Faculty Bibliography

PURPOSE/OBJECTIVE:) mapping of knee cartilage. METHODS:-mapping of knee cartilage, including spatio-temporal finite differences, wavelets, dictionary from principal component analysis, and exponential decay models, and also low rank and low rank plus sparse models (L+S). Complex-valued fitting was used and Marchenko-Pastur principal component analysis filtering also tested. RESULTS:mapping, with median normalized absolute deviation below 10% up to AF of 6. CONCLUSION/CONCLUSIONS:mapping of knee cartilage, being it is a good alternative to Cartesian sampling for reducing scan time and/or improving image and mapping quality. The methods exponential decay models, spatio-temporal finite differences, and low rank obtained the best results for radial sampling patterns.

Low-dose X-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the X-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator. The use of a multislit collimator necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences than tube-current reduction at larger dose reduction levels. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at the quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.

PURPOSE/OBJECTIVE:. SparseCT partially blocks the x-ray beam with a multi-slit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using compressed sensing algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper. METHODS:We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x-ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose-weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the SNR of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully-sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity-enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively. RESULTS:Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For 4-fold undersampling with a 1.2 mm focal spot, a minimum MSC slit width of 3 detector rows (projected to the detector surface) is needed for beam separation; for 3-fold undersampling, a minimum slit width of 4 detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of 3 or 4 detector rows yields similar image quality. CONCLUSION/CONCLUSIONS:The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x-ray beam penumbra effects showed that for 3-fold and 4-fold dose reductions, an MSC slit width of 4 detector rows provided reasonable beam separation, undersampling efficiency, and image quality.

Purpose: SparseCT is a practical compressed sensing approach for CT dose reduction, which undersamples each view along the row dimension with a multislit collimator (MSC). The MSC is mounted between tube and patient and moves along the row direction to change the undersampling pattern along the row dimension for each view. This study aims to investigate the impact of MSC motion on SparseCT image quality.
Method(s): A SparseCT prototype was built with the MSC installed on a state-of-art clinical CT scanner. The MSC is a tungsten plate with periodic slits parallel to detector row direction. The slit separation is 3 times wider than the slit width, such that the dose reduction factor is 3. A liver phantom was scanned repeatedly at various MSC locations, each sampling different rows. The MSC was static during each scan, but 'dynamic MSC' scans were retrospectively simulated by stitching together projections from different scans. Six MSC motions were tested, including 3 patterns (linear, back-and-forth, and random) and 2 speeds (1 and 5 row(s)/projection). The dynamic MSC scans were reconstructed iteratively using a compressed sensing reconstruction algorithm that enforces 3D sparsity using total variation regularization. Image quality for different motions were compared in terms of PSNR and SSIM.
Result(s): Increasing MSC motion speed significantly improved PSNR and SSIM while the effect of motion pattern was negligible. Higher motion speeds also markedly reduced undersampling artifacts observed around high attenuation, high frequency objects such as the spine. The best PSNR and SSIM were achieved using a combination of linear motion and a speed of 5 rows/projection.
Conclusion(s): The motion of the MSC has a significant impact on the performance of SparseCT. Higher motion speed yields more incoherent undersampling artifacts and thus improves reconstruction quality

BACKGROUND:The value of dynamic contrast-enhanced (DCE) sequences in prostate MRI compared with noncontrast MRI is controversial. PURPOSE/OBJECTIVE:To evaluate the population net benefit of risk stratification using DCE-MRI for detection of high-grade prostate cancer (HGPCA), with or without high spatiotemporal resolution DCE imaging. STUDY TYPE/METHODS:Decision curve analysis. POPULATION/METHODS:Previously published patient studies on MRI for HGPCA detection, one using DCE with golden-angle radial sparse parallel (GRASP) images and the other using standard DCE-MRI. FIELD STRENGTH/SEQUENCE/UNASSIGNED:GRASP or standard DCE-MRI at 3 T. ASSESSMENT/RESULTS:Each study reported the proportion of lesions with HGPCA in each Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) category (1-5), before and after reclassification of peripheral zone lesions from PI-RADS 3-4 based on contrast-enhanced images. This additional risk stratifying information was translated to population net benefit, when biopsy was hypothetically performed for: all lesions, no lesions, PI-RADS ≥3 (using NC-MRI), and PI-RADS ≥4 on DCE. STATISTICAL TESTS/UNASSIGNED:Decision curve analysis was performed for both GRASP and standard DCE-MRI data, translating the avoidance of unnecessary biopsies and detection of HGPCA to population net benefit. We standardized net benefit values for HGPCA prevalence and graphically summarized the comparative net benefit of biopsy strategies. RESULTS:For a clinically relevant range of risk thresholds for HGPCA (>11%), GRASP DCE-MRI with biopsy of PI-RADS ≥4 lesions provided the highest net benefit, while biopsy of PI-RADS ≥3 lesions provided highest net benefit at low personal risk thresholds (2-11%). In the same range of risk thresholds using standard DCE-MRI, the optimal strategy was biopsy for all lesions (0-15% risk threshold) or PI-RADS ≥3 on NC-MRI (16-33% risk threshold). DATA CONCLUSION/UNASSIGNED:GRASP DCE-MRI may potentially enable biopsy of PI-RADS ≥4 lesions, providing relatively preserved detection of HGPCA and avoidance of unnecessary biopsies compared with biopsy of all PI-RADS ≥3 lesions. J. Magn. Reson. Imaging 2019.

