Search

There is a clinical need for alternatives to gadolinium contrast‐enhanced magnetic resonance imaging (MRI) to facilitate early detection and assessment of femoral head ischemia in pediatric patients with Legg‐Calvé‐Perthes disease (LCPD), a juvenile form of idiopathic osteonecrosis of the femoral head. The purpose of this study was to determine if intravoxel incoherent motion (IVIM), a noncontrast‐enhanced MRI method to simultaneously measure tissue perfusion and diffusion, can detect femoral head ischemia using a piglet model of LCPD. Twelve 6‐week‐old piglets underwent unilateral hip surgery to induce complete femoral head ischemia. The unoperated, contralateral femoral head served as a perfused control. The bilateral hips of the piglets were imaged in vivo at 3T MRI using IVIM and contrast‐enhanced MRI 1 week after surgery. Median apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient: Ds; perfusion coefficient: Df; perfusion fraction: f; and perfusion flux: f*Df) were compared between regions of interest comprising the epiphyseal bone marrow of the ischemic and control femoral heads. Contrast‐enhanced MRI confirmed complete femoral head ischemia in 11/12 piglets. IVIM perfusion fraction (f) and flux (f*Df) were significantly decreased in the ischemic versus control femoral heads: on average, f decreased 47 ± 27% (Δf = −0.055 ± 0.034; p = 0.0003) and f*Df decreased 50 ± 27% (Δf*Df = −0.59 ± 0.49 × 10−3 mm2/s; p = 0.0026). In contrast, IVIM diffusion coefficient (Ds) and ADC were significantly increased in the ischemic versus control femoral heads: on average, Ds increased 78 ± 21% (ΔDs = 0.60 ± 0.14 × 10−3 mm2/s; p < 0.0001) and ADC increased 60 ± 36% (ΔADC = 0.50 ± 0.23 × 10−3 mm2/s; p < 0.0001). In conclusion, IVIM is sensitive in detecting bone marrow ischemia in a piglet model of LCPD. [ABSTRACT FROM AUTHOR]

Purpose: Simultaneous multi‐slice acquisitions are essential for modern neuroimaging research, enabling high temporal resolution functional and high‐resolution q‐space sampling diffusion acquisitions. Recently, deep learning reconstruction techniques have been introduced for unaliasing these accelerated acquisitions, and robust artificial‐neural‐networks for k‐space interpolation (RAKI) have shown promising capabilities. This study systematically examines the impacts of hyperparameter selections for RAKI networks, and introduces a novel technique for training data generation which is analogous to the split‐slice formalism used in slice‐GRAPPA. Methods: RAKI networks were developed with variable hyperparameters and with and without split‐slice training data generation. Each network was trained and applied to five different datasets including acquisitions harmonized with Human Connectome Project lifespan protocol. Unaliasing performance was assessed through L1 errors computed between unaliased and calibration frequency‐space data. Results: Split‐slice training significantly improved network performance in nearly all hyperparameter configurations. Best unaliasing results were achieved with three layer RAKI networks using at least 64 convolutional filters with receptive fields of 7 voxels, 128 single‐voxel filters in the penultimate RAKI layer, batch normalization, and no training dropout with the split‐slice augmented training dataset. Networks trained without the split‐slice technique showed symptoms of network over‐fitting. Conclusions: Split‐slice training for simultaneous multi‐slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance. [ABSTRACT FROM AUTHOR]

Objective: Digital breast tomosynthesis (DBT) has significantly improved cancer detection capabilities through its identification of subtle findings often imperceptible on 2D digital mammography, particularly architectural distortion (AD). The purpose of this study was to analyze of suspicious AD detected on screening DBT to evaluate the incidence of malignancy and to determine other patient or imaging characteristics in these cases as possible predictors of malignancy. Methods: This was an IRB approved retrospective analysis of subjects with AD detected on DBT screening mammography who were given a biopsy recommendation between January 1, 2016, and June 30, 2018. Univariate analysis of various imaging characteristics and patient high-risk factors was performed for statistical correlation with diagnosis of malignancy. Results: In the 218 DBT-detected AD findings with a final BI-RADS assessment of 4 or 5 on diagnostic workup, 94 (43.1%) yielded malignancy, 57 (26.2%) were classified as high-risk, and 67 (30.7%) were benign. There was a strong statistically significant association with malignancy in the cases with an US correlate (P < 0.0001). There was a statistically significant inverse correlation between malignancy and one-view findings (P = 0.0002). The presence of AD on 2D (P = 0.005) or synthetic 2D views (P = 0.002) showed statistically significant correlations with malignancy, whereas breast density or high-risk factors (P = 0.316) did not. Conclusion: AD detected on DBT that persists on further workup and has no explainable cause should be considered suspicious for malignancy. Identification of the AD on both standard mammographic views and the presence of an US correlate significantly increase the probability of malignancy. [ABSTRACT FROM AUTHOR]

