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We compared the feasibility of quantitative analysis methods using bone SPECT/CT with those using planar bone scans to assess active sacroiliitis.We retrospectively reviewed whole-body bone scans and pelvic bone SPECT/CTs of 8 patients who had clinically confirmed sacroiliitis and enrolled 24 patients without sacroiliitis as references. The volume of interest of each sacroiliac joint, including both the ilium and sacrum, was drawn. Active arthritis zone (AAZ) was defined as the zone of voxels with higher SUV than sacral mean SUV within the VOI of SI joint. Then, the following SPECT/CT quantitative parameters, SUVmax (maximum SUV), SUV50% (mean SUV in highest 50% of SUV), and SUV-AAZ, and the ratio of those values to sacral mean SUV (SUVmax/S, SUV50%/S, SUV-AAZ/S) were calculated. For the planar bone scan, the mean count ratio of SI joint/sacrum (SI/S) was conventionally measured.Most of the SPECT/CT parameters of the sacroiliitis group were significantly higher than the normal group, whereas SI/S of the planar bone scan was not significantly different between the two groups. In receiver operating characteristic curve analysis, SUV-AAZ/S showed the highest AUC of 0.992, followed by SUV50%/S and SUVmax/S. All ratio parameters of the SPECT/CT showed higher AUC values than the SUV parameters of SI joint or SI/S of the planar scan.The quantitative analyses of bone SPECT/CT showed better performance in assessing active sacroiliitis than the planar bone scan. SPECT/CT parameters using the ratio of the SI joint to sacrum showed more favorable results than SUV parameters such as SUVmax, SUV50%, and SUV-AAZ.

Current prognostic systems for intrahepatic cholangiocarcinoma (IHCC) rely on surgical pathology data and are not applicable to a preoperative setting. We aimed to develop and validate preoperative models to predict postsurgical outcomes in mass-forming IHCC patients based on clinical, radiologic, and radiomics features. This multicenter retrospective cohort study included patients who underwent curative-intent resection for mass-forming IHCC. In the development cohort (single institution data), three preoperative multivariable Cox models for predicting recurrence-free survival (RFS) were constructed, including the clinical-radiologic, radiomics, and clinical-radiologic-radiomics (CRR) models based on clinical and CT findings, CT-radiomics features, and a combination of both, respectively. Model performance was evaluated in the test cohort (data from five institutions) using Harrell’s C-index and compared with postoperative prognostic systems. A total of 345 patients (233, development cohort; 112, test cohort) were evaluated. The clinical-radiologic model included five independent CT predictors (infiltrative contour, multiplicity, periductal infiltration, extrahepatic organ invasion, and suspicious metastatic lymph node) and showed similar performance in predicting RFS to the radiomics model (C-index, 0.65 vs. 0.68; p = 0.43 in the test cohort). The CRR model showed significantly improved performance (C-index, 0.71; p = 0.01) than the clinical-radiologic model and demonstrated similar performance to the postoperative prognostic systems in predicting RFS (C-index, 0.71–0.73 vs. 0.70–0.73; p ≥ 0.40) and overall survival (C-index, 0.68–0.71 vs. 0.64–0.74; p ≥ 0.27) in the test cohort. A model integrating clinical, CT, and radiomics information may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming IHCC. • The radiomics analysis had incremental value in predicting recurrence-free survival of patients with intrahepatic mass-forming cholangiocarcinoma. • The clinical-radiologic-radiomics model demonstrated similar performance to the postoperatively available prognostic systems (including 8th AJCC system) in predicting recurrence-free survival and overall survival. • The clinical-radiologic-radiomics model may be useful for the preoperative assessment of postsurgical outcomes in patients with mass-forming intrahepatic cholangiocarcinoma.

Background To facilitate translational drug development for liver fibrosis, preclinical trials need to be run in parallel with clinical research. Liver function estimation by gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI) is being established in clinical research, but still rarely used in preclinical trials. We aimed to evaluate feasibility of DCE-MRI indices as translatable biomarkers in a liver fibrosis animal model. Methods Liver fibrosis was induced in Sprague-Dawley rats by thioacetamide (200 mg, 150 mg, and saline for the high-dose, low-dose, and control groups, respectively). Subsequently, DCE-MRI was performed to measure: relative liver enhancement at 3-min (RLE-3), RLE-15, initial area-under-the-curve until 3-min (iAUC-3), iAUC-15, and maximum-enhancement (Emax). The correlation coefficients between these MRI indices and the histologic collagen area, indocyanine green retention at 15-min (ICG-R15), and shear wave elastography (SWE) were calculated. Diagnostic performance to diagnose liver fibrosis was also evaluated by receiver-operating-characteristic (ROC) analysis. Results Animal model was successful in that the collagen area of the liver was the largest in the high-dose group, followed by the low-dose group and control group. The correlation between the DCE-MRI indices and collagen area was high for iAUC-15, Emax, iAUC-3, and RLE-3 but moderate for RLE-15 (r, − 0.81, − 0.81, − 0.78, − 0.80, and − 0.51, respectively). The DCE-MRI indices showed moderate correlation with ICG-R15: the highest for iAUC-15, followed by iAUC-3, RLE-3, Emax, and RLE-15 (r, − 0.65, − 0.63, − 0.62, − 0.58, and − 0.56, respectively). The correlation coefficients between DCE-MRI indices and SWE ranged from − 0.59 to − 0.28. The diagnostic accuracy of RLE-3, iAUC-3, iAUC-15, and Emax was 100% (AUROC 1.000), whereas those of RLE-15 and SWE were relatively low (AUROC 0.777, 0.848, respectively). Conclusion Among the gadoxetate-enhanced DCE-MRI indices, iAUC-15 and iAUC-3 might be bidirectional translatable biomarkers between preclinical and clinical research for evaluating histopathologic liver fibrosis and physiologic liver functions in a non-invasive manner.

