Jagtap, Jaidip

Introduction: Hyperpolarized [1- 13 C] pyruvate magnetic resonance imaging (HP- 13 C MRI) enables real-time, noninvasive assessment of myocardial metabolism. While the technique is increasingly applied in cardiac research, dedicated tools for standardized analysis remain limited. We developed HP- 13 CMRAnalyst , a MATLAB-based application that supports dynamic 13 C data processing through semi-automated segmentation, time-course extraction, and metabolic ratio mapping (Figure 1A). Hypothesis: An integrated platform combining multi-frame 13 C data import, cine co-registration, multi-segment analysis, and voxel-level ratio mapping facilitates reproducible quantification of myocardial metabolism. Methods: HP- 13 CMRAnalyst was implemented in MATLAB App Designer and distributed as an .mlappinstall package. It accepts DICOM or NIfTI dynamic pyruvate, lactate, and bicarbonate images (∼3 s resolution; ≤ 60 s)(Figure 1B). Cine-registered T 1 -weighted images guide AHA 17-segment LV segmentation on short axis (SAX) and long axis (LAX) views (Figure 1C). A time slider displays metabolite overlays. Peak intensities are extracted per segment to compute bicarbonate/lactate and bicarbonate/pyruvate ratios. Pixel-wise ratio maps are exported as CSV, MATLAB struct, and PDF summaries. Results: Eight subjects (n = 4 healthy; n = 4 coronary artery disease [CAD]), each imaged pre- and post-CABG (total n = 12 datasets; age 55 ± 12 years), were processed in < 5 min per dataset on a standard desktop (MATLAB R2024b). Semi-automated versus manual segmentation agreement was high (Dice 0.89–0.93). Inter-observer variability for segmental bicarbonate/(bicarbonate + lactate) ratios were minimal biases < 0.005 and limits of agreement within ± 0.083 across all observer pairs (Figure 1D). Ratio maps qualitatively revealed regional metabolic heterogeneity. Automated reports accurately reflected metadata and segmental summaries. The GUI, logs, and summary files streamline standardized processing. Conclusions: HP- 13 CMRAnalyst delivers a rapid (< 5 min), reproducible framework for dynamic HP- 13 C MRI analysis. By combining semi-automated segmentation and pixel-level ratio mapping with low inter-observer variability, this platform may accelerate adoption of 13 C metabolic imaging in human cardiac research.

Introduction Radiation therapy for head and neck squamous cell carcinoma is constrained by radiotoxicity to normal tissue. We demonstrate 100 nm theranostic nanoparticles for image-guided radiation therapy planning and enhancement in rat head and neck squamous cell carcinoma models. Methods PEG conjugated theranostic nanoparticles comprising of Au nanorods coated with Gadolinium oxide layers were tested for radiation therapy enhancement in 2D cultures of OSC-19-GFP-luc cells, and orthotopic tongue xenografts in male immunocompromised Salt sensitive or SS rats via both intratumoral and intravenous delivery. The radiation therapy enhancement mechanism was investigated. Results Theranostic nanoparticles demonstrated both X-ray/magnetic resonance contrast in a dose-dependent manner. Magnetic resonance images depicted optimal tumor-to-background uptake at 4 h post injection. Theranostic nanoparticle + Radiation treated rats experienced reduced tumor growth compared to controls, and reduction in lung metastasis. Conclusions Theranostic nanoparticles enable preprocedure radiotherapy planning, as well as enhance radiation treatment efficacy for head and neck tumors.

Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above 90%. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.

Abstract Primary sclerosis cholangitis (PSC) predisposes individuals to liver failure, but it is challenging for radiologists examining radiologic images to predict which patients with PSC will ultimately develop liver failure. Motivated by algebraic topology, a topological data analysis - inspired framework was adopted in the study of the imaging pattern between the “Early Decompensation” and “Not Early” groups. The results demonstrate that the proposed methodology discriminates “Early Decompensation” and “Not Early” groups. Our study is the first attempt to provide a topological representation-based method into early hepatic decompensation and not early groups.