Purpose : Due to host‐mediated adverse reaction to metallic debris, there is an increasing need for noninvasive assessment of the soft tissue surrounding large joint arthroplasties. Quantitative T2 mapping can be beneficial for tissue characterization and early diagnosis of tissue pathology but current T2 mapping techniques lack the capability to image near metal hardware. A novel multi‐spectral T2 mapping technique is proposed to address this unmet need. Methods : A T2 mapping pulse sequence based on routinely implemented 3D multi‐spectral imaging (3D‐MSI) pulse sequences is described and demonstrated. The 3D‐MSI pulse sequence is altered to acquire images at 2 echo times. Phantom and knee experiments were performed to assess the quantitative capabilities of the sequence in comparison to a commercially available T2 mapping sequence. The technique was demonstrated for use within a clinical protocol in 2 total hip arthroplasty (THA) cases to assess T2 variations within the periprosthetic joint space. Results : The proposed multi‐spectral T2 mapping technique agreed, within experimental errors, with T2 values derived from a commercially available clinical standard of care T2 mapping sequence. The same level of agreement was observed in quantitative phantoms and in vivo experiments. In THA cases, the method was able to assess variations of T2 within the synovial envelope immediately adjacent to implant interfaces. Conclusions : The proposed 3D‐MSI T2 mapping sequence was successfully demonstrated in assessing tissue T2 variations near metal implants. [ABSTRACT FROM AUTHOR]

Purpose The need for diffusion-weighted-imaging (DWI) near metallic implants is becoming increasingly relevant for a variety of clinical diagnostic applications. Conventional DWI methods are significantly hindered by metal-induced image artifacts. A novel approach relying on multispectral susceptibility artifact reduction techniques is presented to address this unmet need. Methods DWI near metal implants is achieved through a combination of several advanced MRI acquisition technologies. Previously described approaches to Carr-Purcell-Meiboom-Gill spin-echo train DWI sequences using the periodically rotated overlapping parallel lines with enhanced reconstruction are combined with multispectral-imaging metal artifact reduction principles to provide DWI with substantially reduced artifact levels. The presented methods are applied to limited sets of slices over areas of sarcoma risk near six implanted devices. Results Using the presented methods, DWI assessment without bulk image distortions is demonstrated in the immediate vicinity of metallic interfaces. In one subject, the apparent diffusion coefficient was reduced in a region of suspected sarcoma directly adjacent to fixation hardware. Conclusions An initial demonstration of minimal-artifact multispectral DWI in the near vicinity of metallic hardware is described and successfully demonstrated on clinical subjects. Magn Reson Med 79:987-993, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Recent work on blind compressed sensing (BCS) has shown that exploiting sparsity in dictionaries that are learnt directly from the data at hand can outperform compressed sensing (CS) that uses fixed dictionaries. A challenge with BCS however is the large computational complexity during its optimization, which limits its practical use in several MRI applications. In this paper, we propose a novel optimization algorithm that utilize variable splitting strategies to significantly improve the convergence speed of the BCS optimization. The splitting allows us to efficiently decouple the sparse coefficient, and dictionary update steps from the data fidelity term, resulting in subproblems that take closed form analytical solutions, which otherwise require slower iterative conjugate gradient algorithms. Through experiments on multi coil parametric MRI data, we demonstrate the superior performance of BCS over conventional CS schemes, while achieving convergence speed up factors of over 10 fold over the previously proposed implementation of the BCS algorithm.