Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023. The data consisted of T2-weighted MRI scans acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic, and lumbar spine. A deep learning model, SCIseg (which is open source and accessible through the Spinal Cord Toolbox, version 6.2 and above), was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg, and contrast-agnostic, all part of the Spinal Cord Toolbox). The Wilcoxon signed rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results The study included 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 [74%] male patients). SCIseg achieved a mean Dice score of 0.92 ± 0.07 and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and automatically extracted clinically relevant lesion characteristics. Keywords: Spinal Cord, Trauma, Segmentation, MR Imaging, Supervised Learning, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license.

• Klíčová slova
• Convolutional Neural Network (CNN), MR Imaging, Segmentation, Spinal Cord, Supervised Learning, Trauma,
• MeSH
• deep learning * MeSH
• dospělí MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• poranění míchy * diagnostické zobrazování   patologie   MeSH
• retrospektivní studie MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0 . 82 ± 0 . 07 $$ 0.82\pm 0.07 $$ is reduced to: 0 . 66 ± 0 . 04 $$ 0.66\pm 0.04 $$ (Sign Correction); 0 . 34 ± 0 . 04 $$ 0.34\pm 0.04 $$ and 0 . 32 ± 0 . 04 $$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value < 0 . 05 $$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.

• Klíčová slova
• denoising, dual‐VENC, phase‐contrast MRI,
• MeSH
• algoritmy * MeSH
• aorta * diagnostické zobrazování   MeSH
• artefakty * MeSH
• fantomy radiodiagnostické MeSH
• interpretace obrazu počítačem metody   MeSH
• intrakraniální aneurysma diagnostické zobrazování   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek diagnostické zobrazování   MeSH
• počítačová simulace MeSH
• počítačové zpracování obrazu * metody   MeSH
• poměr signál - šum * MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Transcranial sonography (TCS) magnetic resonance (MR) fusion imaging and digital image analysis are useful tools for the evaluation of various brain pathologies. This study aimed to compare the echogenicity of predefined brain structures in Huntington's disease (HD) patients and healthy controls by TCS-MR fusion imaging using Virtual Navigator and digitized image analysis. MATERIALS AND METHODS: The echogenicity of the caudate nucleus (CN), substantia nigra (SN), lentiform nucleus (LN), insula, and brainstem raphe (BR) evaluated by TCS-MR fusion imaging using digitized image analysis was compared between 21 HD patients and 23 healthy controls. The cutoff values of echogenicity indices for the CN, LN, insula, and BR with optimal sensitivity and specificity were calculated using receiver operating characteristic analysis. RESULTS: The mean echogenicity indices for the CN (67.0±22.6 vs. 37.9±7.6, p<0.0001), LN (110.7±23.6 vs. 59.7±11.1, p<0.0001), and insula (121.7±39.1 vs. 70.8±23.0, p<0.0001) were significantly higher in HD patients than in healthy controls. In contrast, BR echogenicity (24.8±5.3 vs. 30.1±5.3, p<0.001) was lower in HD patients than in healthy controls. The area under the curve was 90.9%, 95.5%, 84.1%, and 81.8% for the CN, LN, insula, and BR, respectively. The sensitivity and specificity were 86% and 96%, respectively, for the CN and 90% and 100%, respectively, for the LN. CONCLUSION: Increased CN, LN, and insula echogenicity and decreased BR echogenicity are typical findings in HD patients. The high sensitivity and specificity of the CN and LN hyperechogenicity in TCS-MR fusion imaging make them promising diagnostic markers for HD.

