) Yalcinkaya, Dilek Mirgun; Youssef, Khalid; Heydari, Bobby; Zamudio, Luis; Dharmakumar, Rohan; Sharif, Behzad; Medicine, School of Medicine
In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.
Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation
(IEEE, 2021) Chartsias, Agisilaos; Papanastasiou, Giorgos; Wang, Chengjia; Semple, Scott; Newby, David E.; Dharmakumar, Rohan; Tsaftaris, Sotirios A.; Medicine, School of Medicine
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common information shared between modalities (an organ's anatomy) is beneficial for multi-modality processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to obtain this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no (unsupervised) annotations are available for this specific modality. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, which non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation. Code is available at https://github.com/vios-s/multimodal_segmentation.

Browsing by Author "Dharmakumar, Rohan"

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