Publications by authors named "Sagar Mandava"
Article Synopsis
- The study aimed to compare the image quality and visibility of lesions in zero echo time (ZTE) MRI using a deep learning (DL) algorithm against conventional reconstruction methods, and to evaluate DL ZTE's effectiveness in assessing bone loss for shoulder instability compared to CT scans.
- Forty-four patients with shoulder instability underwent both ZTE MRI and CT scans, with images evaluated by two radiologists for clarity, resolution, and how well they showed lesions, using a rating scale.
- Results indicated that DL ZTE MRI images had superior resolution and showed lesions more clearly than conventional methods, with excellent agreement between DL ZTE and CT scans for measuring bone parameters.
Article Synopsis
- T1 mapping and Quantitative Susceptibility Mapping (QSM) are being studied as methods to measure changes in knee osteoarthritis, focusing on their effectiveness in analysis of articular cartilage.* -
- The study involved 20 healthy participants who underwent MRI with different spin lock times, aiming to assess how these combinations impact T1 estimation and correlate with QSM data.* -
- Results showed longer T1 times with higher spin lock periods and noted significant depth-specific differences in both T1 and QSM, suggesting these methods together can enhance the understanding of cartilage microstructure changes.*
Purpose: Quantitative T mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T mapping methods are confounded by fat and inhomogeneities, resulting in unreliable T estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold.
View Article and Find Full Text PDFArticle Synopsis
- Silent MRA, enhanced by a new deep learning algorithm from GE HealthCare, effectively evaluates stent performance for treating intracranial aneurysms.
- The study involved implanting two Surpass Evolve stents in silicone tubes to assess lumen visibility using different MRA techniques, including TOF and silent MRA.
- Results indicated that silent MRA provided better signal intensity ratios and reduced artifacts compared to TOF images, showcasing its potential for improved assessment of stent function.
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest.
View Article and Find Full Text PDFPurpose: To compare the quality of dynamic imaging between stack-of-stars acquisition without breath-holding (DISCO-Star) and the breath-holding method (Cartesian LAVA and DISCO).
Methods: This retrospective study was conducted between October 2019 and February 2020. Two radiologists performed visual assessments of respiratory motion or pulsation artifacts, streak artifacts, liver edge sharpness, and overall image quality using a 5-point scale for two datasets: Dataset 1 (n = 107), patients with Cartesian LAVA and DISCO-Star; Dataset 2 (n = 41), patients with DISCO and DISCO-Star at different time points.
Subspace-constrained reconstruction methods restrict the relaxation signals (of size M) in the scene to a pre-determined subspace (of size K≪M) and allow multi-contrast imaging and parameter mapping from accelerated acquisitions. However, these constraints yield poor image quality at some imaging contrasts, which can impact the parameter mapping performance. Additional regularization such as the use of joint-sparse (JS) or locally-low-rank (LLR) constraints can help improve the recovery of these images but are not sufficient when operating at high acceleration rates.
View Article and Find Full Text PDFA multi-scale residual network for accelerated radial MR parameter mapping.
Magn Reson Imaging
November 2020
A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches.
View Article and Find Full Text PDFPurpose: A new method for streak artifact reduction in radial MRI based on phased array filtering.
Theory: Radial imaging in applications that require large fields-of-view can be susceptible to streaking artifacts due to gradient nonlinearities. Coil removal methods prune the coils contributing the most to streaking artifacts at the expense of signal loss.
Background: Double inversion recovery (DIR) fast spin-echo (FSE) cardiovascular magnetic resonance (CMR) sequences are used clinically for black-blood T2-weighted imaging. However, these sequences suffer from slice inefficiency due to the non-selective inversion pulses. We propose a multi-band (MB) encoded DIR radial FSE (MB-DIR-RADFSE) technique to simultaneously excite two slices.
View Article and Find Full Text PDFPurpose: A new reconstruction method for multi-contrast imaging and parameter mapping based on a union of local subspaces constraint is presented.
Theory: Subspace constrained reconstructions use a predetermined subspace to explicitly constrain the relaxation signals. The choice of subspace size impacts the approximation error vs noise-amplification tradeoff associated with these methods.