PURPOSE/OBJECTIVE:parameters (short and long relaxation times and corresponding fractions) comparable to fully sampled scans. METHODS:parameter estimation was also tested. RESULTS:Most CS methods performed satisfactorily for an acceleration factor (AF) of 2, with relative median normalized absolute deviation (MNAD) around 10%. Some sparsifying transforms, such as low rank with spatial finite difference (L + S SFD), spatiotemporal finite difference (STFD), and exponential dictionaries (EXP) significantly improved this performance, reaching MNAD below 15% with AF up to 10, when spatial filtering was used. CONCLUSION/CONCLUSIONS:mapping of knee cartilage with CS is feasible. The best results were obtained by STFD, EXP, and L + S SFD regularizers combined with spatial prefiltering. These 3 CS methods performed satisfactorily on synthetic phantom as well as in vivo knee cartilage for AFs up to 10, with median error below 15%.

BACKGROUND:Computed tomography (CT) and spirometry are the current standard methods for assessing lung anatomy and pulmonary ventilation, respectively. However, CT provides limited ventilation information and spirometry only provides global measures of lung ventilation. Thus, a method that can enable simultaneous examination of lung anatomy and ventilation is of clinical interest. PURPOSE/OBJECTIVE:To develop and test a 4D respiratory-resolved sparse lung MRI (XD-UTE: eXtra-Dimensional Ultrashort TE imaging) approach for simultaneous evaluation of lung anatomy and pulmonary ventilation. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:In all, 23 subjects (11 volunteers and 12 patients, mean age = 63.6 ± 8.4). FIELD STRENGTH/SEQUENCE/UNASSIGNED:3T MR; a prototype 3D golden-angle radial UTE sequence, a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence. ASSESSMENT/RESULTS:All subjects were scanned using the 3D golden-angle radial UTE sequence during normal breathing. Ten subjects underwent an additional scan during alternating normal and deep breathing. Respiratory-motion-resolved sparse reconstruction was performed for all the acquired data to generate dynamic normal-breathing or deep-breathing image series. For comparison, BH-VIBE was performed in 12 subjects. Lung images were visually scored by three experienced chest radiologists and were analyzed by two observers who segmented the left and right lung to derive ventilation parameters in comparison with spirometry. STATISTICAL TESTS/UNASSIGNED:Nonparametric paired two-tailed Wilcoxon signed-rank test; intraclass correlation coefficient, Pearson correlation coefficient. RESULTS:XD-UTE achieved significantly improved image quality compared both with Cartesian BH-VIBE and radial reconstruction without motion compensation (P < 0.05). The global ventilation parameters (a sum of the left and right lung measures) were in good correlation with spirometry in the same subjects (correlation coefficient = 0.724). There were excellent correlations between the results obtained by two observers (intraclass correlation coefficient ranged from 0.8855-0.9995). DATA CONCLUSION/UNASSIGNED:Simultaneous evaluation of lung anatomy and ventilation using XD-UTE is demonstrated, which have shown good potential for improved diagnosis and management of patients with heterogeneous lung diseases. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

PURPOSE/OBJECTIVE:To develop a rapid dynamic contrast-enhanced MRI method with high spatial and temporal resolution for small-animal imaging at 7 Tesla. METHODS:An ultra-short echo time (UTE) pulse sequence using a 3D golden-angle radial sampling was implemented to achieve isotropic spatial resolution with flexible temporal resolution. Continuously acquired radial spokes were grouped into subsets for image reconstruction using a multicoil compressed sensing approach (Golden-angle RAdial Sparse Parallel; GRASP). The proposed 3D-UTE-GRASP method with high temporal and spatial resolutions was tested using 7 mice with GL261 intracranial glioma models. RESULTS:Iterative reconstruction with different temporal resolutions and regularization factors λ showed that, in all cases, the cost function decreased to less than 2.5% of its starting value within 20 iterations. The difference between the time-intensity curves of 3D-UTE-GRASP and nonuniform fast Fourier transform (NUFFT) images was minimal when λ was 1% of the maximum signal intensity of the initial NUFFT images. The 3D isotropic images were used to generate pharmacokinetic parameter maps to show the detailed images of the tumor characteristics in 3D and also to show longitudinal changes during tumor growth. CONCLUSION/CONCLUSIONS:This feasibility study demonstrated that the proposed 3D-UTE-GRASP method can be used for effective measurement of the 3D spatial heterogeneity of tumor pharmacokinetic parameters.

More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T₂ and T_1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed.

PURPOSE/OBJECTIVE:relaxation times. METHODS:parameter estimation was also tested. Synthetic phantom (n = 6) and in vivo human knee cartilage datasets (n = 7) were included. RESULTS:fitting. CONCLUSION/CONCLUSIONS:error of 6.5%.