Dibash Basukala

• PhD in Computer Science
• Postdoctoral Fellow at NYU Langone Health

Introduction The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in l...

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Hypoxia, a condition characterized by insufficient oxygen supply in tumor tissues, is closely associated with tumor aggressiveness, resistance to therapy, and poor clinical outcomes. Accurate assessment of tumor hypoxia can guide treatment decisions, predict thera...

Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from m...

Background Monoexponential apparent diffusion coefficient (ADC) and biexponential intravoxel incoherent motion (IVIM) analysis of diffusion‐weighted imaging is helpful in the characterization of breast tumors. However, repeatability/reproducibility studies across scanners and across sites are scarce. Purpose To evaluate the repeatability and repro...

The effect of cardiac gating on quantitative diffusion weighted magnetic resonance imaging of the kidney was investigated using an advanced cardiac triggered diffusion-weighted imaging sequence which allows the acquisition and estimation of diffusion tensor imaging and intra-voxel incoherent motion parameters. Cardiac gating significantly influence...

Purpose: Diffusion-weighted imaging (DWI) of the abdomen has increased dramatically for both research and clinical purposes. Motion and static field inhomogeneity related challenges limit image quality of abdominopelvic imaging with the most conventional echo-planar imaging (EPI) pulse sequence. While reversed phase encoded imaging is increasingly...

Background: Renal diffusion-weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity. Purpose: To measure modulation of DWI with cardiac phase and flow-compensated (...

Accurate segmentation of substantia nigra (SN) and red nucleus (RN) is challenging, yet important for understanding health problems like Parkinson's disease (PD). This paper proposes an algorithm to segment SN and RN from quantitative susceptibility mapping (QSM) MRI and use the results to investigate PD. Algorithm-derived segments (based on level...

Image segmentation is an important step in most medical image analysis tasks. An effective image segmentation method helps clinicians and patients in image-guided surgery, radiotherapy, early disease detection, volumetric measurement, and three-dimensional visualization. The fuzzy c-means (FCM) clustering algorithm is one of the most popular method...

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over-segmentation is its most significant limitation. Therefore, this article proposes a combinat...

Watershed Transformation is a popular segmentation method coming from the field of mathematical morphology. Different kernels such as rice, wheat, and corn are over-segmented by the traditional watershed algorithm. Therefore, this paper proposes an improved watershed segmentation algorithm by automatic selection of threshold value using moment pres...