UNLABELLED: ZIEL: Transkranielle Sonografie (TCS), Magnetresonanz-Fusionsbildgebung (MR-Fusionsbildgebung) und digitale Bildanalyse sind nützliche Werkzeuge für die Beurteilung verschiedener Hirnpathologien. Ziel dieser Studie war es, durch TCS-MR-Fusionsbildgebung mittels Virtual Navigator und digitaler Bildanalyse die Echogenität vordefinierter Hirnstrukturen bei Patienten mit der Huntington-Krankheit (HD) und gesunden Kontrollpersonen zu vergleichen. MATERIAL UND METHODEN: Die Echogenität des Nucleus caudatus (NC), der Substantia nigra (SN), des Nucleus lentiformis (NL), der Insula und der Hirnstamm-Raphe (BR), die mittels TCS-MR-Fusionsbildgebung und digitalisierter Bildanalyse bewertet wurde, wurde bei 21 Patienten mit Huntington-Krankheit und 23 gesunden Kontrollpersonen verglichen. Die Cutoff-Werte der Echogenitätsindizes für NC, NL, Insula und BR mit optimaler Sensitivität und Spezifität wurden mittels ROC-Analyse (Receiver Operating Characteristic Analyse) berechnet. ERGEBNISSE: Bei HD-Patienten im Vergleich zu gesunden Kontrollen waren die mittleren Echogenitätsindizes signifikant höher für den NC (67,0 ± 22,6 vs. 37,9 ± 7,6; p<0,0001), den NL (110,7 ± 23,6 vs. 59,7 ± 11,1; p<0,0001) und die Insula (121,7 ± 39,1 vs. 70,8 ± 23,0; p<0,0001). Im Gegensatz dazu war die BR-Echogenität bei HD-Patienten geringer als bei gesunden Kontrollpersonen (24,8 ± 5,3 vs. 30,1 ± 5,3; p<0,001). Die AUC betrug 90,9% für den NC, 95,5% für den NL, 84,1% für die Insula und 81,8% für die BR. Die Sensitivität und Spezifität betrugen 86% bzw. 96% für den NC und 90% bzw. 100% für den NL. SCHLUSSFOLGERUNG: Erhöhte Echogenität von NC, NL und Insula und verringerte BR-Echogenität sind typische Befunde bei HD-Patienten. Die hohe Sensitivität und Spezifität der Hyperechogenität von NC und NL in der TCS-MR-Fusionsbildgebung machen diese zu vielversprechenden diagnostischen Markern für HD.

• MeSH
• dospělí MeSH
• Huntingtonova nemoc * diagnostické zobrazování   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mozek * diagnostické zobrazování   MeSH
• mozková kůra diagnostické zobrazování   MeSH
• multimodální zobrazování metody   MeSH
• nucleus caudatus diagnostické zobrazování   MeSH
• počítačové zpracování obrazu metody   MeSH
• referenční hodnoty MeSH
• ROC křivka MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• ultrasonografie dopplerovská transkraniální metody   MeSH
• uživatelské rozhraní počítače MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Measurement of an- hippocampal area or volume is useful in clinical practice as a supportive aid for diagnosis of Alzheimer's disease. Since it is time-consuming and not simple, it is not being used very often. We present a simplified protocol for hippocampal atrophy evaluation based on a single optimal slice in Alzheimer's disease. METHODS: We defined a single optimal slice for hippocampal measurement on brain magnetic resonance imaging (MRI) at the plane where the amygdala disappears and only the hippocampus is present. We compared an absolute area and volume of the hippocampus on this optimal slice between 40 patients with Alzheimer disease and 40 age-, education- and gender-mateched elderly controls. Furthermore, we compared these results with those relative to the size of the brain or the skull: the area of the optimal slice normalized to the area of the brain at anterior commissure and the volume of the hippocampus normalized to the total intracranial volume. RESULTS: Hippocampal areas on the single optimal slice and hippocampal volumes on the left and right in the control group were significantly higher than those in the AD group. Normalized hippocampal areas and volumes on the left and right in the control group were significantly higher compared to the AD group. Absolute hippocampal areas and volumes did not significantly differ from corresponding normalized hippocampal areas as well as normalized hippocampal volumes using comparisons of areas under the receiver operating characteristic curves. CONCLUSION: The hippocampal area on the well-defined optimal slice of brain MRI can reliably substitute a complicated measurement of the hippocampal volume. Surprisingly, brain or skull normalization of these variables does not add any incremental differentiation between Alzheimer disease patients and controls or give better results.

