Maximum spherical mean value filtering for whole-brain QSM.

PURPOSE: To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. THEORY AND METHODS: Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. RESULTS: Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. CONCLUSION: The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.

Parenchymal CSF fraction is a measure of brain glymphatic clearance and positively associated with amyloid beta deposition on PET.

INTRODUCTION: Mapping of microscopic changes in the perivascular space (PVS) of the cerebral cortex, beyond magnetic resonance-visible PVS in white matter, may enhance our ability to diagnose Alzheimer's disease (AD) early. METHODS: We used the cerebrospinal fluid (CSF) water fraction (CSFF), a magnetic resonance imaging-based biomarker, to characterize brain parenchymal CSF water, reflecting microscopic PVS in parenchyma. We measured CSFF and amyloid beta (Aß) using 11 C Pittsburgh compound B positron emission tomography to investigate their relationship at both the subject and voxel levels. RESULTS: Our research has demonstrated a positive correlation between the parenchymal CSFF, a non-invasive imaging biomarker indicative of parenchymal glymphatic clearance, and Aß deposition, observed at both individual and voxel-based assessments in the posterior cingulate cortex. DISCUSSION: This study shows that an increased parenchymal CSFF is associated with Aß deposition, suggesting that CSFF could serve as a biomarker for brain glymphatic clearance, which can be used to detect early fluid changes in PVS predisposing individuals to the development of AD. HIGHLIGHTS: Cerebrospinal fluid fraction (CSFF) could be a biomarker of parenchymal perivascular space. CSFF is positively associated with amyloid beta (Aß) deposition at subject level. CSFF in an Aß+ region is higher than in an Aß- region in the posterior cingulate cortex. Correspondence is found between Aß deposition and glymphatic clearance deficits measured by CSFF.

LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO-QSM.git.

A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis.

OBJECTIVES: Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. METHODS: We performed 3 studies: (1) a cross-sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso-intense, hypo-intense, hyperintense, lesions with hypo-intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology-QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. RESULTS: At baseline, hypo- and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2-year follow-up, hypo-/iso-intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo-/iso-intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). INTERPRETATION: These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486-502.

QSM Throughout the Body.

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Single-Administration Long-Acting Microarray Patch with Ultrahigh Loading Capacity and Multiple Releases of Thermally Stable Antibodies.

Current antibody (Ab) therapies require development of stable formulations and an optimal delivery system. Here, we present a new strategy to create a single-administration long-lasting Ab-delivery microarray (MA) patch, which can carry high doses of thermally stabilized Abs. The MA fabricated by an additive three-dimensional manufacturing technology can be fully embedded into the skin via a single application to deliver doses of Abs at multiple programmable time points, thus sustaining Ab concentrations in systemic circulation. We developed an MA formulation that stabilized and delivered human immunoglobulins (hIg) in a time-controlled manner while maintaining their structure and functionality. As an example, the b12 Ab─a broadly neutralizing Ab against HIV-1─maintained antiviral activity in vitro after MA manufacturing and heat exposure. Pharmacokinetic studies of MA patch-delivered hIg in rats successfully provided a proof of concept for concurrent and time-delayed Ab delivery. These MA patches codeliver different Abs, providing a tool for expanded protection against viral infections or combination HIV therapy and prevention.

Biodegradable nanofiber-based piezoelectric transducer.

Piezoelectric materials, a type of "smart" material that generates electricity while deforming and vice versa, have been used extensively for many important implantable medical devices such as sensors, transducers, and actuators. However, commonly utilized piezoelectric materials are either toxic or nondegradable. Thus, implanted devices employing these materials raise a significant concern in terms of safety issues and often require an invasive removal surgery, which can damage directly interfaced tissues/organs. Here, we present a strategy for materials processing, device assembly, and electronic integration to 1) create biodegradable and biocompatible piezoelectric PLLA [poly(l-lactic acid)] nanofibers with a highly controllable, efficient, and stable piezoelectric performance, and 2) demonstrate device applications of this nanomaterial, including a highly sensitive biodegradable pressure sensor for monitoring vital physiological pressures and a biodegradable ultrasonic transducer for blood-brain barrier opening that can be used to facilitate the delivery of drugs into the brain. These significant applications, which have not been achieved so far by conventional piezoelectric materials and bulk piezoelectric PLLA, demonstrate the PLLA nanofibers as a powerful material platform that offers a profound impact on various medical fields including drug delivery, tissue engineering, and implanted medical devices.

