This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Introduction and hypothesis Pelvic organ prolapse quantification by means of upright magnetic resonance imaging (MRI) is a promising research field. This study determines the angle for the pelvic inclination correction system (PICS) for upright patient position, which is hypothesized to deviate from the supine PICS angle. The necessity of different PICS angles for various patient positions will also be discussed. Methods Magnetic resonance scans of 113 women, acquired in an upright patient position, were used to determine the upright PICS angle, defined as the angle between the sacrococcygeal–inferior pubic point (SCIPP) line and the horizontal line. The difference and correlation between the upright and supine PICS angles were calculated using the paired Student’s t-test and the Pearson’s correlation coefficient (r) respectively. The effect of the difference between the upright and supine PICS angle on the measured pelvic organ extent was calculated using goniometry. Results The mean (interquartile range) PICS angles were 29° (26–35°) for the upright and 33° (30–37°) for the supine patient position. They were significantly different (p<0.001) and very strongly correlated (r = 0.914, p<0.001). The 4° difference between the average upright and supine PICS angle results in an average underestimation of the measured cervix height of approximately 0.5 cm for patients scanned in upright position. Conclusions The PICS angle for the upright patient position is 29°. The use of a dedicated PICS angle for different patient positions allows for more accurate pelvic organ extent analysis in patients with prolapse.
Magnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy.Limitations related to metabolite fitting of low SNR data, signal variations due to partial volume effects, acquisition and extra-cranial lipid artefacts, along with clinically relevant aspects such as scan-time constraints, are among the factors that hinder the widespread implementation of in vivo MRSI. The aim of this work was to address these factors and to develop an acquisition, reconstruction and postprocessing pipeline to derive lipid suppressed metabolite values based on Free Induction Decay (FID-MRSI) measurements made using a 7 tesla MR scanner. Anatomical images were used to perform highresolution (1mm 3 ) partial-volume correction to account for grey matter, white matter and cerebralspinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the MNI standard atlas facilitated the creation of high-resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Reported metabolite values include glutamate, choline, (phospo)creatine, myo-inositol, glutathione, N-acetyl aspartyl glutamate(and glutamine) and N-acetyl aspartate. MNI-registered average metabolite maps facilitate group-based analysis; thus offering the possibility to mitigate uncertainty in variable MRSI.
Background: Patients with psychotic disorders often show prominent cognitive impairment. Glutamate seems to play a prominent role, but its role in deep gray matter (DGM) regions is unclear. Aims: To evaluate glutamate levels within deep gray matter structures in patients with a psychotic disorder in relation to cognitive functioning, using advanced spectroscopic acquisition, reconstruction, and post-processing techniques. Methods: A 7-Tesla magnetic resonance imaging scanner combined with a lipid suppression coil and subject-specific water suppression pulses was used to acquire high-resolution magnetic resonance spectroscopic imaging data. Tissue fraction correction and registration to a standard brain were performed for group comparison in specifically delineated DGM regions. The brief assessment of cognition in schizophrenia was used to evaluate cognitive status. Results: Average glutamate levels across DGM structures (i.e. caudate, pallidum, putamen, and thalamus) in mostly medicated patients with a psychotic disorder ( n = 16, age = 33, 4 females) were lower compared to healthy controls ( n = 23, age = 24, 7 females; p = 0.005, d = 1.06). Stratified analyses showed lower glutamate levels in the caudate ( p = 0.046, d = 0.76) and putamen p = 0.013, d = 0.94). These findings were largely explained by age differences between groups. DGM glutamate levels were positively correlated with psychomotor speed ( r(30) = 0.49, p = 0.028), but not with other cognitive domains. Conclusions: We find reduced glutamate levels across DGM structures including the caudate and putamen in patients with a psychotic disorder that are linked to psychomotor speed. Despite limitations concerning age differences, these results underscore the potential role of detailed in vivo glutamate assessments to understand cognitive deficits in psychotic disorders.
Patients with psychotic disorders often show prominent cognitive impairment. Glutamate seems to play a prominent role, but knowledge on its role in deep gray matter regions is limited and previous studies have yielded heterogeneous results. The aim was to evaluate glutamate levels within deep gray matter structures in patients with a psychotic disorder in relation to cognitive functioning, using advanced spectroscopic acquisition, reconstruction and post-processing techniques. A 7 tesla MRI scanner combined with a unique lipid suppression coil and subject specific water signal suppression pulses were used to acquire high-resolution magnetic resonance spectroscopic imaging data. Anatomical scans were used to perform tissue fraction correction and registration to a standard brain for group comparison in specifically delineated brain regions. The brief assessment of cognition in schizophrenia was used to evaluate cognitive status. Average glutamate levels across deep gray matter structures (i.e. caudate, pallidum, putamen, and thalamus) in patients with a psychotic disorder (n=16, 4 females) were lower compared to healthy controls (n=23, 7 females). Stratified analyses showed lower glutamate levels in the caudate and putamen but not in the pallidum or thalamus. Average glutamate levels across deep gray matter structures were positively correlated with cognition, particularly to psychomotor speed. We find reduced glutamate levels across deep brain structures such as the caudate and putamen in patients with a psychotic disorder that are linked to psychomotor speed. Our results underscore the potential role of detailed in vivo glutamate assessments to understand cognitive deficits in patients with psychotic disorders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.