Automated assessment of regional muscle volume and hypertrophy using MRI.
Ontology highlight
ABSTRACT:
SUBMITTER: Mandic M
PROVIDER: S-EPMC7010694 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
Publications
Automated assessment of regional muscle volume and hypertrophy using MRI.
Mandić Mirko M Rullman Eric E Widholm Per P Lilja Mats M Dahlqvist Leinhard Olof O Gustafsson Thomas T Lundberg Tommy R TR
Scientific reports 20200210 1
This study aimed to validate a fully automatic method to quantify knee-extensor muscle volume and exercise-induced hypertrophy. By using a magnetic resonance imaging-based fat-water separated two-point Dixon sequence, the agreement between automated and manual segmentation of a specific ~15-cm region (partial volume) of the quadriceps muscle was assessed. We then explored the sensitivity of the automated technique to detect changes in both complete and partial quadriceps volume in response to 8 ...[more]
Similar Datasets
Project description:BackgroundAlthough structural magnetic resonance imaging (MRI) studies have repeatedly demonstrated regional brain structural abnormalities in patients with schizophrenia, relatively few MRI-based studies have attempted to distinguish between patients with first-episode schizophrenia and healthy controls.MethodThree-dimensional MR images were acquired from 52 (29 males, 23 females) first-episode schizophrenia patients and 40 (22 males, 18 females) healthy subjects. Multiple brain measures (regional brain volume and cortical thickness) were calculated by a fully automated procedure and were used for group comparison and classification by linear discriminant function analysis.ResultsSchizophrenia patients showed gray matter volume reductions and cortical thinning in various brain regions predominantly in prefrontal and temporal cortices compared with controls. The classifiers obtained from 66 subjects of the first group successfully assigned 26 subjects of the second group with accuracy above 80%.ConclusionOur results showed that combinations of automated brain measures successfully differentiated first-episode schizophrenia patients from healthy controls. Such neuroimaging approaches may provide objective biological information adjunct to clinical diagnosis of early schizophrenia.
| S-EPMC3119676 | biostudies-literature
Project description:PurposeTo retrospectively assess the repeatability of physiological F-18 labeled fluorodeoxyglucose (FDG) uptake in the skin on positron emission tomography/magnetic resonance imaging (PET/MRI) and explore its regional distribution and relationship with sex and age.MethodsOut of 562 examinations with normal FDG distribution on whole-body PET/MRI, 74 repeated examinations were evaluated to assess the repeatability and regional distribution of physiological skin uptake. Furthermore, 224 examinations were evaluated to compare differences in the uptake due to sex and age. Skin segmentation on PET was performed as body-surface contouring on an MR-based attenuation correction map using an off-line reconstruction software. Bland-Altman plots were created for the repeatability assessment. Kruskal-Wallis test was performed to compare the maximum standardized uptake value (SUVmax) with regional distribution, age, and sex.ResultsThe limits of agreement for the difference in SUVmean and SUVmax of the skin were less than 30%. The highest SUVmax was observed in the face (3.09±1.04), followed by the scalp (2.07±0.53). The SUVmax in the face of boys aged 0-9 years and 10-20 years (1.33±0.64 and 2.05±1.00, respectively) and girls aged 0-9 years (0.98±0.38) was significantly lower than that of men aged ≥20 years and girls aged ≥10 years (p<0.001). In women, the SUVmax of the face (2.31±0.71) of ≥70-year-olds was significantly lower than that of 30-39-year-olds (3.83±0.82) (p<0.05).ConclusionPET/MRI enabled the quantitative analysis of skin FDG uptake with repeatability. The degree of physiological FDG uptake in the skin was the highest in the face and varied between sexes. Although attention to differences in body habitus between age groups is needed, skin FDG uptake also depended on age.
| S-EPMC7997016 | biostudies-literature
Project description:Background:Paraspinal musculature forms one of the largest muscle compartments of the human body, but evidence for regional variation of its composition and dependency on gender or body mass index (BMI) is scarce. Methods:This study applied six-echo chemical shift encoding-based water-fat magnetic resonance imaging (MRI) at 3 Tesla in 76 subjects (24 males and 52 females, age: 40.0±13.7 years, BMI: 25.4±5.6 kg/m2) to evaluate the proton density fat fraction (PDFF) of psoas muscles and erector spinae muscles, with the latter being divided into three segments in relation to levels of spine anatomy (L3-L5, T12-L2, and T9-T11). Results:For the psoas muscles and the erector spinae muscles (L3-L5), gender differences in PDFF values were observed (PDFF psoas muscles: males: 5.1%±3.4% vs. females: 6.0%±2.2%, P=0.006; PDFF erector spinae muscles L3-L5: males: 10.7%±7.6% vs. females: 18.2%±6.8%, P<0.001). Furthermore, the PDFF of the erector spinae muscles (L3-L5) showed higher PDFF values when compared to the other segments (PDFF erector spinae muscles L3-L5 vs. T12-L2: P<0.001; PDFF erector spinae muscles L3-L5 vs. T9-T11: P<0.001) and showed to be independent of BMI, which was not the case for the other segments (T12-L2 or T9-T11) or the psoas muscles. When considering age and BMI as control variables, correlations of PDFF between segments of the erector spinae muscles remained significant for both genders. Conclusions:This study explored regional variation of paraspinal muscle composition and dependency on gender and BMI, thus offering new insights into muscle physiology. The PDFF of the erector spinae muscles (L3-L5) was independent of BMI, suggesting that this level may be suited for representative paraspinal muscle segmentation and PDFF extraction as a biomarker for muscle alterations in the future.
| S-EPMC7063279 | biostudies-literature
Project description:PurposeTo assess the performance of an automated myocardial T2 and extracellular volume (ECV) quantification method using transfer learning of a fully convolutional neural network (CNN) pretrained to segment the myocardium on T1 mapping images.Materials and methodsA single CNN previously trained and tested using 11 550 manually segmented native T1-weighted images was used to segment the myocardium for automated myocardial T2 and ECV quantification. Reference measurements from 1525 manually processed T2 maps and 1525 ECV maps (from 305 patients) were used to evaluate the performance of the pretrained network. Correlation coefficient (R) and Bland-Altman analysis were used to assess agreement between automated and reference values on per-patient, per-slice, and per-segment analyses. Furthermore, transfer learning effectiveness in the CNN was evaluated by comparing its performance to four CNNs trained using manually segmented T2-weighted and postcontrast T1-weighted images and initialized using random-weights or weights of the pretrained CNN.ResultsT2 and ECV measurements using the pretrained CNN strongly correlated with reference values in per-patient (T2: R = 0.88, 95% confidence interval [CI]: 0.85, 0.91; ECV: R = 0.91, 95% CI: 0.89, 0.93), per-slice (T2: R = 0.83, 95% CI: 0.81, 0.85; ECV: R = 0.84, 95% CI: 0.82, 0.86), and per-segment (T2: R = 0.75, 95% CI: 0.74, 0.77; ECV: R = 0.76, 95% CI: 0.75, 0.77) analyses. In Bland-Altman analysis, the automatic and reference values were in good agreement in per-patient (T2: 0.3 msec ± 2.9; ECV: -0.3% ± 1.7), per-slice (T2: 0.1 msec ± 4.6; ECV: -0.3% ± 2.5), and per-segment (T2: 0.0 msec ± 6.5; ECV: -0.4% ± 3.5) analyses. The performance of the pretrained network was comparable to networks refined or trained from scratch using additional manually segmented images.ConclusionTransfer learning extends the utility of pretrained CNN-based automated native T1 mapping analysis to T2 and ECV mapping without compromising performance. Supplemental material is available for this article. © RSNA, 2020.
| S-EPMC6996604 | biostudies-literature
Project description:Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.
| S-EPMC8656605 | biostudies-literature
Project description:Vessel wall imaging techniques have been introduced to assess the burden of peripheral arterial disease (PAD) in terms of vessel wall thickness, area or volume. Recent advances in a 3D black-blood MRI sequence known as the 3D motion-sensitized driven equilibrium (MSDE) prepared rapid gradient echo sequence (3D MERGE) have allowed the acquisition of vessel wall images with up to 50 cm coverage, facilitating noninvasive and detailed assessment of PAD. This work introduces an algorithm that combines 2D slice-based segmentation and 3D user editing to allow for efficient plaque burden analysis of the femoral artery images acquired using 3D MERGE.The 2D slice-based segmentation approach is based on propagating segmentation results of contiguous 2D slices. The 3D image volume was then reformatted using the curved planar reformation (CPR) technique. User editing of the segmented contours was performed on the CPR views taken at different angles. The method was evaluated on six femoral artery images. Vessel wall thickness and area obtained before and after editing on the CPR views were assessed by comparison with manual segmentation. Difference between semiautomatically and manually segmented contours were compared with the difference of the corresponding measurements between two repeated manual segmentations.The root-mean-square (RMS) errors of the mean wall thickness (t(mean)) and the wall area (WA) of the edited contours were 0.35 mm and 7.1 mm(2), respectively, which are close to the RMS difference between two repeated manual segmentations (RMSE: 0.33 mm in t(mean), 6.6 mm(2) in WA). The time required for the entire semiautomated segmentation process was only 1%-2% of the time required for manual segmentation.The difference between the boundaries generated by the proposed algorithm and the manually segmented boundary is close to the difference between repeated manual segmentations. The proposed method provides accurate plaque burden measurements, while considerably reducing the analysis time compared to manual review.
| S-EPMC3189974 | biostudies-other
Project description:AIMS:The aim of this study was to report the metrological qualities of techniques currently used to quantify skeletal muscle volume and 3D shape in healthy and pathological muscles. METHODS:A systematic review was conducted (Prospero CRD42018082708). PubMed, Web of Science, Cochrane and Scopus databases were searched using relevant keywords and inclusion/exclusion criteria. The quality of the articles was evaluated using a customized scale. RESULTS:Thirty articles were included, 6 of which included pathological muscles. Most evaluated lower limb muscles. Partially or completely automatic and manual techniques were assessed in 10 and 24 articles, respectively. Manual slice-by-slice segmentation reliability was good-to-excellent (n = 8 articles) and validity against dissection was moderate to good(n = 1). Manual slice-by-slice segmentation was used as a gold-standard method in the other articles. Reduction of the number of manually segmented slices (n = 6) provided good to excellent validity if a sufficient number of appropriate slices was chosen. Segmentation on one slice (n = 11) increased volume errors. The Deformation of a Parametric Specific Object (DPSO) method (n = 5) decreased the number of manually-segmented slices required for any chosen level of error. Other automatic techniques combined with different statistical shape or atlas/images-based methods (n = 4) had good validity. Some particularities were highlighted for specific muscles. Except for manual slice by slice segmentation, reliability has rarely been reported. CONCLUSIONS:The results of this systematic review help the choice of appropriate segmentation techniques, according to the purpose of the measurement. In healthy populations, techniques that greatly simplified the process of manual segmentation yielded greater errors in volume and shape estimations. Reduction of the number of manually segmented slices was possible with appropriately chosen segmented slices or with DPSO. Other automatic techniques showed promise, but data were insufficient for their validation. More data on the metrological quality of techniques used in the cases of muscle pathology are required.
| S-EPMC6264864 | biostudies-literature
Project description:IntroductionPooling of multicenter brain imaging data is a trend in studies on ageing related brain diseases. This poses challenges to MR-based brain segmentation. The performance across different field strengths of three widely used automated methods for brain volume measurements was assessed in the present study.MethodsTen subjects (mean age: 64 years) were scanned on 1.5T and 3T MRI on the same day. We determined robustness across field strength (i.e., whether measured volumes between 3T and 1.5T scans in the same subjects were similar) for SPM12, Freesurfer 5.3.0 and FSL 5.0.7. As a frame of reference, 3T MRI scans from 20 additional subjects (mean age: 71 years) were segmented manually to determine accuracy of the methods (i.e., whether measured volumes corresponded with expert-defined volumes).ResultsTotal brain volume (TBV) measurements were robust across field strength for Freesurfer and FSL (mean absolute difference as % of mean volume ? 1%), but less so for SPM (4%). Gray matter (GM) and white matter (WM) volume measurements were robust for Freesurfer (1%; 2%) and FSL (2%; 3%) but less so for SPM (5%; 4%). For intracranial volume (ICV), SPM was more robust (2%) than FSL (3%) and Freesurfer (9%). TBV measurements were accurate for SPM and FSL, but less so for Freesurfer. For GM volume, SPM was accurate, but accuracy was lower for Freesurfer and FSL. For WM volume, Freesurfer was accurate, but SPM and FSL were less accurate. For ICV, FSL was accurate, while SPM and Freesurfer were less accurate.ConclusionBrain volumes and ICV could be measured quite robustly in scans acquired at different field strengths, but performance of the methods varied depending on the assessed compartment (e.g., TBV or ICV). Selection of an appropriate method in multicenter brain imaging studies therefore depends on the compartment of interest.
| S-EPMC5087903 | biostudies-literature
Project description:PurposeManagement of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard.MethodsUsing high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy.ResultsWe found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, [Formula: see text]), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation.ConclusionWe suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.
| S-EPMC7419453 | biostudies-literature
Project description:BackgroundBlood flow velocity and pulsatility of small cerebral perforating arteries can be measured using 7T quantitative 2D phase contrast (PC) MRI. However, ghosting artifacts arising from subject movement and pulsating large arteries cause false positives when applying a previously published perforator detection method.PurposeTo develop a robust, automated method to exclude perforators located in ghosting artifacts.Study typeRetrospective.SubjectsFifteen patients with vascular cognitive impairment or carotid occlusive disease and 10 healthy controls.Field strength/sequence7T/cardiac-gated 2D PC MRI.AssessmentPerforators were automatically excluded from ghosting regions, which were defined as bands in the phase-encoding direction of large arteries. As reference, perforators were manually excluded by two raters (T.A., J.J.M.Z.), based on perforator location with respect to visible ghosting artifacts. The performance of both censoring methods was assessed for the number of (Nincluded ), mean velocity (Vmean ), and pulsatility index (PI) of the included perforators.Statistical testsFor within-method comparisons, inter- and intrarater reliability were assessed for the manual method, and test-retest reliability was assessed for both methods from repeated 2D PC scans (without repositioning). Intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CIs) were determined for Nincluded , Vmean , and PI for all within-method comparisons. The ICC to compare between the two methods was determined with the use of both (test-retest) scans using a multilevel nonlinear mixed model.ResultsThe automated censoring method showed a moderate to good ICC (95% CI) vs. manual censoring for Nincluded (0.73 [0.58-0.87]) and Vmean (0.90 [0.84-0.96]), and a moderate ICC for PI (0.57 [0.37-0.76]). The test-retest reliability of the manual censoring method was considerably lower than the interrater and intrarater reliability, indicating that scanner noise dominates the uncertainty of the analysis.Data conclusionThe proposed automated censoring method can reliably exclude small perforators affected by ghosting artifacts.Level of evidence3.Technical efficacy stage1.
| S-EPMC7754489 | biostudies-literature