Ganesh Adluru

Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging

To improve myocardial perfusion magnetic resonance imaging (MRI) by reconstructing undersampled radial data with a spatiotemporal constrained reconstruction method (STCR).

Temporally constrained reconstruction of dynamic cardiac perfusion MRI.

Dynamic contrast‐enhanced (DCE) MRI is a powerful technique to probe an area of interest in the body. Here a temporally constrained reconstruction (TCR) technique that requires less k‐space data over time to obtain good‐quality reconstructed images is proposed. This approach can be used to improve the spatial or temporal resolution, or increase the coverage of the object of interest. The method jointly reconstructs the space‐time data iteratively with a temporal constraint in order to resolve aliasing. The method was implemented and its feasibility tested on DCE myocardial perfusion data with little or no motion. The results obtained from sparse k‐space data using the TCR method were compared with results obtained with a sliding‐window (SW) method and from full data using the standard inverse Fourier transform (IFT) reconstruction. Acceleration factors of 5 (R = 5) were achieved without a significant loss in image quality. Mean improvements of 28 ± 4% in the signal‐to‐noise ratio (SNR) and 14 ± 4% in the contrast‐to‐noise ratio (CNR) were observed in the images reconstructed using the TCR method on sparse data (R = 5) compared to the standard IFT reconstructions from full data for the perfusion datasets. The method has the potential to improve dynamic myocardial perfusion imaging and also to reconstruct other sparse dynamic MR acquisitions. Magn Reson Med 57:1027–1036, 2007.

Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE.

For patients with impaired breath‐hold capacity and/or arrhythmias, real‐time cine MRI may be more clinically useful than breath‐hold cine MRI. However, commercially available real‐time cine MRI methods using parallel imaging typically yield relatively poor spatio‐temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm2) and temporal resolution (∼40 ms), to produce high‐quality real‐time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real‐time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k‐t SPARSE‐SENSE). Compared with reference, breath‐hold cine MRI, our eightfold accelerated real‐time cine MRI produced significantly worse qualitative grades (1–5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real‐time cine and breath‐hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation <10% for left ventricular volumes. Our proposed eightfold accelerated real‐time cine MRI with k–t SPARSE‐SENSE is a promising modality for rapid imaging of myocardial function. J. Magn. Reson. Imaging 2013.

Model-based registration for dynamic cardiac perfusion MRI

To assess the accuracy of a model‐based approach for registration of myocardial dynamic contrast‐enhanced (DCE)‐MRI corrupted by respiratory motion.

Reconstruction of 3D dynamic contrast-enhanced magnetic resonance imaging using nonlocal means

To develop and test a nonlocal means‐based reconstruction algorithm for undersampled 3D dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) of tumors.

Temporally constrained reconstruction applied to MRI temperature data

The monitoring of thermal ablation procedures would benefit from an acceleration in the rate at which MRI temperature maps are acquired. Constrained reconstruction techniques have been shown to be capable of generating high quality images using only a fraction of the k‐space data. Here, we present a temporally constrained reconstruction (TCR) algorithm applied to proton resonance frequency shift (PRF) data. The algorithm generates images from undersampled data by iteratively minimizing a cost function. The unique challenges of using an iterative constrained reconstruction technique to generate real‐time images were addressed. For a set of eight heating experiments on ex vivo porcine tissue, a maximum reduction factor of 4 was achieved while keeping the root mean square error (RMSE) of the temperature below 0.5°C. For a set of three heating experiments on in vivo canine muscle tissue, the maximum reduction factor achieved was 3 while keeping the temperature RMSE below 1.0°C. At these reduction factors, the TCR algorithm underpredicted the thermal dose by an average of 6% for the ex vivo data and 28% for the in vivo data. Compared with sliding window and low resolution reconstructions, the RMSE of the TCR algorithm was significantly lower (P < 0.05 in all cases). Magn Reson Med, 2009.

Reordering for improved constrained reconstruction from undersampled k-space data

Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented.

Model‐based reconstruction of undersampled diffusion tensor k‐space data

The practical utility of diffusion tensor imaging, especially for 3D high‐resolution spin warp experiments of ex vivo specimens, has been hampered by long acquisition times. To accelerate the acquisition, a compressed sensing framework that uses a model‐based formulation to reconstruct diffusion tensor fields from undersampled k‐space data was presented and evaluated. Accuracies in brain specimen white matter fiber orientation, fractional anisotropy, and mean diffusivity mapping were compared with alternative methods achievable using the same scan time via reduced image resolution, fewer diffusion encoding directions, standard compressed sensing, or asymmetrical sampling reconstruction. The efficiency of the proposed approach was also compared with fully sampled cases across a range of the number of diffusion encoding directions. In general, the proposed approach was found to reduce the image blurring and noise and to provide more accurate fiber orientation, fractional anisotropy, and mean diffusivity measurements compared with the alternative methods. Moreover, depending on the degree of undersampling used and the diffusion tensor imaging parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled diffusion tensor imaging datasets that consist of 33–67% more encoding directions and require proportionally longer scan times. The findings show model‐based compressed sensing to be promising for improving the resolution, accuracy, or scan time of diffusion tensor imaging. Magn Reson Med 70:429–440, 2013.

Myocardial perfusion MRI with an undersampled 3D stack‐of‐stars sequence

PURPOSE To determine the feasibility of three-dimensional (3D) hybrid radial (stack-of-stars) MRI with spatiotemporal total variation (TV) constrained reconstruction for dynamic contrast enhanced myocardial perfusion imaging. METHODS An ECG-triggered saturation recovery turboFLASH sequence with undersampled stack-of-stars sampling with spatiotemporal TV constrained reconstruction was developed for dynamic contrast enhanced myocardial perfusion imaging. Simulations were performed to study the dependence of the approach to steady state on flip angle and saturation recovery time for this stack-of-stars acquisition. Phantom studies were used to show the effect of the flip angle selection and imperfect spoiling on image qualities. Studies were done in three humans to test the feasibility of the approach for myocardial perfusion imaging. RESULTS The simulation and phantom studies showed that imperfect spoiling and magnetization changes during the readout were a function of flip angle and nonoptimized selection of flip angle could degrade the images. Low flip angle acquisitions in the human subjects result in images with good quality similar to multislice radial 2D images. CONCLUSIONS 3D stack-of-stars sampling with spatiotemporal TV constrained reconstruction provides a promising alternative for myocardial perfusion imaging.

Three-dimensional late gadolinium enhancement imaging of the left atrium with a hybrid radial acquisition and compressed sensing.

To develop and test a hybrid radial (stack of stars) acquisition and compressed sensing reconstruction for efficient late gadolinium enhancement (LGE) imaging of the left atrium.