Cranial bone imaging using ultrashort echo-time bone-selective MRI as an alternative to gradient-echo based "black-bone" techniques.

OBJECTIVES: CT is the clinical standard for surgical planning of craniofacial abnormalities in pediatric patients. This study evaluated three MRI cranial bone imaging techniques for their strengths and limitations as a radiation-free alternative to CT. METHODS: Ten healthy adults were scanned at 3 T with three MRI sequences: dual-radiofrequency and dual-echo ultrashort echo time sequence (DURANDE), zero echo time (ZTE), and gradient-echo (GRE). DURANDE bright-bone images were generated by exploiting bone signal intensity dependence on RF pulse duration and echo time, while ZTE bright-bone images were obtained via logarithmic inversion. Three skull segmentations were derived, and the overlap of the binary masks was quantified using dice similarity coefficient. Craniometric distances were measured, and their agreement was quantified. RESULTS: There was good overlap of the three masks and excellent agreement among craniometric distances. DURANDE and ZTE showed superior air-bone contrast (i.e., sinuses) and soft-tissue suppression compared to GRE. DISCUSSIONS: ZTE has low levels of acoustic noise, however, ZTE images had lower contrast near facial bones (e.g., zygomatic) and require effective bias-field correction to separate bone from air and soft-tissue. DURANDE utilizes a dual-echo subtraction post-processing approach to yield bone-specific images, but the sequence is not currently manufacturer-supported and requires scanner-specific gradient-delay corrections.

Quantification of whole-organ individual and bilateral renal metabolic rate of oxygen.

PURPOSE: Renal metabolic rate of oxygen (rMRO2 ) is a potentially important biomarker of kidney function. The key parameters for rMRO2 quantification include blood flow rate (BFR) and venous oxygen saturation (SvO2 ) in a draining vessel. Previous approaches to quantify renal metabolism have focused on the single organ. Here, both kidneys are considered as one unit to quantify bilateral rMRO2 . A pulse sequence to facilitate bilateral rMRO2 quantification is introduced. METHODS: To quantify bilateral rMRO2 , measurements of BFR and SvO2 are made along the inferior vena cava (IVC) at suprarenal and infrarenal locations. From the continuity equation, these four parameters can be related to derive an expression for bilateral rMRO2 . The recently reported K-MOTIVE pulse sequence was implemented at four locations: left kidney, right kidney, suprarenal IVC, and infrarenal IVC. A dual-band variant of K-MOTIVE (db-K-MOTIVE) was developed by incorporating simultaneous-multi-slice imaging principles. The sequence simultaneously measures BFR and SvO2 at suprarenal and infrarenal locations in a single pass of 21 s, yielding bilateral rMRO2 . RESULTS: SvO2 and BFR are higher in suprarenal versus infrarenal IVC, and the renal veins are highly oxygenated (SvO2 >90%). Bilateral rMRO2 quantified in 10 healthy subjects (8 M, 30 ± 8 y) was found to be 291 ± 247 and 349 ± 300 (µmolO2 /min)/100 g, derived from K-MOTIVE and db-K-MOTIVE, respectively. In comparison, total rMRO2 from combining left and right was 329 ± 273 (µmolO2 /min)/100 g. CONCLUSION: The present work demonstrates that bilateral rMRO2 quantification is feasible with fair reproducibility and physiological plausibility. The indirect method is a promising approach to compute bilateral rMRO2 when individual rMRO2 quantification is difficult.

Six-minute, in vivo MRI quantification of proximal femur trabecular bone 3D microstructure.

BACKGROUND: Assessment of proximal femur trabecular bone microstructure in vivo by magnetic resonance imaging has recently been validated for acquiring information independent of bone mineral density in osteoporotic patients. However, the requisite signal-to-noise ratio (SNR) and resolution for interrogation of the trabecular microstructure at this anatomical location prolongs the scan duration and renders the imaging protocol clinically infeasible. Parallel imaging and compressed sensing (PICS) techniques can reduce the scan duration of the imaging protocol without substantially compromising image quality. The present work investigates the limits of acceleration for a commonly used PICS technique, ℓ1-ESPIRiT, for the purpose of quantifying measures of trabecular bone microarchitecture. Based on a desired error tolerance, a six-minute, prospectively accelerated variant of the imaging protocol was developed and assessed for intersession reproducibility and agreement with the longer reference scan. PURPOSE: To investigate the limits of acceleration for MRI-based trabecular bone quantification by parallel imaging and compressed sensing reconstruction, and to develop a prototypical imaging protocol for assessing the proximal femur microstructure in a clinically practical scan time. METHODS: Healthy participants (n = 11) were scanned by a 3D balanced steady-state free precession (bSSFP) sequence satisfying the Nyquist criterion with a scan duration of about 18 min. The raw data were retrospectively undersampled and reconstructed to mimic various acceleration factors ranging from 2 to 6. Trabecular volumes-of-interest in four major femoral regions (greater trochanter, intertrochanteric region, femoral neck, and femoral head) were analyzed and six relevant measures of trabecular bone microarchitecture (bone volume fraction, surface-to-curve ratio, erosion index, elastic modulus, trabecular thickness, plates-to-rods ratio) were obtained for images of all accelerations. To assess agreement, median percent error and intraclass correlation coefficients (ICCs) were computed using the fully-sampled data as reference. Based on this analysis, a prospectively 3-fold accelerated sequence with a duration of about 6 min was developed and the analysis was repeated. RESULTS: A prospective acceleration factor of 3 demonstrated comparable performance in reproducibility and absolute agreement to the fully-sampled scan. The median CoV over all image-derived metrics was generally <6 % and ICCs >0.70. Also, measurements from prospectively 3-fold accelerated scans demonstrated in general median percent errors of <7 % and ICCs >0.70. CONCLUSION: The present work proposes a method to make in vivo quantitative assessment of proximal femur trabecular microstructure with a clinically practical scan duration of about 6 min.

Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus.

Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times. PURPOSE: To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity. METHODS: In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples. RESULTS: The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2 ≥ 0.97, ICC ≥ 0.98) and geometry (R2 ≥ 0.82, ICC ≥ 0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2 ≥ 0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P ≤ .002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R| ≥ 0.88) and DXA (|R| ≥ 0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity. CONCLUSION: This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

MRI Quantification of Cortical Bone Porosity, Mineralization, and Morphologic Structure in Postmenopausal Osteoporosis.

Background Preclinical studies have suggested that solid-state MRI markers of cortical bone porosity, morphologic structure, mineralization, and osteoid density are useful measures of bone health. Purpose To explore whether MRI markers of cortical bone porosity, morphologic structure, mineralization, and osteoid density are affected in postmenopausal osteoporosis (OP) and to examine associations between MRI markers and bone mineral density (BMD) in postmenopausal women. Materials and Methods In this single-center study, postmenopausal women were prospectively recruited from January 2019 to October 2020 into two groups: participants with OP who had not undergone treatment, defined as having any dual-energy x-ray absorptiometry (DXA) T-score of -2.5 or less, and age-matched control participants without OP (hereafter, non-OP). Participants underwent MRI in the midtibia, along with DXA in the hip and spine, and peripheral quantitative CT in the midtibia. Specifically, MRI measures of cortical bone porosity (pore water and total water), osteoid density (bound water [BW]), morphologic structure (cortical bone thickness), and mineralization (phosphorous [P] density [31P] and 31P-to-BW concentration ratio) were quantified at 3.0 T. MRI measures were compared between OP and non-OP groups and correlations with BMD were assessed. Results Fifteen participants with OP (mean age, 63 years ± 5 [SD]) and 19 participants without OP (mean age, 65 years ± 6) were evaluated. The OP group had elevated pore water (11.6 mol/L vs 9.5 mol/L; P = .007) and total water densities (21.2 mol/L vs 19.7 mol/L; P = .03), and had lower cortical bone thickness (4.8 mm vs 5.6 mm; P < .001) and 31P density (6.4 mol/L vs 7.5 mol/L; P = .01) than the non-OP group, respectively, although there was no evidence of a difference in BW or 31P-to-BW concentration ratio. Pore and total water densities were inversely associated with DXA and peripheral quantitative CT BMD (P < .001), whereas cortical bone thickness and 31P density were positively associated with DXA and peripheral quantitative CT BMD (P = .01). BW, 31P density, and 31P-to-BW concentration ratio were positively associated with DXA (P < .05), but not with peripheral quantitative CT. Conclusion Solid-state MRI of cortical bone was able to help detect potential impairments in parameters reflecting porosity, morphologic structure, and mineralization in postmenopausal osteoporosis. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Investigating the relationship between emotional granularity and cardiorespiratory physiological activity in daily life.

Emotional granularity describes the ability to create emotional experiences that are precise and context-specific. Despite growing evidence of a link between emotional granularity and mental health, the physiological correlates of granularity have been under-investigated. This study explored the relationship between granularity and cardiorespiratory physiological activity in everyday life, with particular reference to the role of respiratory sinus arrhythmia (RSA), an estimate of vagal influence on the heart often associated with positive mental and physical health outcomes. Participants completed a physiologically triggered experience-sampling protocol including ambulatory recording of electrocardiogram, impedance cardiogram, movement, and posture. At each prompt, participants generated emotion labels to describe their current experience. In an end-of-day survey, participants elaborated on each prompt by rating the intensity of their experience on a standard set of emotion adjectives. Consistent with our hypotheses, individuals with higher granularity exhibited a larger number of distinct patterns of physiological activity during seated rest, and more situationally precise patterns of activity during emotional events: granularity was positively correlated with the number of clusters of cardiorespiratory physiological activity discovered in seated rest data, as well as with the performance of classifiers trained on event-related changes in physiological activity. Granularity was also positively associated with RSA during seated rest periods, although this relationship did not reach significance in this sample. These findings are consistent with constructionist accounts of emotion that propose concepts as a key mechanism underlying individual differences in emotional experience, physiological regulation, and physical health.

Automatic detection of simulated motion blur in mammograms.

PURPOSE: To use machine-learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision-making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. METHODS: Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point-spread-function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super-sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine-learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six-way classification. RESULTS: For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. CONCLUSIONS: Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists' lesion detection performance. As of this date, no other study has investigated the ability of machine-learning classifiers and BM operators to detect motion blur in mammograms.