To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort.A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p0.01) and the test (AUC, 0.95; p=0.01) cohorts.Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity.The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVIn the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LVOur DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

Purpose To develop and validate a radiomics-based model for staging liver fibrosis by using gadoxetic acid-enhanced hepatobiliary phase MRI. Materials and Methods In this retrospective study, 436 patients (mean age, 51 years; age range, 18-86 years; 319 men [mean age, 51 years; age range, 18-86 years]; 117 women [mean age, 50 years; age range, 18-79 years]) with pathologic analysis-proven liver fibrosis who underwent gadoxetic acid-enhanced MRI from June 2015 to December 2016 were randomized in a three-to-one ratio into development (n = 329) and test (n = 107) cohorts, respectively. In the development cohort, a model was developed to calculate radiomics fibrosis index (RFI) by using logistic regression with elastic net regularization to differentiate stage F3-F4 from stage F0-F2. Optimal RFI cutoffs to diagnose clinically significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4), and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. In the test cohort, the diagnostic performance of RFI was compared with that of normalized liver enhancement, aspartate transaminase-to-platelet ratio index (APRI), and fibrosis-4 index by using the Obuchowski index. Results In the test cohort, RFI (Obuchowski index, 0.86) significantly outperformed normalized liver enhancement (Obuchowski index, 0.77; P.03), APRI (Obuchowski index, 0.60; P.001), and fibrosis-4 index (Obuchowski index, 0.62; P.001) for staging liver fibrosis. By using the cutoffs, RFI had sensitivities and specificities as follows: 81% (95% confidence interval: 71%, 89%) and 78% (95% confidence interval: 63%, 89%) for diagnosing stage F2-F4, respectively; 79% (95% confidence interval: 67%, 88%) and 82% (95% confidence interval: 69%, 91%), respectively, for diagnosing stage F3-F4; and 92% (95% confidence interval: 79%, 98%) and 75% (95% confidence interval: 62%, 83%), respectively, for diagnosing stage F4. Conclusion Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis. © RSNA, 2018 Online supplemental material is available for this article.

To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set. The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort. The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. • A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Oxidative stress is a key mediator of neuronal death in acute brain injuries, such as epilepsy, trauma, and stroke. Although it is accompanied by diverse cellular changes, increases in levels of intracellular zinc ion (Zn2+) and calcium ion (Ca2+) may play a critical causative role in oxidative neuronal death. However, the mechanistic link between Zn2+ and Ca2+ dyshomeostasis in neurons during oxidative stress is not well-understood. Here, we show that the exposure of cortical neurons to H2O2 led to a zinc-triggered calcium influx, which resulted in neuronal death. The cyclin-dependent kinase inhibitor, NU6027, inhibited H2O2-induced Ca2+ increases and subsequent cell death in cortical neurons, without affecting the early increase in Zn2+. Therefore, we attempted to identify the zinc-regulated Ca2+ pathway that was inhibited by NU6027. The expression profile in cortical neurons identified transient receptor potential cation channel 5 (TRPC5) as a candidate that is known to involve in the generation of epileptiform burst firing and epileptic neuronal death (Phelan KD et al. 2012a; Phelan KD et al. 2013b). NU6027 inhibited basal and zinc-augmented TRPC5 currents in TRPC5-overexpressing HEK293 cells. Consistently, cortical neurons from TRPC5 knockout mice were highly resistant to H2O2-induced death. Moreover, NU6027 is neuroprotective in kainate-treated epileptic rats. Our results demonstrate that TRPC5 is a novel therapeutic target against oxidative neuronal injury in prolonged seizures and that NU6027 is a potent inhibitor of TRPC5.

Purpose To evaluate the robustness of MR transverse relaxation times of trabecular bone from spin-echo and gradient-echo acquisitions at multiple spatial resolutions of 7 T. Methods The effects of MRI resolutions to T2 and T2* of trabecular bone were numerically evaluated by Monte Carlo simulations. T2 , T2*, and trabecular structural indices from multislice multi-echo and UTE acquisitions were measured in defatted human distal femoral condyles on a 7 T scanner. Reference structural indices were extracted from high-resolution microcomputed tomography images. For bovine knee trabecular samples with intact bone marrow, T2 and T2* were measured by degrading spatial resolutions on a 7 T system. Results In the defatted trabecular experiment, both T2 and T2* values showed strong ( |r| > 0.80) correlations with trabecular spacing and number, at a high spatial resolution of 125 µm3 . The correlations for MR image-segmentation-derived structural indices were significantly degraded ( |r| 0.85). In the bovine trabecular experiments with intact marrow, low-resolution (approximately 1 mm3 , 2 minutes) T2 values did not shorten ( |r| > 0.95 with respect to approximately 0.4 mm3 , 11 minutes) and maintained consistent correlations ( |r| > 0.70) with respect to trabecular spacing (turbo spin echo, 22.5 minutes). Conclusion T2 measurements of trabeculae at 7 T are robust with degrading spatial resolution and may be preferable in assessing trabecular spacing index with reduced scan time, when high-resolution 3D micro-MRI is difficult to obtain.

Background/AimTo analyse ultrawide-field fluorescein angiography (UWF-FA) images of diabetic retinopathy using a novel software that automatically calculates microaneurysm (MA) and non-perfusion area.MethodsTwo hundred UWF-FA images of treatment-naïve diabetic retinopathy (38 proliferative diabetic retinopathy and 162 non-proliferative diabetic retinopathy) from 120 patients (mean age 54.22; 80 male) were analysed using novel software to determine the number of MAs, area of capillary non-perfusion (ischaemic index) and number of neovascularisations. Each result was compared according to its retinal regions.ResultsFor the total retina, the mean number of MAs was 292.02 (±175.57) and the ischaemic index was 59.42% (±14.78%). Most MAs were located in the mid-peripheral retina (80.54%); however, the density of MAs was highest in the posterior pole (pConclusionThe automated software allowed prompt and quantitative analysis of UWF-FA images of DMR. MAs were most frequent in the nasal and mid-peripheral retina, with their density being highest in the posterior pole and nasal retina. Ischaemic index increased with distance from the posterior pole, showing strong correlation with central foveal thickness in all retinal areas except the posterior pole.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising tool for evaluating tumor vascularity, as it can provide vasculature-derived, functional, and quantitative parameters. To implement DCE-MRI parameters as biomarkers for monitoring the effect of antiangiogenic or vascular-disrupting treatment, two crucial elements of surrogate endpoint, ie, validation and qualification, should be satisfied. Although early studies have shown the accuracy and reliability of DCE-MRI parameters for evaluating treatment-driven vascular alterations, there have been an increasing number of studies demonstrating the limitations of DCE-MRI parameters as surrogate endpoints. Therefore, in order to improve the application of DCE-MRI parameters in drug development, it is necessary to establish a standardized evaluation method and to determine the correct therapeutics-oriented meaning of individual DCE-MRI parameter. In this regard, this article describes the biophysical background and data acquisition/analysis techniques of DCE-MRI while focusing on the validation and qualification issues. Specifically, the causes of disagreement and confusion encountered in the preclinical and clinical trials using DCE-MRI are presented in detail. Finally, considering these limitations, we present potential strategies to optimize implementation of DCE-MRI. J. Magn. Reson. Imaging 2016;44:251-264.

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

To assess whether increases in amide proton transfer (APT)–weighted signal reflect the effects of tissue recovery from acidosis using transient rat middle cerebral artery occlusion (MCAO) models, compared to permanent occlusion models. Twenty-four rats with MCAO (17 transient and seven permanent occlusions) were prepared. APT-weighted signal (APTw), apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and MR spectroscopy were evaluated at three stages in each group (occlusion, reperfusion/1 h post-occlusion, and 3 h post-reperfusion/4 h post-occlusion). Deficit areas showing 30% reduction to the contralateral side were measured. Temporal changes were compared with repeated measures of analysis of variance. Relationship between APTw and lactate concentration was calculated. Both APTw and CBF values increased and APTw deficit area reduced at reperfusion (largest p = .002) in transient occlusion models, but this was not demonstrated in permanent occlusion. No significant temporal change was demonstrated with ADC at reperfusion. APTw deficit area was between ADC and CBF deficit areas in transient occlusion model. APTw correlated with lactate concentration at occlusion (r = − 0.49, p = .04) and reperfusion (r = − 0.32, p = .02). APTw values increased after reperfusion and correlated with lactate content, which suggests that APT-weighted MRI could become a useful imaging technique to reflect tissue acidosis and its reversal. • APT-weighted signal increases in the tissue reperfusion, while remains stable in the permanent occlusion. • APTw deficit area was between ADC and CBF deficit areas in transient occlusion model, possibly demonstrating metabolic penumbra. • APTw correlated with lactate concentration during ischemia and reperfusion, indicating tissue acidosis.

Coordinated expression of guidance molecules and their signal transduction are critical for correct brain wiring. Previous studies have shown that phospholipase C gamma1 (PLCγ1), a signal transducer of receptor tyrosine kinases, plays a specific role in the regulation of neuronal cell morphology and motility in vitro . However, several questions remain regarding the extracellular stimulus that triggers PLCγ1 signaling and the exact role PLCγ1 plays in nervous system development. Here, we demonstrate that PLCγ1 mediates axonal guidance through a netrin‐1/deleted in colorectal cancer (DCC) complex. Netrin‐1/DCC activates PLCγ1 through Src kinase to induce actin cytoskeleton rearrangement. Neuronal progenitor‐specific knockout of Plcg1 in mice causes axon guidance defects in the dorsal part of the mesencephalon during embryogenesis. Adult Plcg1 ‐deficient mice exhibit structural alterations in the corpus callosum, substantia innominata, and olfactory tubercle. These results suggest that PLCγ1 plays an important role in the correct development of white matter structure by mediating netrin‐1/DCC signaling.

Oxidative stress is a key mediator of neuronal death in acute brain injuries, such as epilepsy, trauma, and stroke. Although it is accompanied by diverse cellular changes, increases in levels of intracellular zinc ion (Zn

OBJECTIVE To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. MATERIALS AND METHODS Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. RESULTS Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). CONCLUSION Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.

Objective To simulate the B1-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods B1-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R1, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (Ktrans, ve, and vp). Results B1-inhomogeneity resulted in −23–5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: −16.7–61.8% and −16.7–61.8% for the pre-contrast R1, −1.0–0.3% and −5.2–1.3% for the contrast-enhancement ratio, and −14.2–58.1% and −14.1–57.8% for the Gd-concentration, respectively. These resulted in −43.1–48.4% error for Ktrans, −32.3–48.6% error for the ve, and −43.2–48.6% error for vp. The pre-contrast R1 was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a −10% FA error led to a 23.6% deviation in the pre-contrast R1, −0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a −10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were −23.7% for Ktrans, −23.7% for ve, and −23.7% for vp. Conclusion Even a small degree of B1-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R1 calculations to FA deviations is a significant cause of the miscalculation.

Background Lymph node (LN) status is an important parameter for determining the treatment strategy and for predicting the prognosis for patients with uterine cervical cancer. Computer-aided diagnosis (CAD) can be feasible for differentiating metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer Purpose To determine the usefulness of CAD that comprehensively evaluates MR images and clinical findings for detecting LN metastasis in uterine cervical cancer. Material and Methods In 680 LNs from 143 patients who underwent radical hysterectomy for uterine cervical cancer, the CAD system using the Bayesian classifier estimated the probability of metastasis based on MR findings and clinical findings. We compared the diagnostic accuracy for detecting metastatic LNs in the CAD and MR findings. Results Metastasis was diagnosed in 70 (12%) LNs from 34 (24%) patients. The area under ROC curves of CAD (0.924) was greater than those of the mean ADC (0.854), minimum ADC (0.849), maximum ADC (0.827), short-axis diameter (0.856) and long-axis diameter (0.753) ( P < 0.05). The specificity and accuracy of the CAD (86%, 86%) were greater than those of the mean ADC (77%, 77%), maximum ADC (77%, 77%), minimum ADC (68%, 70%), and short-axis diameter (65%, 67%) ( P < 0.05). Conclusion CAD system can improve the diagnostic performance of MR for detecting metastatic LNs in uterine cervical cancer.

The purpose of this study was to compare quantitative and visual assessments of regional heterogeneity of emphysema and to investigate the influence of regional heterogeneity on pulmonary function in smoking-related emphysema.We developed an automatic computerized algorithm to quantitatively assess heterogeneity in the upper-lower, anterior-posterior, and central-peripheral directions. The emphysema index was plotted with a linear function (emphysema index slopes: slope of emphysema index in upper-lower direction, slope of emphysema index in anterior-posterior direction, and slope of emphysema index in central-peripheral direction) for consecutive 1-pixel-thick slices using volumetric CT data of 59 patients (58 men and one woman; mean age, 65.7 years). Emphysema index was defined as the percentage area of lung with attenuation values below -950 HU. Visual assessment was performed using a 5-point scoring system. Quantitative and visual assessments were compared. Multiple linear regression was performed to evaluate the influence of emphysema index and emphysema index slopes on the pulmonary function test.Quantitative and visual assessments were significantly correlated in both upper-lower (r(2) = 0.40 and r(2) = 0.67 for observers 1 and 2, respectively) and central-peripheral (r(2) = 0.51 and r(2) = 0.47, respectively) directions. Multiple linear regression revealed that emphysema index, slope of emphysema index in upper-lower direction, and slope of emphysema index in anterior-posterior direction were independent determinants of forced expiratory volume in 1 second (FEV(1)) (r(2) = 0.30; p0.001). Emphysema index and slope of emphysema index in upper-lower direction were independent determinants of the ratio of FEV(1) to forced vital capacity (FEV(1)/FVC) (r(2) = 0.32; p0.001). In addition to higher emphysema index, lower and posterior lung dominance was associated with a decrease in FEV(1) and FEV(1)/FVC.Computerized, quantitative assessment using the emphysema index slope is comparable to visual assessment in the evaluation of regional heterogeneity of emphysema. In addition to the emphysema index, regional heterogeneity of smoking-related emphysema contributes to impairment of pulmonary function.

Purpose To determine the reference site for relative apparent diffusion coefficient (rADC) and to evaluate the benefit of rADC for detecting metastatic lymph nodes in uterine cervical cancer. Materials and Methods Two observers independently measured ADCs in the spleen, liver, renal cortex, lumbar spine, lumbar spinal cord, and gluteus maximus on diffusion-weighted images (b value, 0 and 1000 mm/sec2) in 50 patients. The reference site for rADC was determined using the intra- and interobserver coefficient of variation (CV) of ADC in these organs. rADC was calculated by ADClesion/ADCreference site. The benefit of rADC over ADC was validated by comparing the area under the receiver operating curve for identifying metastatic lymph nodes in uterine cervical cancer in 130 patients. Results The renal cortex was determined to be the reference site for rADC, as its CVs (intraobserver, 5%–7%; interobserver, 5%) were less than those of the other organs (P < 0.05). The ADC and rADC of metastatic lymph nodes (n = 29, ADC, 0.7483 × 10−3 mm2/sec; rADC, 0.3832) were less than those of nonmetastatic lymph nodes (n = 229, ADC, 0.9960 × 10−3 mm2/sec; rADC, 0.5383) (P < 0.05). The area under the receiver operating characteristics curve for differentiating metastatic from nonmetastatic lymph nodes was greater for rADC (0.914; 95% confidence interval [CI], 0.872–0.945) than for ADC (0.872; 95% CI, 0.825–0.910) (P = 0.007). Conclusion The renal cortex is an appropriate reference site for rADC and rADC may improve the accuracy for diagnosing metastatic lymph nodes in uterine cervical cancer. J. Magn. Reson. Imaging 2009;29:383–390. © 2009 Wiley-Liss, Inc.

Purpose To evaluate whether measurement of apparent diffusion coefficient (ADC) and pure diffusion coefficient (D) can help to characterize solid pancreatic masses. Materials and Methods Diffusion-weighted MR imaging was performed in both a patient group (n = 71; pancreatic cancer [n = 47], mass-forming pancreatitis [n = 13], solid pseudopapillary neoplasm [n = 6], and neuroendocrine tumor [n = 5]) and a normal control group (n = 11) by applying three b-factors of 0, 500, and 1000 sec/mm2. ADC500, ADC1000, D (ADC using b = 500 and 1000 sec/mm2), and perfusion fraction (f, 1- exp [-500 sec/mm2 × (ADC500 – D)]) of normal pancreas, pancreatic cancer, and mass-forming pancreatitis were compared using the Kruskal–Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance and optimal cutoff value of these parameters in differentiating pancreatic cancer from mass-forming pancreatitis. Results Normal pancreas had significantly higher mean ADC500, ADC1000, and f than either pancreatic cancer (P < 0.001, < 0.001, and 0.004, respectively) or mass-forming pancreatitis (P < 0.001, < 0.001, and 0.002, respectively). ADC500, ADC1000, and D of mass-forming pancreatitis were significantly lower than those of pancreatic cancer (P = 0.002, 0.004, and 0.014, respectively). Sensitivities and specificities in the diagnosis of pancreatic cancer were 72.3% and 76.9% for ADC500, 87.2% and 69.2% for ADC1000, 87.2% and 61.5% for D, and 42.6% and 92.3% for f, respectively. Conclusion Measurement of ADC and D may be helpful in differentiating pancreatic cancers from mass-forming pancreatitis. J. Magn. Reson. Imaging 2008;28:928–936. © 2008 Wiley-Liss, Inc.

Purpose To investigate the feasibility of diffusion-weighted imaging (DWI) in the differentiation of metastatic from nonmetastatic lymph nodes. Materials and Methods In 125 patients who underwent lymph node dissection for uterine cervical cancer, DWI was performed at b value of 0 and 1000 s/mm2. By referring to the surgical maps of the pelvic lymph nodes, the apparent diffusion coefficient (ADC) was compared in the metastatic and nonmetastatic lymph nodes, and receiver-operating-characteristics analysis was performed to evaluate the diagnostic performance of the ADC in differentiating metastatic from nonmetastatic lymph nodes. Results The ADC were significantly lower in the metastatic lymph nodes (0.7651 × 10−3 mm2/s ± 0.1137) than in the nonmetastatic lymph nodes (1.0021 × 10−3 mm2/s ± 0.1859; P < 0.001). The area-under-the-curve of ADC for differentiating metastatic from nonmetastatic lymph nodes, was 0.902. The sensitivity and specificity of ADC for differentiating metastatic from nonmetastatic lymph nodes, were 87% for the ADC and 80%, respectively. Conclusion DWI is feasible for differentiating metastatic from nonmetastatic lymph nodes in patients with uterine cervical cancer. J. Magn. Reson. Imaging 2008;28:714–719. © 2008 Wiley-Liss, Inc.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising tool for evaluating tumor vascularity, as it can provide vasculature-derived, functional, and quantitative parameters. To implement DCE-MRI parameters as biomarkers for monitoring the effect of antiangiogenic or vascular-disrupting treatment, two crucial elements of surrogate endpoint, ie, validation and qualification, should be satisfied. Although early studies have shown the accuracy and reliability of DCE-MRI parameters for evaluating treatment-driven vascular alterations, there have been an increasing number of studies demonstrating the limitations of DCE-MRI parameters as surrogate endpoints. Therefore, in order to improve the application of DCE-MRI parameters in drug development, it is necessary to establish a standardized evaluation method and to determine the correct therapeutics-oriented meaning of individual DCE-MRI parameter. In this regard, this article describes the biophysical background and data acquisition/analysis techniques of DCE-MRI while focusing on the validation and qualification issues. Specifically, the causes of disagreement and confusion encountered in the preclinical and clinical trials using DCE-MRI are presented in detail. Finally, considering these limitations, we present potential strategies to optimize implementation of DCE-MRI. J. Magn. Reson. Imaging 2016;44:251-264.

Summary In this paper, local quasi-likelihood regression is considered for stationary random fields of dependent variables. In the case of independent data, local polynomial quasi-likelihood regression is known to have several appealing features such as minimax efficiency, design adaptivity and good boundary behaviour. These properties are shown to carry over to the case of random fields. The asymptotic normality of the regression estimator is established and explicit formulae for its asymptotic bias and variance are derived for strongly mixing stationary random fields. The extension to multi-dimensional covariates is also provided in full generality. Moreover, evaluation of the finite sample performance is made through a simulation study.

Summary Regression functions may have a change or discontinuity point in the ν th derivative function at an unknown location. This paper considers a method of estimating the location and the jump size of the change point based on the local polynomial fits with one-sided kernels when the design points are random. It shows that the estimator of the location of the change point achieves the rate n −1/(2ν+1) when ν is even. On the other hand, when ν is odd, it converges faster than the rate n −1/(2ν+1) due to a property of one-sided kernels. Computer simulation demonstrates the improved performance of the method over the existing ones.

Local likelihood methods hold considerable promise in density estimation. They offer unmatched flexibility and adaptivity as the resulting density estimators inherit both of the best properties of nonparametric approaches and parametric inference. However, the adoption of local likelihood methods with dependent observations, in particular with random fields, is inhibited by lack of knowledge about their properties in the case. In the present paper we detail asymptotic properties of the local likelihood density estimators for stationary random fields in the usual smoothing context of the bandwidth, h, tending to zero as the sample size tends to infinity. The asymptotic analysis is substantially more complex than in ordinary kernel density estimation on random fields.

Park et al. (Econometric Theory 16 (2000) 855) and Gijbels et al. (J. Amer. Statist. Assoc. 94 (1999) 220) derived the limit distributions of the two most popular nonparametric estimators, FDH and DEA, of a boundary in a setting where the density or intensity of the data has a sharp or fault-type boundary. In this paper, we extend their results to the general case where the density or intensity may decrease to zero or infinity at a speed of power α −1 ( α >0) of the distance from the boundary.

Objective To simulate the B1-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods B1-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R1, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (Ktrans, ve, and vp). Results B1-inhomogeneity resulted in -23-5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: -16.7-61.8% and -16.7-61.8% for the pre-contrast R1, -1.0-0.3% and -5.2-1.3% for the contrast-enhancement ratio, and -14.2-58.1% and -14.1-57.8% for the Gd-concentration, respectively. These resulted in -43.1-48.4% error for Ktrans, -32.3-48.6% error for the ve, and -43.2-48.6% error for vp. The pre-contrast R1 was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a -10% FA error led to a 23.6% deviation in the pre-contrast R1, -0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a -10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were -23.7% for Ktrans, -23.7% for ve, and -23.7% for vp. Conclusion Even a small degree of B1-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R1 calculations to FA deviations is a significant cause of the miscalculation.

Contrast-enhancing magnetic resonance mechanism, employing either positive or negative signal changes, has contrast-specific signal characteristics. Although highly sensitive, negative contrast typically decreases the resolution and spatial specificity of MRI, whereas positive contrast lacks a high contrast-to-noise ratio but offers high spatial accuracy. To overcome these individual limitations, dual-contrast acquisitions were performed using iron oxide nanoparticles and a pair of MRI acquisitions. Specifically, vascular signals in MR angiography were positively enhanced using ultrashort echo (UTE) acquisition, which provided highly resolved vessel structures with increased vessel/tissue contrast. In addition, fast low angle shot (FLASH) acquisition yielded strong negative vessel contrast, resulting in the higher number of discernible vessel branches than those obtained from the UTE method. Taken together, the high sensitivity of the negative contrast delineated ambiguous vessel regions, whereas the positive contrast effectively eliminated the false negative contrast areas (e.g., airways and bones), demonstrating the benefits of the dual-contrast method. From the Clinical Editor In this study, the MRI properties of iron oxide nanoparticles were studied in an animal model. These contrast agents are typically considered negative contrast materials, leading to signal loss on T2* weighted images, but they also have known T1 effects as well, which is lower than that of standard positive contrast agents (like gadolinium or manganese) but is still detectable. This dual property was utilized in this study, demonstrating high sensitivity of the negative contrast in delineating ambiguous vessel regions, whereas the positive contrast eliminated false negative contrast areas (areas giving rise to susceptibility effects).

The purpose of this article is to quantitatively assess the rate of resolution of clot burden detected on pulmonary CT angiography (CTA) in patients with acute pulmonary embolism (PE).We evaluated 111 consecutive patients (55 men and 56 women) in a retrospective cohort who were diagnosed with PE by pulmonary CTA and had at least one follow-up pulmonary CTA within 1 year. Two radiologists in consensus measured the volume of each clot using a semiautomated quantification program. Semiquantitative measures of clot burden were also computed. The resolution rates of the total clot volume, as well as clot volumes of the central (main and lobar) and peripheral vessels (segmental and subsegmental), were analyzed.The mean (± SD) clot volume per study was 3403.3 ± 6505.6 mm(3) at baseline and 531.6 ± 2383.5 mm(3) at the follow-up pulmonary CTA. Overall, 85 patients (77% ) showed complete resolution at the follow-up pulmonary CTA. Complete resolution was seen in 17 of 30 patients (56.7%) at a follow-up interval of 1-14 days, in 24 of 31 patients (77.4%) at 29-90 days, and in 32 of 34 patients (94.1%) after 90 days. The total clot volume measurements summed for all patients decreased by 78% (central clot, 69.4%; peripheral clot, 86.0%) at 14 days, by 96.6% (central clot, 93.4%; peripheral clot, 100%) at 90 days, and by 97.7% (central clot, 95.9%; peripheral clot, 100%) after 90 days.Clot burden resolved completely in 77% of patients during the follow-up period. Our analysis showed that clots resolved faster in the peripheral arteries than in the central pulmonary arteries.

To evaluate the performance of a semi-automated method for the segmentation of individual renal cysts from magnetic resonance (MR) images in patients with autosomal dominant polycystic kidney disease (ADPKD).This semi-automated method was based on a morphologic watershed technique with shape-detection level set for segmentation of renal cysts from MR images. T2-weighted MR image sets of 40 kidneys were selected from 20 patients with mild to moderate renal cyst burden (kidney volume1500 ml) in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP). The performance of the semi-automated method was assessed in terms of two reference metrics in each kidney: the total number of cysts measured by manual counting and the total volume of cysts measured with a region-based thresholding method. The proposed and reference measurements were compared using intraclass correlation coefficient (ICC) and Bland-Altman analysis.Individual renal cysts were successfully segmented with the semi-automated method in all 20 cases. The total number of cysts in each kidney measured with the two methods correlated well (ICC, 0.99), with a very small relative bias (0.3% increase with the semi-automated method; limits of agreement, 15.2% reduction to 17.2% increase). The total volume of cysts measured using both methods also correlated well (ICC, 1.00), with a small relative bias of10% (9.0% decrease in the semi-automated method; limits of agreement, 17.1% increase to 43.3% decrease).This semi-automated method to segment individual renal cysts in ADPKD kidneys provides a quantitative indicator of severity in early and moderate stages of the disease.

The purpose of this article is to assess the value of computer-aided diagnosis (CAD) for prostate cancer detection on dynamic contrast-enhanced MRI (DCE-MRI).DCE-MRI examinations of 42 patients with prostate cancer were used to generate perfusion parameters, including baseline and peak signal intensities, initial slope, maximum slope within the initial 50 seconds after the contrast injection (slope(50)), wash-in rate, washout rate, time to peak, percentage of relative enhancement, percentage enhancement ratio, time of arrival, efflux rate constant from the extravascular extracellular space to the blood plasma (k(ep)), first-order rate constant for eliminating gadopentetate dimeglumine from the blood plasma (k(el)), and constant depending on the properties of the tissue and represented by the size of the extravascular extracellular space (A(H)). CAD for cancer detection was established by comprehensive evaluation of parameters using a support vector machine. The diagnostic accuracy of single perfusion parameters was estimated using receiver operating characteristic analysis, which determined threshold and parametric maps for cancer detection. The diagnostic performance of CAD for cancer detection was compared with those of T2-weighted imaging (T2WI) and single perfusion parameter maps, using histologic results as the reference standard.The accuracy, sensitivity, and specificity of CAD were 83%, 77%, and 77%, respectively, in the entire prostate; 77%, 91%, and 64%, respectively, in the transitional zone; and 89%, 89%, and 89%, respectively, in the peripheral zone. Values for k(ep), k(el), initial slope, slope(50), wash-in rate, washout rate, and time to peak showed greater area under the curve values (0.803-0.888) than did the other parameters (0.545-0.665) (p0.01) and were compared with values for CAD. In the entire prostate, accuracy was greater for CAD than for all perfusion parameters or T2WI (63-77%); sensitivity was greater for CAD than for T2WI, initial slope, wash-in rate, slope(50), and washout rate (38-77%); and specificity was greater for CAD than for T2WI, k(ep), k(el), and time to peak (59-68%) (p0.05).CAD can improve the diagnostic performance of DCE-MRI in prostate cancer detection, which may vary according to zonal anatomy.

Sodium magnetic resonance (MR) imaging is a promising technique for detecting changes of proteoglycan (PG) content in cartilage associated with knee osteoarthritis. Despite its potential clinical benefit, sodium MR imaging in vivo is challenging because of intrinsically low sodium concentration and low MR signal sensitivity. Some of the challenges in sodium MR imaging may be eliminated by the use of a high-sensitivity radiofrequency (RF) coil, specifically, a dual-tuned (DT) proton/sodium RF coil which facilitates the co-registration of sodium and proton MR images and the evaluation of both physiochemical and structural properties of knee cartilage. Nevertheless, implementation of a DT proton/sodium RF coil is technically difficult because of the coupling effect between the coil elements (particularly at high field) and the required compact design with improved coil sensitivity. In this study, we applied a multitransceiver RF coil design to develop a DT proton/sodium coil for knee cartilage imaging at 3 T. With the new design, the size of the coil was minimized, and a high signal-to-noise ratio (SNR) was achieved. DT coil exhibited high levels of reflection S11 (∼-21 dB) and transmission coefficient S12 (∼-19 dB) for both the proton and sodium coils. High SNR (range 27-38) and contrast-to-noise ratio (CNR) (range 15-21) were achieved in sodium MR imaging of knee cartilage in vivo at 3-mm(3) isotropic resolution. This DT coil performance was comparable to that measured using a sodium-only birdcage coil (SNR of 28 and CNR of 20). Clinical evaluation of the DT coil on four normal subjects demonstrated a consistent acquisition of high-resolution proton images and measurement of relative sodium concentrations of knee cartilages without repositioning of the subjects during the same MR scanning session.

Purpose: To evaluate the effect of diffusion-weighted imaging (DWI) methods, apparent diffusion coefficient (ADC) calculation methods, and selection of b-values on the ADCs and the measurement reproducibility of malignant hepatic tumors. Materials and Methods: Nineteen patients with pathologically confirmed malignant hepatic tumors underwent breath-hold DWI (b-values = 0, 50, 500 s/mm2) and respiratory-triggered DWI (0, 50, 300, 500, 1000 s/mm2) twice on a 1.5 T magnetic resonance imaging (MRI) scanner. ADCs were calculated using a two b-value and/or a multiple b-value method. The reproducibility of the ADC measurements was evaluated from the intraclass correlation coefficients (ICCs) and the 95% Bland–Altman limit-of-agreement (LOA). Results: The ADCs were different according to the DWI methods (P = 0.040–0.282), ADC calculation methods (P = 0.003–0.825), and the choice of b-values (P < 0.001). The ADC tended to be more reproducible with use of breath-hold DWI (ICC: 0.898–0.933; LOA, 18.8%–24.0%) than respiratory-triggered DWI (ICC: 0.684–0.928; LOA, 15.0%–31.9%) (P = 0.008–0.122). For respiratory-triggered DWI, the multiple b-value method using five b-values had better reproducibility than the two b-value method for measurement of ADC (P = 0.009–0.221). Conclusion: The DWI method, ADC calculation method, and selection of b-values potentially influence the ADCs and the reproducibility of malignant hepatic tumors. ADCs calculated from breath-hold DWI are more reproducible than from respiratory-triggered DWI. A multiple b-value method may improve the reproducibility of respiratory-triggered DWI. J. Magn. Reson. Imaging 2012;36:1131–1138. © 2012 Wiley Periodicals, Inc.

The purpose of our study was to assess the accuracy and reproducibility of clot burden quantification with pulmonary CT angiography (CTA).A semiautomated program was developed for segmentation and volumetric quantification of pulmonary embolus with pulmonary CTA. The accuracy of this measurement method was assessed using two pulmonary embolus phantoms. Reproducibility of the measurement method was assessed using clinical pulmonary CTA in 30 patients (16 women, 14 men; mean age, 62 years) with pulmonary embolism (PE). Two observers segmented and measured the volume of blood clot from pulmonary CTA images twice at two separate sessions. Accuracy was evaluated by the relative volume measurement error. Intra- and interobserver reliability were evaluated using intraclass correlation coefficient (ICC); agreement between measurements within and between the two observers was assessed using Bland-Altman analysis.Mean relative measurement error from the two phantoms was less than 1% for both observers. A total of 60 emboli were measured from the 30 patients. The intraobserver ICC was 0.990 for observer 1 and 0.999 for observer 2; interobserver ICC was 0.994 for session 1 and 0.989 for session 2. ICC for all four clot measurements was 0.988. Mean volume measurement difference for intraobserver agreement was 0.9% for observer 1 and 0.3% for observer 2, and interobserver agreement was -5.1% for session 1 and -5.8% for session 2.Blood clot burden can be quantified with a high degree of accuracy and reproducibility from pulmonary CTA images using a semiautomated segmentation method.

Purpose: To evaluate the feasibility of flow-sensitive alternating inversion recovery (FAIR) for measuring blood flow in tumor models. Materials and Methods: In eight mice tumor models, FAIR and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed. The reliability for measuring blood flow on FAIR was evaluated using the coefficient of variation of blood flow on psoas muscle. Three regions of interest (ROIs) were drawn in the peripheral, intermediate, and central portions within each tumor. The location of ROI was the same on FAIR and DCE-MR images. The correlation between the blood flow on FAIR and perfusion-related parameters on DCE-MRI was evaluated using the Pearson correlation coefficient. Results: The coefficient of variation for measuring blood flow was 9.8%. Blood flow on FAIR showed a strong correlation with Kep (r = 0.77), percent relative enhancement (r = 0.73), and percent enhancement ratio (r = 0.81). The mean values of blood flow (mL/100 g/min) (358 vs. 207), Kep (sec−1) (7.46 vs. 1.31), percent relative enhancement (179% vs. 134%), and percent enhancement ratio (42% vs. 26%) were greater in the peripheral portion than in the central portion (P < 0.01). Conclusion: As blood flow measurement on FAIR is reliable and closely related with that on DCE-MR, FAIR is feasible for measuring tumor blood flow. J. Magn. Reson. Imaging 2010;32:738–744. © 2010 Wiley-Liss, Inc.

To prospectively evaluate the reproducibility of apparent diffusion coefficient (ADC) measurement in malignant hepatic tumors and to examine the influence of imaging methods, lesion location, and lesion size on the reproducibility of ADC measurement.The institutional review board approved the study protocol, and informed consent was obtained. Forty-nine patients underwent both breath-hold and respiratory-triggered diffusion-weighted (DW) magnetic resonance imaging on a 1.5-T system twice. Two independent readers measured the ADC of the largest malignant hepatic tumor for each patient on each image sets. Mean ADCs were compared between repeated acquisitions and imaging techniques by using the paired t test. Reproducibility of the ADC measurement and interobsever agreement were determined by using 95% Bland-Altman limits of agreement and intraclass correlation coefficients (ICCs). The effects of the imaging technique, lesion location, and lesion size on the reproducibility of the ADC measurements were assessed by comparing ICCs by using the z test.There were no significant differences in the mean ADC between repeated acquisitions for breath-hold ([1.266-1.275] x 10(-3) mm(2)/sec vs [1.285-1.290] x 10(-3) mm(2)/sec; P = .572-.634) or respiratory-triggered ([1.487-1.502] x 10(-3) mm(2)/sec vs [1.421-1.441] x 10(-3) mm(2)/sec; P = .073-.091) DW MR imaging. The mean ADCs measured by using the respiratory-triggered method ([1.421-1.502] x 10(-3) mm(2)/sec) were significantly higher than those measured by using the breath-hold method ([1.266-1.290] x 10(-3) mm(2)/sec) (Por = .001). The 95% limits of agreement between ADCs measured on repeated DW images were 28.7%-31.3% of the mean, and those between ADCs measured by two readers were 14.6%-22.5% of the mean. ADC measurement of malignant hepatic tumors tended to be more reproducible for right-lobe than for left-lobe lesions and for larger rather than smaller lesions.Changes in ADCs of less than approximately 30% fall into the range of measurement error. Imaging technique significantly affected ADCs of malignant hepatic tumors. Lesion location and size are potentially influential on the reproducibility of ADC measurement.

Pulmonary embolism (PE) is a medical condition defined as the obstruction of pulmonary arteries by a blood clot, usually originating in the deep veins of the lower limbs. PE is a common but elusive illness that can cause significant disability and death if not promptly diagnosed and effectively treated. CT Pulmonary Angiography (CTPA) is the first line imaging study for the diagnosis of PE. While clinical prediction rules have been recently developed to associate short-term risks and stratify patients with acute PE, there is a dearth of objective biomarkers associated with the long-term prognosis of the disease. Clot (embolus) burden is a promising biomarker for the prognosis and recurrence of PE and can be quantified from CTPA images. However, to our knowledge, no study has reported a method for segmentation and measurement of clot from CTPA images. Thus, the purpose of this study was to develop a semi-automated method for segmentation and measurement of clot from CTPA images. Our method was based on Modified Seeded Region Growing (MSRG) algorithm which consisted of two steps: (1) the observer identifies a clot of interest on CTPA images and places a spherical seed over the clot; and (2) a region grows around the seed on the basis of a rolling-ball process that clusters the neighboring voxels whose CT attenuation values are within the range of the mean ± two standard deviations of the initial seed voxels. The rollingball propagates iteratively until the clot is completely clustered and segmented. Our experimental results revealed that the performance of the MSRG was superior to that of the conventional SRG for segmenting clots, as evidenced by reduced degrees of over- or under-segmentation from adjacent anatomical structures. To assess the clinical value of clot burden for the prognosis of PE, we are currently applying the MSRG for the segmentation and volume measurement of clots from CTPA images that are acquired in a large cohort of patients with PE in an on-going NIH-sponsored clinical trial.

The purpose of the study was to perform a node-by-node comparison of an ADC-based diagnosis and various size-based criteria on T2-weighted imaging (T2WI) with regard to their correlation with PET/CT findings in patients with uterine cervical cancer. In 163 patients with 339 pelvic lymph nodes (LNs) with short-axis diameter >5 mm, the minimum apparent diffusion coefficient (ADC), mean ADC, short- and long-axis diameters, and ratio of long- to short-axis diameters (L/S ratio) were compared in PET/CT-positive and -negative LNs. On PET/CT, 118 (35%) LNs in 58 patients were positive. The mean value of minimum and mean ADCs, short- and long-axis diameters, and L/S ratio were different in PET/CT-positive (0.6436 x 10(-3) mm(2)/s, 0.756 x 10(-3) mm(2)/s, 10.3 mm, 13.2 mm, 1.32, respectively) and PET/CT-negative LNs (0.8893 x 10(-3) mm(2)/s, 1.019 x 10(-3) mm(2)/s, 7.4 mm, 11.0 mm, 1.49, respectively) (P < 0.05). The Az value of the minimum ADC (0.864) was greater than those of mean ADC (0.836), short-axis diameter (0.764), long-axis diameter (0.640) and L/S ratio (0.652) (P < 0.05). The sensitivity and accuracy of the minimum ADC (86%, 82%) were greater than those of the short-axis diameter (55%, 74%), long-axis diameter (73%, 58%) and L/S ratio (52%, 66%) (P < 0.05). ADC showed superior correlation with PET/CT compared with conventional size-based criteria on T2WI.