Purpose

To validate the potential of quantifying R₂ -R_1ρ using one pair of signals with T_1ρ preparation and T₂ preparation incorporated to magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) acquisition and to find an optimal preparation time (T_prep ) for in vivo knee MRI.

Methods

Bloch equation simulations were first performed to assess the accuracy of quantifying R₂ -R_1ρ using T_1ρ - and T₂ -prepared signals with an equivalent T_prep . For validation of this technique in comparison to the conventional approach that calculates R₂ -R_1ρ after estimating both T₂ and T_1ρ , phantom experiments and in vivo validation with five healthy subjects and five osteoarthritis patients were performed at a clinical 3T scanner.

Results

Bloch equation simulations demonstrated that the accuracy of this efficient R₂ -R_1ρ quantification method and the optimal T_prep can be affected by image signal-to-noise ratio (SNR) and tissue relaxation times, but quantification can be closest to the reference with an around 25 ms T_prep for knee cartilage. Phantom experiments demonstrated that the proposed method can depict R₂ -R_1ρ changes with agarose gel concentration. With in vivo data, significant correlation was observed between cartilage R₂ -R_1ρ measured from the conventional and the proposed methods, and a T_prep of 25.6 ms provided the most agreement by Bland-Altman analysis. R₂ -R_1ρ was significantly lower in patients than in healthy subjects for most cartilage compartments.

Conclusion

As a potential biomarker to indicate cartilage degeneration, R₂ -R_1ρ can be efficiently measured using one pair of T_1ρ -prepared and T₂ -prepared signals with an optimal T_prep considering cartilage relaxation times and image SNR.

This study characterized the distribution of [¹⁸ F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [¹⁸ F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T_1ρ /T₂ magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T_1ρ /T₂ quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (K_pat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and K_pat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with K_pat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [¹⁸ F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA. © 2019 The Authors. Journal of Orthopaedic Research^® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society J Orthop Res 37:2671-2680, 2019.

To explore bone shape features that are associated with patellofemoral joint (PFJ) osteoarthritic features. Thirty subjects with PFJ degeneration (six males, 53.2 ± 9.8 years) and 23 controls (12 males, 48.1 ± 10.6 years) were included. Magnetic resonance (MR) assessment was performed to provide bone segmentation, morpholgocial grading, and cartilage relaxation times. In addition, subject self-reported symptoms were reported. Logistic regressions were used to identify the shape features that were associated with the presence and worsening of PFJ morphological lesions over 3 years, and worsening of self-reported symptoms. Statistical parametric mapping was used to evaluate the associations between shape features and cartilage relaxation times at 3 years. Results indicated that subjects with PFJ degeneration exhibited a trochlea with longer lateral condyle and shallower trochlear groove (adjusted odds ratio [OR] = 0.30; 95% confidence interval [CI]: 0.10, 0.86; P = .025). Subjects with worsening of PFJ degeneration exhibited a patella with equally distributed facets (adjusted OR = 3.14; 95% CI: 1.05, 9.37; P = .040) and lateral bump (adjusted OR = 0.14; 95% CI: 0.02, 0.83; P = .030). No shape features were associated with worsening of self-reported symptoms. Elevated T_1ρ and T₂ times at 3 years were associated with a patella with a lateral hook, equally distributed facets, round and thick as well as a trochlea larger in size (R = 0.38~0.46, P = .015~.025). The study demonstrated the ability of 3D statistical shape modeling to quantify patella and trochlear bone shape features that are associated with the presence and progression of PFJ osteoarthritic features.

OBJECTIVES: To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine magnetic resonance imaging (MRI). METHODS: Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived. RESULTS: Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2≥ 0.86 for disc heights and r2≥ 0.98 for vertebral body volumes). CONCLUSIONS: This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

Background

Amino acid PET imaging of brain tumors has been shown to play an important role in predicting tumor grade, delineation of tumor margins, and differentiating tumor recurrence from the background of postradiation changes, but is not commonly used in clinical practice because of high cost. We propose that PET/MRI imaging of patients grouped to the day of tracer radiosynthesis will significantly decrease the cost of PET imaging, which will improve patient access to PET.

Methods

Seventeen patients with either primary brain tumors or metastatic brain tumors were recruited for imaging on 3T PET/MRI and were scanned on 4 separate days in groups of 3 to 5 patients. The first group of consecutively imaged patients contained 3 patients, followed by 2 groups of 5 patients, and a last group of 4 patients.

Results

For each of the patients, standard of care gadolinium-enhanced MRI and dynamic PET imaging with ¹⁸F-FDOPA amino acid tracer was obtained. The total cost savings of scanning 17 patients in batches of 4 as opposed to individual radiosynthesis was 48.5% ($28 321). Semiquantitative analysis of tracer uptake in normal brain were performed with appropriate accumulation and expected subsequent washout.

Conclusion

Amino acid PET tracers have been shown to play a critical role in the characterization of brain tumors but their adaptation to clinical practice has been limited because of the high cost of PET. Scheduling patient imaging to maximally use the radiosynthesis of imaging tracer significantly reduces the cost of PET and results in increased availability of PET tracer use in neuro-oncology.

Objective

To assess differences in biochemical composition of the deep cartilage layer in subjects with type 2 diabetes mellitus (T2DM) and nondiabetic controls using UTE (ultra-short echo time) T2* mapping and to investigate the association of vascular health and UTE T2* measurements.

Design

Ten subjects with T2DM matched for age, sex, and body mass index with 10 nondiabetic controls. A 3D UTE sequence with 6 echo times was acquired using 3T magnetic resonance imaging of the knee. For UTE T2* analysis, the deep cartilage layer was segmented and analyzed in 5 compartments (patella, medial, and lateral femur and tibia). The ankle brachial index (ABI) was obtained in all subjects. Linear regression analyses were used to assess associations of T2DM and UTE T2* relaxation times and the associations of ABI measurements and UTE measurements.

Results

Compared with nondiabetic controls, T2DM subjects had significantly lower mean T2*-UTE in the patella (mean difference 4.87 ms; 95% confidence interval [CI] 1.09-8.65; P = 0.015), the lateral tibia (mean difference 2.26 ms; 95% CI 0.06-4.45; P = 0.045), and the lateral femur (mean difference 4.96 ms; 95% CI 0.19-9.73; P = 0.043). Independent of diabetic status, subjects with higher ABI values, indicating better vascular health, had higher T2*-UTE of the patella (coefficient 15.2; 95% CI 3.3-21.4; P = 0.017), the medial tibia (coefficient 9.8; 95% CI 1.0-18.6; P = 0.031), and the lateral femur (coefficient 18.8; 95% CI 3.3-34.3; P = 0.021).

Conclusions

T2*-UTE measurements of the deep cartilage layer were consistently lower in subjects with T2DM and in subjects with impaired vascular health, likely indicating increased mineralization of this layer.