Dattesh Shanbhag
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View article: Data Adaptive Few-shot Multi Label Segmentation with Foundation Model
Data Adaptive Few-shot Multi Label Segmentation with Foundation Model Open
The high cost of obtaining accurate annotations for image segmentation and localization makes the use of one and few shot algorithms attractive. Several state-of-the-art methods for few-shot segmentation have emerged, including text-based …
View article: LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging Open
Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (S…
View article: Language Augmentation in CLIP for Improved Anatomy Detection on Multi-modal Medical Images
Language Augmentation in CLIP for Improved Anatomy Detection on Multi-modal Medical Images Open
Vision-language models have emerged as a powerful tool for previously challenging multi-modal classification problem in the medical domain. This development has led to the exploration of automated image description generation for multi-mod…
View article: One-shot Localization and Segmentation of Medical Images with Foundation Models
One-shot Localization and Segmentation of Medical Images with Foundation Models Open
Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models with their ability to capture rich semantic features of the image have been used for image correspondence tasks on natural images. In this paper, we examine the …
View article: Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network
Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network Open
Objective . In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the informa…
View article: Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network Open
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propo…
View article: A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations Open
Fast, automated deep learning-based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values…
View article: Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI
Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI Open
Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in …
View article: Evaluation of Sinus/Edge-Corrected Zero-Echo-Time–Based Attenuation Correction in Brain PET/MRI
Evaluation of Sinus/Edge-Corrected Zero-Echo-Time–Based Attenuation Correction in Brain PET/MRI Open
In brain PET/MRI, the major challenge of zero-echo-time (ZTE)-based attenuation correction (ZTAC) is the misclassification of air/tissue/bone mixtures or their boundaries. Our study aimed to evaluate a sinus/edge-corrected (SEC) ZTAC (ZTAC…
View article: Hybrid <scp>ZTE</scp>/Dixon <scp>MR</scp>‐based attenuation correction for quantitative uptake estimation of pelvic lesions in <scp>PET</scp>/<scp>MRI</scp>
Hybrid <span>ZTE</span>/Dixon <span>MR</span>‐based attenuation correction for quantitative uptake estimation of pelvic lesions in <span>PET</span>/<span>MRI</span> Open
Purpose This study introduces a new hybrid ZTE /Dixon MR ‐based attenuation correction ( MRAC ) method including bone density estimation for PET / MRI and quantifies the effects of bone attenuation on metastatic lesion uptake in the pelvis…
View article: Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging
Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging Open
Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We inve…
View article: Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull
Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull Open
This is the first, to our knowledge, clinical evaluation of skull bone identification based on a ZTE sequence. The results suggest that proton density-weighted ZTE imaging is an efficient means of obtaining high-resolution maps of bone tis…
View article: Zero <scp>TE</scp><scp>MR</scp> bone imaging in the head
Zero <span>TE</span><span>MR</span> bone imaging in the head Open
Purpose To investigate proton density (PD)‐weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures. Methods A rotating ultra‐fast imaging sequence (RUFIS) type ZT pulse sequence was developed a…