• MeSH
• Alzheimerova nemoc diagnostické zobrazování   MeSH
• hipokampus diagnostické zobrazování   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• senioři MeSH
• zobrazování trojrozměrné metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Even though MRI visualization of white matter lesions is pivotal for the diagnosis and management of multiple sclerosis (MS), the issue of detecting diffuse brain tissue damage beyond the apparent T2-hyperintense lesions continues to spark considerable interest. Motivated by the notion that rotating frame MRI methods are sensitive to slow motional regimes critical for tissue characterization, here we utilized novel imaging protocols of rotating frame MRI on a clinical 3 Tesla platform, including adiabatic longitudinal, T1ρ, and transverse, T2ρ, relaxation methods, and Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank 4 (RAFF4), in 10 relapsing-remitting multiple sclerosis patients and 10 sex- and age-matched healthy controls. T1ρ, T2ρ and RAFF4 relaxograms extracted from the whole white matter exhibited a significant shift towards longer relaxation time constants in MS patients as compared to controls. T1ρ and RAFF4 detected alterations even when considering only regions of normally appearing white matter (NAWM), while other MRI metrics such as T1w/T2w ratio and diffusion tensor imaging measures failed to find group differences. In addition, RAFF4, T2ρ and, to a lesser extent, T1ρ showed differences in subcortical grey matter structures, mainly hippocampus, whereas no functional changes in this region were detected in resting-state functional MRI metrics. We conclude that rotating frame MRI techniques are exceptionally sensitive methods for the detection of subtle abnormalities not only in NAWM, but also in deep grey matter in MS, where they surpass even highly sensitive measures of functional changes, which are often suggested to precede detectable structural alterations. Such abnormalities are consistent with a wide spectrum of different, but interconnected pathological features of MS, including the loss of neuronal cells and their axons, decreased levels of myelin even in NAWM, and altered iron content.

• Klíčová slova
• Adiabatic pulses, DTI, Multiple sclerosis, Rotating frame relaxation MRI, T1w/T2w ratio,
• MeSH
• bílá hmota diagnostické zobrazování   patologie   MeSH
• dospělí MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mozek patologie   MeSH
• neurozobrazování metody   MeSH
• průřezové studie MeSH
• relabující-remitující roztroušená skleróza diagnostické zobrazování   patologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

• MeSH
• algoritmy * MeSH
• interpretace obrazu počítačem přístrojové vybavení   metody   MeSH
• lidé MeSH
• molekulární zobrazování metody   MeSH
• počítačová grafika přístrojové vybavení   trendy   MeSH
• počítačové zpracování signálu přístrojové vybavení   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• dopisy MeSH
• práce podpořená grantem MeSH

OBJECTIVE: T2 maps are more vendor independent than other MRI protocols. Multi-echo spin-echo signal decays to a non-zero offset due to imperfect refocusing pulses and Rician noise, causing T2 overestimation by the vendor's 2-parameter algorithm. The accuracy of the T2 estimate is improved, if the non-zero offset is estimated as a third parameter. Three-parameter Levenberg-Marquardt (LM) T2 estimation takes several minutes to calculate, and it is sensitive to initial values. We aimed for a 3-parameter fitting algorithm that was comparably accurate, yet substantially faster. METHODS: Our approach gains speed by converting the 3-parameter minimisation problem into an empirically unimodal univariate problem, which is quickly minimised using the golden section line search (GS). RESULTS: To enable comparison, we propose a novel noise-masking algorithm. For clinical data, the agreement between the GS and the LM fit is excellent, yet the GS algorithm is two orders of magnitude faster. For synthetic data, the accuracy of the GS algorithm is on par with that of the LM fit, and the GS algorithm is significantly faster. The GS algorithm requires no parametrisation or initialisation by the user. DISCUSSION: The new GS T2 mapping algorithm offers a fast and much more accurate off-the-shelf replacement for the inaccurate 2-parameter fit in the vendor's software.

