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.

Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra-tissue with low perfusion yet intact cellular integrity-making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.

Osteo-inductive effect of piezoelectric stimulation from the poly(l-lactic acid) scaffolds.

Piezoelectric biomaterials can generate piezoelectrical charges in response to mechanical activation. These generated charges can directly stimulate bone regeneration by triggering signaling pathway that is important for regulating osteogenesis of cells seeded on the materials. On the other hand, mechanical forces applied to the biomaterials play an important role in bone regeneration through the process called mechanotransduction. While mechanical force and electrical charges are both important contributing factors to bone tissue regeneration, they operate through different underlying mechanisms. The utilizations of piezoelectric biomaterials have been explored to serve as self-charged scaffolds which can promote stem cell differentiation and the formation of functional bone tissues. However, it is still not clear how mechanical activation and electrical charge act together on such a scaffold and which factors play more important role in the piezoelectric stimulation to induce osteogenesis. In our study, we found Poly(l-lactic acid) (PLLA)-based piezoelectric scaffolds with higher piezoelectric charges had a more pronounced osteoinductive effect than those with lower charges. This provided a new mechanistic insight that the observed osteoinductive effect of the piezoelectric PLLA scaffolds is likely due to the piezoelectric stimulation they provide, rather than mechanical stimulation alone. Our findings provide a crucial guide for the optimization of piezoelectric material design and usage.

Intense exercise increases dopamine transporter and neuromelanin concentrations in the substantia nigra in Parkinson's disease.

Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons. Exercise has been reported to slow the clinical progression of PD. We evaluated the dopaminergic system of patients with mild and early PD before and after a six-month program of intense exercise. Using 18F-FE-PE2I PET imaging, we measured dopamine transporter (DAT) availability in the striatum and substantia nigra. Using NM-MRI, we evaluated the neuromelanin content in the substantia nigra. Exercise reversed the expected decrease in DAT availability into a significant increase in both the substantia nigra and putamen. Exercise also reversed the expected decrease in neuromelanin concentration in the substantia nigra into a significant increase. These findings suggest improved functionality in the remaining dopaminergic neurons after exercise. Further research is needed to validate our findings and to pinpoint the source of any true neuromodulatory and neuroprotective effects of exercise in PD in large clinical trials.

Ensembling Low Precision Models for Binary Biomedical Image Segmentation.

Segmentation of anatomical regions of interest such as vessels or small lesions in medical images is still a difficult problem that is often tackled with manual input by an expert. One of the major challenges for this task is that the appearance of foreground (positive) regions can be similar to background (negative) regions. As a result, many automatic segmentation algorithms tend to exhibit asymmetric errors, typically producing more false positives than false negatives. In this paper, we aim to leverage this asymmetry and train a diverse ensemble of models with very high recall, while sacrificing their precision. Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent. Thus, in aggregate the false positive errors will cancel out, yielding high performance for the ensemble. Our strategy is general and can be applied with any segmentation model. In three different applications (carotid artery segmentation in a neck CT angiography, myocardium segmentation in a cardiovascular MRI and multiple sclerosis lesion segmentation in a brain MRI), we show how the proposed approach can significantly boost the performance of a baseline segmentation method.