Peripheral and Central Iron Measures in Alcohol Use Disorder and Aging: A Quantitative Susceptibility Mapping Pilot Study.

Chronic excessive alcohol use has neurotoxic effects, which may contribute to cognitive decline and the risk of early-onset dementia. Elevated peripheral iron levels have been reported in individuals with alcohol use disorder (AUD), but its association with brain iron loading has not been explored. We evaluated whether (1) serum and brain iron loading are higher in individuals with AUD than non-dependent healthy controls and (2) serum and brain iron loading increase with age. A fasting serum iron panel was obtained and a magnetic resonance imaging scan with quantitative susceptibility mapping (QSM) was used to quantify brain iron concentrations. Although serum ferritin levels were higher in the AUD group than in controls, whole-brain iron susceptibility did not differ between groups. Voxel-wise QSM analyses revealed higher susceptibility in a cluster in the left globus pallidus in individuals with AUD than controls. Whole-brain iron increased with age and voxel-wise QSM indicated higher susceptibility with age in various brain areas including the basal ganglia. This is the first study to analyze both serum and brain iron loading in individuals with AUD. Larger studies are needed to examine the effects of alcohol use on iron loading and its associations with alcohol use severity, structural and functional brain changes, and alcohol-induced cognitive impairments.

Subsecond accurate myelin water fraction reconstruction from FAST-T2 data with 3D UNET.

PURPOSE: To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS: The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS: Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION: Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET). METHODS: The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test. RESULTS: In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps. CONCLUSION: QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Cerebral Microbleeds Are Associated With Increased Brain Iron and Cognitive Impairment in Patients With Cerebral Small Vessel Disease: A Quantitative Susceptibility Mapping Study.

BACKGROUND: Cerebral microbleeds (CMBs) have been recognized to play an important role in cognitive impairment of cerebral small vessel disease (CSVD) patients. However, the mechanism of this effect is still unclear. PURPOSE: Comparing the susceptibility values in the selected subcortical gray matter structures of CSVD patients without CMBs (CSVD-N) and with CMBs (CSVD-C) as well as healthy controls (HCs). STUDY TYPE: Prospective. SUBJECTS: Sixty-nine CSVD patients and 28 HCs were included; 24 CSVD patients (34.78%) had CMBs and 45 CSVD patients (65.22%) had no CMBs. FIELD STRENGTH/SEQUENCE: All subjects were imaged on a 3.0 T MR scanner. The protocol consisted of a three-dimensional (3D) T1-weighted sequence and a 3D multi-echo gradient echo (mGRE) sequence. Brain QSM maps were computed from mGRE data using the morphology-enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference algorithm (MEDI+0). ASSESSMENT: The mean susceptibility value within each region of interest was recorded. All participants underwent the cognitive assessment. Brain iron deposition burden of CMB lesions of every CSVD-C patient was computed. STATISTICAL TESTS: One-way analysis of variance test followed by Tukey's honest significance test and Kruskal-Wallis test were used with significance level of 0.05. Stepwise multivariate linear analysis was used to explore the factors influencing cognitive scores. RESULTS: Montreal cognitive assessment (MoCA), trail-making test (TMT)-A and TMT-B scores in the three groups were significantly different (all P < 0.05). Stepwise multivariate linear regression analysis revealed that the factors influenced MoCA scores were having CMBs (P < 0.05), white matter hyperintensities (P < 0.05), lacunes (P < 0.05) in brain, and the brain iron deposition burden of CMB lesions (P < 0.05) and for TMT scores (TMT-A + TMT-B), the influencing factors were age (P < 0.05), education years (P < 0.05), and the brain iron deposition burden of CMB lesions (P < 0.05). DATA CONCLUSION: The higher iron deposition burden of CMB lesions in brain may be an imaging quantitative marker of cognitive decline in patients with CSVD-C. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Lesion features on magnetic resonance imaging discriminate multiple sclerosis patients.

BACKGROUND: Magnetic resonance imaging (MRI) provides insight into various pathological processes in multiple sclerosis (MS) and may provide insight into patterns of damage among patients. OBJECTIVE: We sought to determine if MRI features have clinical discriminative power among a cohort of MS patients. METHODS: Ninety-six relapsing remitting and seven progressive MS patients underwent myelin water fraction (MWF) imaging and conventional MRI for cortical thickness and thalamic volume. Patients were clustered based on lesion level MRI features using an agglomerative hierarchical clustering algorithm based on principal component analysis (PCA). RESULTS: One hundred and three patients with 1689 MS lesions were analyzed. PCA on MRI features demonstrated that lesion MWF and volume distributions (characterized by 25th, 50th, and 75th percentiles) accounted for 87% of the total variability based on four principal components. The best hierarchical cluster confirmed two distinct patient clusters. The clustering features in order of importance were lesion median MWF, MWF 25th, MWF 75th, volume 75th percentiles, median individual lesion volume, total lesion volume, cortical thickness, and thalamic volume (all p values <0.01368). The clusters were associated with patient Expanded Disability Status Scale (EDSS) (n = 103, p = 0.0338) at baseline and at 5 years (n = 72, p = 0.0337). CONCLUSIONS: These results demonstrate that individual MRI features can identify two patient clusters driven by lesion-based values, and our unique approach is an analysis blinded to clinical variables. The two distinct clusters exhibit MWF differences, most likely representing individual remyelination capabilities among different patient groups. These findings support the concept of patient-specific pathophysiological processes and may guide future therapeutic approaches.

Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging.

Damage to the myelin sheath and the neuroaxonal unit is a cardinal feature of multiple sclerosis; however, a detailed characterization of the interaction between myelin and axon damage in vivo remains challenging. We applied myelin water and multi-shell diffusion imaging to quantify the relative damage to myelin and axons (i) among different lesion types; (ii) in normal-appearing tissue; and (iii) across multiple sclerosis clinical subtypes and healthy controls. We also assessed the relation of focal myelin/axon damage with disability and serum neurofilament light chain as a global biological measure of neuroaxonal damage. Ninety-one multiple sclerosis patients (62 relapsing-remitting, 29 progressive) and 72 healthy controls were enrolled in the study. Differences in myelin water fraction and neurite density index were substantial when lesions were compared to healthy control subjects and normal-appearing multiple sclerosis tissue: both white matter and cortical lesions exhibited a decreased myelin water fraction and neurite density index compared with healthy (P < 0.0001) and peri-plaque white matter (P < 0.0001). Periventricular lesions showed decreased myelin water fraction and neurite density index compared with lesions in the juxtacortical region (P < 0.0001 and P < 0.05). Similarly, lesions with paramagnetic rims showed decreased myelin water fraction and neurite density index relative to lesions without a rim (P < 0.0001). Also, in 75% of white matter lesions, the reduction in neurite density index was higher than the reduction in the myelin water fraction. Besides, normal-appearing white and grey matter revealed diffuse reduction of myelin water fraction and neurite density index in multiple sclerosis compared to healthy controls (P < 0.01). Further, a more extensive reduction in myelin water fraction and neurite density index in normal-appearing cortex was observed in progressive versus relapsing-remitting participants. Neurite density index in white matter lesions correlated with disability in patients with clinical deficits (P < 0.01, beta = -10.00); and neurite density index and myelin water fraction in white matter lesions were associated to serum neurofilament light chain in the entire patient cohort (P < 0.01, beta = -3.60 and P < 0.01, beta = 0.13, respectively). These findings suggest that (i) myelin and axon pathology in multiple sclerosis is extensive in both lesions and normal-appearing tissue; (ii) particular types of lesions exhibit more damage to myelin and axons than others; (iii) progressive patients differ from relapsing-remitting patients because of more extensive axon/myelin damage in the cortex; and (iv) myelin and axon pathology in lesions is related to disability in patients with clinical deficits and global measures of neuroaxonal damage.

Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging.

We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and can be used in applications such as studying the temporal dynamics of chronic active MS lesions. Our lesion age estimation models use first order radiomic features over a lesion derived from conventional T1 (T1w) and T2 weighted (T2w) and fluid attenuated inversion recovery (FLAIR), T1w with gadolinium contrast (T1w+c), and Quantitative Susceptibility Mapping (QSM) MRI sequences as well as demographic information. For this analysis, we have a total of 32 patients with 53 new lesions observed at 244 time points. A one or two step random forest model for lesion age is fit on a training set using a lesion volume cutoff of 15 mm3 or 50 mm3. We explore the performance of nine different modeling scenarios that included various combinations of the MRI sequences and demographic information and a one or two step random forest models, as well as simpler models that only uses the mean radiomic feature from each MRI sequence. The best performing model on a validation set is a model that uses a two-step random forest model on the radiomic features from all of the MRI sequences with demographic information using a lesion volume cutoff of 50 mm3. This model has a mean absolute error of 7.23 months (95% CI: [6.98, 13.43]) and a median absolute error of 5.98 months (95% CI: [5.26, 13.25]) in the validation set. For this model, the predicted age and actual age have a statistically significant association (p-value <0.001) in the validation set.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD.

PURPOSE: To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). METHODS: Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test. RESULTS: In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001). CONCLUSION: The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.

Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.

PURPOSE: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. METHODS: The current T2∗ -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T2∗ -IDEAL. RESULTS: All DNN methods generated consistent water/fat separation results that agreed well with T2∗ -IDEAL under proper initialization. CONCLUSION: The water/fat separation problem can be solved using unsupervised deep neural networks.

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network.

PURPOSE: Proof-of-concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer-labeled MRI data. THEORY AND METHODS: To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time-resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety's method that uses a global arterial input function. Multiple post-label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility. RESULTS: Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety's method (45.7%, 2.5-fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety's method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region. CONCLUSIONS: QTM flow velocity mapping is feasible from multi-delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety's method with deconvolution in time only.

White matter tracts related to memory and emotion in very preterm children.

BACKGROUND: Very preterm (VP) children are at risk of memory and emotional impairments; however, the neural correlates remain incompletely defined. This study investigated the effect of VP birth on white matter tracts traditionally related to episodic memory and emotion. METHODS: The cingulum, fornix, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were reconstructed using tractography in 144 VP children and 33 full-term controls at age 7 years. RESULTS: Compared with controls, VP children had higher axial, radial, and mean diffusivities and neurite orientation dispersion, and lower volume and neurite density in the fornix, along with higher neurite orientation dispersion in the medial forebrain bundle. Support vector classification models based on tract measures significantly classified VP children and controls. Higher fractional anisotropy and lower diffusivities in the cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were associated with better episodic memory, independent of key perinatal risk factors. Support vector regression models using tract measures did not predict episodic memory and emotional outcomes. CONCLUSIONS: Altered tract structure is related to adverse episodic memory outcomes in VP children, but further research is required to determine the ability of tract structure to predict outcomes of individual children. IMPACT: We studied white matter fibre tracts thought to be involved in episodic memory and emotion in VP and full-term children using diffusion magnetic resonance imaging and machine learning. VP children have altered fornix and medial forebrain bundle structure compared with full-term children. Altered tract structure can be detected using machine learning, which accurately classified VP and full-term children using tract data. Altered cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation structure was associated with poorer episodic memory skills using linear regression. The ability of tract structure to predict episodic memory and emotional outcomes of individual children based on support vector regression was limited.

Biodegradable Piezoelectric Force Sensor.

Measuring vital physiological pressures is important for monitoring health status, preventing the buildup of dangerous internal forces in impaired organs, and enabling novel approaches of using mechanical stimulation for tissue regeneration. Pressure sensors are often required to be implanted and directly integrated with native soft biological systems. Therefore, the devices should be flexible and at the same time biodegradable to avoid invasive removal surgery that can damage directly interfaced tissues. Despite recent achievements in degradable electronic devices, there is still a tremendous need to develop a force sensor which only relies on safe medical materials and requires no complex fabrication process to provide accurate information on important biophysiological forces. Here, we present a strategy for material processing, electromechanical analysis, device fabrication, and assessment of a piezoelectric Poly-l-lactide (PLLA) polymer to create a biodegradable, biocompatible piezoelectric force sensor, which only employs medical materials used commonly in Food and Drug Administration-approved implants, for the monitoring of biological forces. We show the sensor can precisely measure pressures in a wide range of 0-18 kPa and sustain a reliable performance for a period of 4 d in an aqueous environment. We also demonstrate this PLLA piezoelectric sensor can be implanted inside the abdominal cavity of a mouse to monitor the pressure of diaphragmatic contraction. This piezoelectric sensor offers an appealing alternative to present biodegradable electronic devices for the monitoring of intraorgan pressures. The sensor can be integrated with tissues and organs, forming self-sensing bionic systems to enable many exciting applications in regenerative medicine, drug delivery, and medical devices.

Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.

Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.