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.

MRI-based quantification of whole-organ renal metabolic rate of oxygen.

During the early stages of diabetes, kidney oxygen utilization increases. The mismatch between oxygen demand and supply contributes to tissue hypoxia, a key driver of chronic kidney disease. Thus, whole-organ renal metabolic rate of oxygen (rMRO2 ) is a potentially valuable biomarker of kidney function. The key parameters required to determine rMRO2 include the renal blood flow rate (RBF) in the feeding artery and oxygen saturation in the draining renal vein (SvO2 ). However, there is currently no noninvasive method to quantify rMRO2 in absolute physiologic units. Here, a new MRI pulse sequence, Kidney Metabolism of Oxygen via T2 and Interleaved Velocity Encoding (K-MOTIVE), is described, along with evaluation of its performance in the human kidney in vivo. K-MOTIVE interleaves a phase-contrast module before a background-suppressed T2 -prepared balanced steady-state-free-precession (bSSFP) readout to measure RBF and SvO2 in a single breath-hold period of 22 s, yielding rMRO2 via Fick's principle. Variants of K-MOTIVE to evaluate alternative bSSFP readout strategies were studied. Kidney mass was manually determined from multislice gradient recalled echo images. Healthy subjects were recruited to quantify rMRO2 of the left kidney at 3-T field strength (N = 15). Assessments of repeat reproducibility and comparisons with individual measurements of RBF and SvO2 were performed, and the method's sensitivity was evaluated with a high-protein meal challenge (N = 8). K-MOTIVE yielded the following metabolic parameters: T2  = 157 ± 19 ms; SvO2  = 92% ± 6%; RBF = 400 ± 110 mL/min; and rMRO2  = 114 ± 117(µmol O2 /min)/100 g tissue. Reproducibility studies of T2 and RBF (parameters directly measured by K-MOTIVE) resulted in coefficients of variation less than 10% and intraclass correlation coefficients more than 0.75. The high-protein meal elicited an increase in rMRO2 , which was corroborated by serum biomarkers. The K-MOTIVE sequence measures SvO2 and RBF, the parameters necessary to quantify whole-organ rMRO2 , in a single breath-hold. The present work demonstrates that rMRO2 quantification is feasible with good reproducibility. rMRO2 is a potentially valuable physiological biomarker.

Metabolism of oxygen via T2 and interleaved velocity encoding: A rapid method to quantify whole-brain cerebral metabolic rate of oxygen.

PURPOSE: Cerebral metabolic rate of oxygen (CMRO2 ) is an important biomarker of brain function. Key physiological parameters required to quantify CMRO2 include blood flow rate in the feeding arteries and venous oxygen saturation (SvO2 ) in the draining vein. Here, a pulse sequence, metabolism of oxygen via T2 and interleaved velocity encoding (MOTIVE), was developed to measure both parameters simultaneously and enable CMRO2 quantification in a single pass. METHODS: The MOTIVE sequence interleaves a phase-contrast module between a nonselective saturation and a background-suppressed T2 -prepared EPI readout (BGS-EPI) to measure T2 of blood water protons and cerebral blood flow in 20 s or less. The MOTIVE and standalone BGS-EPI sequences were compared against TRUST ("T2 relaxation under spin tagging") in the brain in healthy subjects (N = 24). Variants of MOTIVE to enhance resolution or shorten scan time were explored. Intrasession and intersession reproducibility studies were performed. RESULTS: MOTIVE experiments yielded an average SvO2 of 61 ± 6% in the superior sagittal sinus of the brain and an average cerebral blood flow of 56 ± 10 ml/min/100 g. The bias in SvO2 of MOTIVE and BGS-EPI to TRUST was +2 ± 4% and +1 ± 3%, respectively. The bias in cerebral blood flow of MOTIVE to Cartesian phase-contrast reference was +1 ± 6 ml/min/100 g. CONCLUSIONS: The MOTIVE sequence is an advance over existing T2 -based oximetric methods. It does not require a control image and simultaneously measures SvO2 and flow velocity. The measurements agree well with TRUST and reference phase-contrast sequences. This noninvasive technique enables CMRO2 quantification in under 20 s and is reproducible for in vivo applications.

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.

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.

Modeling Stem Cell Myogenic Differentiation.

The process of stem cell myogenesis (transformation into skeletal muscle cells) includes several stages characterized by the expression of certain combinations of myogenic factors. The first part of this process is accompanied by cell division, while the second part is mainly associated with direct differentiation. The mechanical cues are known to enhance stem cell myogenesis, and the paper focuses on the stem cell differentiation under the condition of externally applied strain. The process of stem cell myogenic differentiation is interpreted as the interplay among transcription factors, targeted proteins and strain-generated signaling molecule, and it is described by a kinetic multi-stage model. The model parameters are optimally adjusted by using the available data from the experiment with adipose-derived stem cells subjected to the application of cyclic uniaxial strains of the magnitude of 10%. The modeling results predict the kinetics of the process of myogenic differentiation, including the number of cells in each stage of differentiation and the rates of differentiation from one stage to another for different strains from 4% to 16%. The developed model can help better understand the process of myogenic differentiation and the effects of mechanical cues on stem cell use in muscle therapies.

A Modeling Insight into Adipose-Derived Stem Cell Myogenesis.

Adipose-derived stem cells (ASCs) are clinically important in regenerative medicine as they are relatively easy to obtain, are characterized by low morbidity, and can differentiate into myogenic progenitor cells. Although studies have elucidated the principal markers, PAX7, Desmin, MyoD, and MHC, the underlying mechanisms are not completely understood. This motivates the application of computational methods to facilitate greater understanding of such processes. In the following, we present a multi-stage kinetic model comprising a system of ordinary differential equations (ODEs). We sought to model ASC differentiation using data from a static culture, where no strain is applied, and a dynamic culture, where 10% strain is applied. The coefficients of the equations have been modulated by those experimental data points. To correctly represent the trajectories, various switches and a feedback factor based on total cell number have been introduced to better represent the biology of ASC differentiation. Furthermore, the model has then been applied to predict ASC fate for strains different from those used in the experimental conditions and for times longer than the duration of the experiment. Analysis of the results reveals unique characteristics of ASC myogenesis under dynamic conditions of the applied strain.