• Klíčová slova
• Algorithms, Least-squares analysis, Software,
• MeSH
• algoritmy MeSH
• časové faktory MeSH
• fantomy radiodiagnostické MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• metoda nejmenších čtverců MeSH
• nádory prostaty diagnostické zobrazování   MeSH
• počítačové zpracování obrazu metody   MeSH
• poměr signál - šum MeSH
• pravděpodobnost MeSH
• regresní analýza MeSH
• reprodukovatelnost výsledků MeSH
• software MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Brain white matter fiber bundles in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have abnormalities not usually seen in unaffected subjects. Ideal algorithm of the localization-specific properties in white matter integrity might reveal the changes of tissue properties varying along each tract, while previous studies only detected the mean DTI parameters of each fiber. The aim of this study was to investigate whether these abnormalities of nerve fiber tracts are localized to specific regions of the tracts or spread throughout and to analyze which of the examined fiber tracts are involved in the early stages of Alzheimer's disease. In this study, we utilized VBA, TBSS as well as AFQ together to comprehensively investigate the white matter fiber impairment on 25 CE patients, 29 MCI patients and 34 normal control (NC) subjects. Two tract profiles, fractional anisotropy (FA) and mean diffusivity (MD), were extracted to evaluate the white matter integrity at 100 locations along each of 20 fiber tracts and then we validated the results with 27 CE patients, 21 MCI patients and 22 NC from the ADNI cohort. Also, we compare the AFQ with VBA and TBSS in our cohort. In comparison with NC, AD patients showed widespread FA reduction in 25% (5 /20) and MD increase in 65%(13/20) of the examined fiber tracts. The MCI patients showed a regional FA reduction in 5% (1/20) of the examined fiber tracts (right cingulum cingulate) and MD increase in 5%(1/20) of the examined fiber tracts (left arcuate fasciculus). Among these changed tracts, only the right cingulum cingulate showed widespread disruption of myelin or/and fiber axons in MCI and aggravated deterioration in AD, findings supported by FA/MD changes both by the mean and FA changes by point wise methods and TBSS. And the AFQ findings from ADNI cohort showed some similarity with our cohort, especially in the pointwise comparison of MD profiles between AD vs NC. Furthermore, the pattern of white matter abnormalities was different across neuronal fiber tracts; for example, the MCI and AD patients showed similar FA reduction in the middle part of the right cingulum cingulate, and the anterior part were not damaged. However, the left arcuate fasciculus showed MD elevation located at the temporal part of the fibers in the MCI patients and expanding to the temporal and middle part of the fibers in AD patients. So, the AFQ may be an alternative complementary method of VBA and TBSS, and may provide new insights into white matter degeneration in MCI and its association with AD.

• Klíčová slova
• Alzheimer's disease, Automated fiber quantification, Diffusion tensor imaging, Mild cognitive impairment, Pointwise comparison,
• MeSH
• Alzheimerova nemoc diagnostické zobrazování   patologie   MeSH
• bílá hmota diagnostické zobrazování   patologie   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nervová vlákna patologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• zobrazování difuzních tenzorů metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm ± 0.3 for CNN3, compared with 1.5 mm ± 1.0 for CNN1 (P < .05) and 1.3 mm ± 0.6 for CNN2 (P < .05). The LV function parameters derived from CNN3 showed a high correlation (r2 ≥ 0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion A deep learning-based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data. © RSNA, 2018 See also the editorial by Colletti in this issue.

• MeSH
• deep learning * MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• magnetická rezonance kinematografická metody   MeSH
• retrospektivní studie MeSH
• srdce - funkce komor fyziologie   MeSH
• srdeční komory diagnostické zobrazování   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

• Klíčová slova
• classification, computer-aided diagnosis, texture analysis, thyroid, ultrasound,
• MeSH
• algoritmy * MeSH
• diagnóza počítačová metody   MeSH
• diferenciální diagnóza MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• štítná žláza diagnostické zobrazování   patologie   MeSH
• support vector machine MeSH
• ultrasonografie metody   MeSH
• uzly štítné žlázy klasifikace   diagnostické zobrazování   patologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH