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Different factors like treating, handling, storage, exposure to contaminants and climatic change are responsible for the quality of fish. There is a difference in quality between fishes nurtured in fresh water and in polluted or pesticide affected water. Among different impurities, pesticide is one of the major threats to fish quality and human health. Identification and detection of pesticide contamination in fish is a challenging task and conventional methods need number of costly devices and expert manpower. This paper intends to provide a non-destructive computer-aided method for identification of quality differences between pesticide exposed fish and fresh water fish. In the proposed image processing method, the fish eye tissue was selected as the main region for extraction of different features in spatial domain. Statistical features were selected in spatial domain and then these features were analysed for discriminatory variation using strategic image processing techniques. These analysed features were correlated to pesticide treated and fresh water fish using machine learning methods. Different classifiers were used for automatic classification on the extracted features. The experimental results illustrate the efficiency of proposed method and the accuracy of identification is 96.87%. The computational time is less, making it practically well-suited for fish quality assessment in real time. [ABSTRACT FROM AUTHOR]

Background: To investigate the feasibility of using dual-energy CT (DECT) for tissue segmentation and kilovolt (kV) dose calculations in pre-clinical studies and assess potential dose calculation accuracy gain.Methods: Two phantoms and an ex-vivo mouse were scanned in a small animal irradiator with two distinct energies. Tissue segmentation was performed with the single-energy CT (SECT) and DECT methods. A number of different material maps was used. Dose calculations were performed to verify the impact of segmentations on the dose accuracy.Results: DECT showed better overall results in comparison to SECT. Higher number of DECT segmentation media resulted in smaller dose differences in comparison to the reference. Increasing the number of materials in the SECT method yielded more instability. Both modalities showed a limit to which adding more materials with similar characteristics ceased providing better segmentation results, and resulted in more noise in the material maps and the dose distributions. The effect was aggravated with a decrease in beam energy. For the ex-vivo specimen, the choice of only one high dense bone for the SECT method resulted in large volumes of tissue receiving high doses. For the DECT method, the choice of more than one kind of bone resulted in lower dose values for the different tissues occupying the same volume. For the organs at risk surrounded by bone, the doses were lower when using the SECT method in comparison to DECT, due to the high absorption of the bone. SECT material segmentation may lead to an underestimation of the dose to OAR in the proximity of bone.Conclusions: The DECT method enabled the selection of a higher number of materials thereby increasing the accuracy in dose calculations. In phantom studies, SECT performed best with three materials and DECT with seven for the phantom case. For irradiations in preclinical studies with kV photon energies, the use of DECT segmentation combined with the choice of a low-density bone is recommended. [ABSTRACT FROM AUTHOR]

Multiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions before segmentation with intensities similar to those of WM. Here, we propose a new method to refill WM lesions, where contrary to similar approaches, lesion voxel intensities are replaced by random values of a normal distribution generated from the mean WM signal intensity of each two-dimensional slice. We test the performance of our method by estimating the deviation in tissue volume between a set of 30 T1-w 1.5 T and 30 T1-w 3 T images of healthy subjects and the same images where: WM lesions have been previously registered and afterwards replaced their voxel intensities to those between gray matter (GM) and WM tissue. Tissue volume is computed independently using FAST and SPM8. When compared with the state-of-the-art methods, on 1.5 T data our method yields the lowest deviation in WM between original and filled images, independently of the segmentation method used. It also performs the lowest differences in GM when FAST is used and equals to the best method when SPM8 is employed. On 3 T data, our method also outperforms the state-of-the-art methods when FAST is used while performs similar to the best method when SPM8 is used. The proposed technique is currently available to researchers as a stand-alone program and as an SPM extension.

In the domain of human brain image analysis, identification of tumor region and segmentation of tissue structures tend to be a challenging task. Automated segmentation of Magnetic Resonance (MR) brain images would be of great assistance to radiologist, as they minimize the complication evolved due to human interface and offer quicker segmentation results. Automated algorithms offer minimal time duration and lesser manual intervention to a radiologist during clinical diagnosis. Moreover, larger volumes of patient data could be assessed with the aid of an automated algorithm and one such algorithm is proposed through this research to identify the tumor region bounded between normal tissue regions and edema portions. The proposed algorithm offers a better support to a radiologist in the process of diagnosing the pathologies, since; it utilizes both optimization and clustering techniques. Bacteria Foraging Optimization (BFO) and Modified Fuzzy K − Means algorithm (MFKM) are the optimization and clustering techniques used to render efficient MR brain image analysis. The proposed combinational algorithm is compared with Particle Swarm Optimization based Fuzzy C − Means algorithm (PSO based FCM), Modified Fuzzy K − Means (MFKM) and conventional FCM algorithm. The suggested methodology is evaluated using the comparison parameters such as sensitivity, Specificity, Jaccard T animoto C o − efficient Index (TC) and Dice Overlap Index (DOI), computational time and memory requirement. The algorithm proposed through this paper has produced appreciable values of sensitivity and specificity, which are 97.14% and 93.94%, respectively. Finally, it is found that the proposed BFO based MFKM algorithm offers better MR brain image segmentation and provides extensive support to radiologists. [ABSTRACT FROM AUTHOR]

The intrinsic ill-posed nature of the inverse problem in near infrared imaging makes the reconstruction of fine details of objects deeply embedded in turbid media challenging even for the large amounts of data provided by time-resolved cameras. In addition, most reconstruction algorithms for this type of measurements are only suitable for highly symmetric geometries and rely on a linear approximation to the diffusion equation since a numerical solution of the fully non-linear problem is computationally too expensive. In this paper, we will show that a problem of practical interest can be successfully addressed making efficient use of the totality of the information supplied by time-resolved cameras. We set aside the goal of achieving high spatial resolution for deep structures and focus on the reconstruction of complex arrangements of large regions. We show numerical results based on a combined approach of wavelength-normalized data and prior geometrical information, defining a fully parallelizable problem in arbitrary geometries for time-resolved measurements. Fast reconstructions are obtained using a diffusion approximation and Monte-Carlo simulations, parallelized in a multicore computer and a GPU respectively. [ABSTRACT FROM PUBLISHER]

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38-42 weeks gestational age), children and adolescents (4-17 years) and adults (35-71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course. [ABSTRACT FROM AUTHOR]

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19–39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI. • Our aim is automatic fetal brain tissue segmentation in MRI. • We generate noisy labels on a larger number of images using an atlas-based method. • We develop methods for training a deep learning model with these noisy labels. • On manually labeled images, our method achieves superior segmentation accuracy. [ABSTRACT FROM AUTHOR]

Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels. [ABSTRACT FROM AUTHOR]

The quantification of γ-aminobutyric acid (GABA) concentration using localised MRS suffers from partial volume effects related to differences in the intrinsic concentration of GABA in grey (GM) and white (WM) matter. These differences can be represented as a ratio between intrinsic GABA in GM and WM: rM. Individual differences in GM tissue volume can therefore potentially drive apparent concentration differences. Here, a quantification method that corrects for these effects is formulated and empirically validated. Quantification using tissue water as an internal concentration reference has been described previously. Partial volume effects attributed to rM can be accounted for by incorporating into this established method an additional multiplicative correction factor based on measured or literature values of rM weighted by the proportion of GM and WM within tissue-segmented MRS volumes. Simulations were performed to test the sensitivity of this correction using different assumptions of rM taken from previous studies. The tissue correction method was then validated by applying it to an independent dataset of in vivo GABA measurements using an empirically measured value of rM. It was shown that incorrect assumptions of rM can lead to overcorrection and inflation of GABA concentration measurements quantified in volumes composed predominantly of WM. For the independent dataset, GABA concentration was linearly related to GM tissue volume when only the water signal was corrected for partial volume effects. Performing a full correction that additionally accounts for partial volume effects ascribed to rM successfully removed this dependence. With an appropriate assumption of the ratio of intrinsic GABA concentration in GM and WM, GABA measurements can be corrected for partial volume effects, potentially leading to a reduction in between-participant variance, increased power in statistical tests and better discriminability of true effects. [ABSTRACT FROM AUTHOR]

Accurate segmentation of whole slide images is of great significance for the diagnosis of pancreatic cancer. However, developing an automatic model is challenging due to the complex content, limited samples, and high sample heterogeneity of pathological images. This paper presented a multi-tissue segmentation model for whole slide images of pancreatic cancer. We introduced an attention mechanism in building blocks, and designed a multi-task learning framework as well as proper auxiliary tasks to enhance model performance. The model was trained and tested with the pancreatic cancer pathological image dataset from Shanghai Changhai Hospital. And the data of TCGA, as an external independent validation cohort, was used for external validation. The F1 scores of the model exceeded 0.97 and 0.92 in the internal dataset and external dataset, respectively. Moreover, the generalization performance was also better than the baseline method significantly. These results demonstrate that the proposed model can accurately segment eight kinds of tissue regions in whole slide images of pancreatic cancer, which can provide reliable basis for clinical diagnosis.

Fatty infiltration in pancreas leading to steatosis is a major risk factor in pancreas transplantation. Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival, its endocrine function and pancreatic steatosis and can cause non-alcoholic fatty pancreas disease (NAFPD). The current automated tools (E.g. Adiposoft) available for fat analysis are suited for white fat tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. The currently, available pancreas segmentation tool focuses on endocrine islet segmentation based on cell nuclei detection for diagnosis of pancreatic cancer. In the current study, we present a fat quantifying tool, Fatquant, which identifies fat cells in heterogeneous H and E tissue sections with reference to diameter of fat cell. Using histological images from a public database, we observed an intersection over union of 0.797 to 0.962 and 0.675 to 0.937 for manual versus Fatquant analysis of pancreas and liver, respectively., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Activated brown adipose tissue (BAT) plays an important role in thermogenesis and whole body metabolism in mammals. Positron emission tomography (PET)-computed tomography (CT) imaging has identified depots of BAT in adult humans, igniting scientific interest. The purpose of this study is to characterize both active and inactive supraclavicular BAT in adults and compare the values to those of subcutaneous white adipose tissue (WAT). We obtained [18F]fluorodeoxyglucose ([18F]FDG) PET-CT and magnetic resonance imaging (MRI) scans of 25 healthy adults. Unlike [18F]FDG PET, which can detect only active BAT, MRI is capable of detecting both active and inactive BAT. The MRI-derived fat signal fraction (FSF) of active BAT was significantly lower than that of inactive BAT (means ± SD; 60.2 ± 7.6 vs. 62.4 ± 6.8%, respectively). This change in tissue morphology was also reflected as a significant increase in Hounsfield units (HU; -69.4 ± 11.5 vs. -74.5 ± 9.7 HU, respectively). Additionally, the CT HU, MRI FSF, and MRI R2* values are significantly different between BAT and WAT, regardless of the activation status of BAT. To the best of our knowledge, this is the first study to quantify PET-CT and MRI FSF measurements and utilize a semiautomated algorithm to identify inactive and active BAT in the same adult subjects. Our findings support the use of these metrics to characterize and distinguish between BAT and WAT and lay the foundation for future MRI analysis with the hope that some day MRI-based delineation of BAT can stand on its own. [ABSTRACT FROM AUTHOR]

Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1 mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7 T, from which quantitative T 1 maps were calculated. Using these T 1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T 1 -weighted data were automatically processed using FreeSurfer . The spatial alignment between T 2 ⁎ -weighted EPI data and the synthetic T 1 -weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data. [ABSTRACT FROM AUTHOR]

Ready-to-eat foods that does not receive a heat treatment before being consumed can be at risk of foodborne hazards and spoilage, so it would be of great interest to have a method for monitoring their safety. This work expands on and enhances previous successfully studies with hyperspectral imaging in the SW-NIR range. Specifically, a k-nearest-neighbours model was developed to classify the salmon tissue into white myocommata stripes (fat) and muscle (lean) tissue. Partial Least Squares models developed confirm that a spatial segmentation should be performed before a shelf life model can be calculated. Employing the fat spectra and only the 7 most correlated wavelengths, a support vector machine model was calculated to classify into days 0, 10, 20, 40 and 60 with 87.2% prediction accuracy. These results make the method developed very promising as a non-destructive method to analyse the shelf life of vacuum-packed chilled smoked salmon fillets. [ABSTRACT FROM AUTHOR]

The quality of a fish may be affected primarily by handling, processing and storage procedures from the catch to consumers. Retention time and storage temperature of post-harvested fish are key factors for sustaining the final quality of this product. This paper proposes an image processing method which is completely automatic, efficient and non-destructive for segmentation of tissues and prediction of freshness of the fish sample. The gill tissues of the fish sample are automatically segmented using a clustering based method and its features are strategically extracted in the wavelet transformation domain using Haar filter. First, second and third level decomposition in the wavelet domain is performed and the coefficients obtained at each level have been analyzed to predict the freshness of the fish sample. The experimental results indicate a monotonic variation pattern of the coefficients at the third level of decomposition and these coefficients gives an indication of the quality of the fish. This discriminatory variation in the image features with the duration of retention time provides a strategic framework for assessment of fish freshness. [ABSTRACT FROM AUTHOR]

The quality of fish is primarily dependent on its handling, processing, storage, exposure to contaminants and on climatic variability. Fishes nurtured at fresh and contaminated water exhibit marked differences in quality. Among different contaminants, pesticide is reported as a predominant non-specific menace to fish health and quality. Detection and identification of pesticide residues in fish is a challenging task and requires costly sophisticated instruments. This paper proposes an image processing based non-destructive technique for identifying quality differences between pesticide treated and untreated (control) fish. To evaluate the quality variability, rohu (Labeorohita ) fishes were treated with mild dose of cypermethrin for seven days and bio-accumulation status was recorded through GC–MS at post-harvested condition followed by imaging at two days interval. Gill tissue was selected as focal tissue for image processing which was segmented and different features were extracted in wavelet domain using Haar filter. Features were selected up to the third level of decomposition in wavelet domain and analysed for discriminatory features. The discriminatory variations in the different features of images were related to the difference between pesticide treated and untreated fish using strategic image processing techniques. Supervised classification was performed on the extracted features using support vector machine (SVM) classifier. The experimental results indicate that the proposed method is efficient for identification of pesticide treated and untreated fish from the features of the images. The accuracy of identification is high and the computation time is faster enough to make this method efficient as a real time application. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effects of MR parameters on tissue segmentation and determine the optimal MR sequence for attenuation correction in PET/MR hybrid imaging. Eight healthy volunteers were examined using a PET/MR hybrid scanner with six three-dimensional turbo-field-echo sequences for attenuation correction by modifying the echo time, k-space trajectory in the phase-encoding direction, and image contrast. MR images for attenuation correction were obtained from six MR sequences in each session; each volunteer underwent four sessions. Two radiologists assessed the attenuation correction maps generated from the MR images with respect to segmentation errors and ghost artifacts on a five-point scale, and the scores were decided by consensus. Segmentation accuracy and reproducibility were compared. Multiple regression analysis was performed to determine the effects of each MR parameter. The two three-dimensional turbo-field-echo sequences with an in-phase echo time and radial k-space sampling showed the highest total scores for segmentation accuracy, with a high reproducibility. In multiple regression analysis, the score with the shortest echo time (− 3.44, P < 0.0001) and Cartesian sampling in the anterior/posterior phase-encoding direction (− 2.72, P = 0.002) was significantly lower than that with in-phase echo time and Cartesian sampling in the right/left phase-encoding direction. Radial k-space sampling provided a significantly higher score (+ 5.08, P < 0.0001) compared with Cartesian sampling. Furthermore, radial sampling improved intrasubject variations in the segmentation score (− 8.28%, P = 0.002). Image contrast had no significant effect on the total score or reproducibility. These results suggest that three-dimensional turbo-field-echo MR sequences with an in-phase echo time and radial k-space sampling provide improved MR-based attenuation correction maps. [ABSTRACT FROM AUTHOR]

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Background: Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breast. Methods: Planning CT data of 23 patients were evaluated in this study. Texture features were derived from the radial glandular fraction (RGF), which described the distribution of FT within three breast regions (posterior, middle, and anterior). Using visual assessment, experts grouped patients according to FT spatial distribution: sparse or nonsparse. Differences in the features between the two groups were investigated using the Wilcoxon rank test. Classification performance of the features was evaluated for a range of support vector machine (SVM) classifiers. Results: Experts found eight patients and 15 patients had sparse and non-sparse spatial distribution of FT, respectively. A large proportion of features (>9 of 13) from the individual breast regions had significant differences (p <0.05) between the sparse and non-sparse group. The features from middle region had most significant differences and gave the highest classification accuracy for all the SVM kernels investigated. Overall, the features from middle breast region achieved highest accuracy (91 %) with the linear SVM kernel. Conclusion: This study found that features based on radial glandular fraction provide a means for discriminating between fibroglandular tissue distributions and could achieve a classification accuracy of 91 %. [ABSTRACT FROM AUTHOR]

Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository. [ABSTRACT FROM AUTHOR]

Purpose: In order to more precisely differentiate cerebral structures in neuroimaging studies, a novel technique for enhancing the tissue contrast based on a combination of T1-weighted (T1w) and T2-weighted (T2w) MRI images was developed. Methods: The combined image (CI) was calculated as CI=(T1w-sT2w)/(T1w+sT2w), where sT2w is the scaled T2- weighted image. The scaling factor was calculated to adjust the gray-matter (GM) voxel intensities in the T2w image so that their median value equaled that of the GM voxel intensities in the T1w image. The image intensity homogeneity within a tissue and the discriminability between tissues in the CI versus the separate T1w and T2w images were evaluated using the segmentation by the FMRIB Software Library (FSL) and FreeSurfer (Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Boston, MA) software. Results: The combined image significantly improved homogeneity in the white matter (WM) and GM compared to the T1w images alone. The discriminability between WM and GM also improved significantly by applying the CI approach. Significant enhancements to the homogeneity and discriminability also were achieved in most subcortical nuclei tested, with the exception of the amygdala and the thalamus. Conclusion: The tissue discriminability enhancement offered by the CI potentially enables more accurate neuromorphometric analyses of brain structures. [ABSTRACT FROM AUTHOR]

Purpose: To develop a tissue correction for GABA-edited magnetic resonance spectroscopy (MRS) that appropriately addresses differences in voxel gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) fractions.Materials and Methods: Simulations compared the performance of tissue correction approaches. Corrections were then applied to in vivo data from 16 healthy volunteers, acquired at 3T. GM, WM, and CSF fractions were determined from T1 -weighted images. Corrections for CSF content, GM/WM GABA content, and water relaxation of the three compartments are combined into a single, fully corrected measurement.Results: Simulations show that CSF correction increases the dependence of GABA measurements on GM/WM fraction, by an amount equal to the fraction of CSF. Furthermore, GM correction substantially (and nonlinearly) increases the dependence of GABA measurements on GM/WM fraction, for example, by a factor of over four when the voxel GM tissue fraction is 50%. At this tissue fraction, GABA is overestimated by a factor of 1.5. For the in vivo data, correcting for voxel composition increased measured GABA values (P < 0.001 for all regions), but did not reduce intersubject variance (P > 0.5 for all regions). Corrected GABA values differ significantly based on the segmentation procedure used (P < 0.0001) and tissue parameter assumptions made (P < 0.0001).Conclusion: We introduce a comprehensive tissue correction factor that adjusts GABA measurements to correct for different voxel compositions of GM, WM, and CSF. [ABSTRACT FROM AUTHOR]