Time-intensity Curve Shape Research Articles

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Simulation of time-intensity curve based on k-space filling in breast dynamic contrast-enhanced three-dimensional magnetic resonance imaging.

This study elucidated the effects of a three-dimensional k-space trajectory incorporating the partial Fourier (PF) technique on a time-intensity curve (TIC) in a dynamic contrast-enhanced magnetic resonance imaging of a typical malignant breast tumor using a digital phantom. Images were obtained from the Cancer Imaging Archive Open Data for Breast Cancer, and 1-min scans with high temporal resolution were analyzed. The order of the k-space trajectory was set as Linear (sequential), Low-High (centric), PF (62.5%; Z-, Y-, and both directions), and Low-High Radial. k0 (center of the k-space) timing and TIC shape were affected by the chosen k-space trajectory and implementation of the PF technique. A small TIC gradient was obtained using a Low-High Radial order. If the k-space filling method (particularly the radial method) produces a gentle TIC gradient, misinterpretation could arise during the assessment of tumor malignancy status.

POS1391 DEEP LEARNING-BASED PANNUS LOCALIZATION SOFTWARE IN THE HANDS OF INFLAMMATORY ARTHRITIS USING TIME-INTENSITY CURVE (TIC) SHAPE CLASSIFICATION ON DYNAMIC MRI DATASET

BackgroundRheumatoid arthritis (RA) is a chronic inflammatory disease associated with significant functional impairment and disability, linked to inflammatory and structural articular and peri-articular damage [1]. Synovitis is a characteristic feature of RA, and is considered an important factor in disease activity and the best predictive marker of joint damage [2]. Therefore, accurate quantification of synovitis can play an important role in clinical evaluation and treatment serving as a biomarker. Pixel-by-pixel time–intensity curve (TIC) shape analysis is a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique to help visualize differently shaped TICs [3]. Pixels having TIC shape type 4, which is characterized as early enhancement followed by washout phase, were regarded as synovitis pixel [4]. A deep residual network called ResNet-50 model, a convolutional neural network (CNN) that is 50 layers deep, might effectively classify TIC shapes.ObjectivesThis study aimed to develop software that can automatically demonstrate the distribution of enhancing synovial pannus of patients with inflammatory arthritis on DCE-MRI.MethodsModified ResNet-50 was used on MATLAB. Two investigators drew regions of interest (ROIs) of muscle, bone, and synovitis under expert guidance and obtained TICs for each tissue on DCE-MRI of ten rheumatoid patients. Through cross-validation and batch evaluation, we verified the confusion matrices for the performance of the classifier. Then we identified the pixels of enhancing synovial pannus by image classification from the obtained pixel-by-pixel TIC shape and displayed them in color. The software performance was evaluated using a visual assessment on seven hand joints of one patient.Results150 ROIs for muscles, 150 ROIs for bones, and 59 ROIs for synovitis on DCE-MRI of the hand joints were drawn in ten patients and obtained 4049, 3825, and 1041 TIC shape images, respectively. The classifier’s accuracy, precision, sensitivity, and specificity were 99.6%, 99.3%, 98.4%, and 99.7%, respectively. Out of seven joints, four were assessed as good, two as fair, and one as fail.Figure 1.Software performance on contrast enhanced dynamic MRI of the wristConclusionOur classifier showed high accuracy, precision, sensitivity, and specificity. And through the comparison between manual outlining and the result of software, our software had relatively good performance. This automatic software developed using deep learning with CNN might accurately display the enhancing synovial pannus in RA.

Diagnostic value of 3D dynamic contrast-enhanced magnetic resonance imaging in lymph node metastases of head and neck tumors: a correlation study with histology.

BackgroundAccurate staging of cervical lymph nodes (LN) is pivotal for further clinical management of patients with head and neck cancer. Functional magnetic resonance imaging (MRI) such as three-dimensional (3D) dynamic contrast-enhanced (DCE) acquisition might improve the diagnosis of cervical LN metastases.PurposeTo evaluate the additional diagnostic value of high-resolution 3D T1-weighted DCE in detecting LN metastasis compared to standard morphological imaging criteria in patients with head and neck tumors as correlated to histopathology.Material and MethodsStandard MRI with 3D DCE acquisition at voxel sizes of 1 × 1×1 mm was performed in 15 patients before surgery; 92 LN of the head and neck were histopathologically analyzed. A logistic regression analysis of semi-quantitative DCE parameters, time-intensity curve (TIC) shapes, and morphological criteria was performed to differentiate benign from malignant LN.ResultsStandard MRI was sufficient for diagnosis of malignancy in LN with a short-axis diameter ≥ 15 mm (n = 17). For LN metastases with a short-axis diameter <15 mm (n = 12), however, the combination of 3D DCE MRI parameters, TIC shapes, and LN diameter significantly increased the sensitivity and specificity of diagnosing metastases (DCE + TIC shape + LN diameter: 92% and 88% vs. DCE only: 83% and 68% (P < 0.01) vs. LN diameter only: 83% and 77% (P = 0.04).ConclusionMRI including isotropic high-resolution 3D DCE acquisition combined with morphological criteria allows an accurate assessment of small cervical LN metastases in patients with head and neck cancer. For LN ≥ 15 mm diameter, morphologic imaging may suffice to diagnose metastatic disease to the LN.

Robust cerebrovascular blood velocity and flow rate estimation from 4D‐CTA

Blood velocity and flow rate information may be helpful for a wide variety of applications, but it often requires separate acquisitions. The dynamic information combined with the high spatial resolution of four-dimensional computed tomography angiography (4D-CTA) offers the possibility to quantify blood flow simultaneous to vascular anatomy. A 4D-CTA clinical protocol with a novel dedicated postprocessing were validated invitro in a patient-specific model, and tested in a pilot study of six patients. Blood flow was assessed in both internal carotid (ICAs) and vertebral (VAs) arteries by analyzing spatial displacement of contrast agent in the form of time-intensity curves (TICs). Unlike previous approaches, it does not require any a priori assumptions about TIC shape, but rather computes mean velocity and flow rates from the spatial displacement of the TICs along the automatically segmented vessels. In vitro experiments showed good agreement between 4D-CTA and flowmeter measurements under steady and pulsatile flow conditions. In vivo measurements exhibited large interpatient variability of the TIC shapes, from which blood flow rates could nevertheless be successfully measured in all patients and investigated vessels. On average, measured flow rates were 3.2±0.7ml/s (in ICAs) and 1.3±0.8ml/s (in VAs) consistent with previous reference standards. Contrary to our novel approach, which considered the full TIC shape, application of previous time-to-peak analyses based on idealized TIC shapes showed limited reliability. We demonstrate the high potential of 4D-CTA for assessing blood velocity and flow rate in addition to anatomical evaluation. The wide variety of TIC shapes encountered invivo highlights the importance of an adaptive TIC analysis as proposed in the present work.

DCE-MRI time-intensity curve visual inspection to assess pathological response after neoadjuvant therapy in locally advanced rectal cancer.

To investigate the potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate responder from non-responder patients after preoperative chemoradiotherapy (pCRT) in locally advanced rectal cancer (LARC). One hundred and fifty-eight consecutive patients were enrolled in this prospective study. We compared morphological MRI (mMRI) using T2-weighted images about tumor presence and invasiveness, and functional DCE-MRI using time-intensity curve (TIC) visual inspection (qMRI), classifying TIC shape into three types: type 1, persistent enhancement; type 2, high enhancement with plateau; type 3, high enhancement with wash-out. Clinical TNM was obtained before and after CRT by radiological consensus of two expert radiologists. Pathological tumor-nodes-metastasis classification and tumor regression grade (TRG) were confirmed as the golden standard. Non-parametric test, sensitivity, specificity, and positive and negative predictive values were calculated. Ninety-eight patients (62%) were classified as responders (TRG ≤ 2), while 60 (38%) were classified as non-responders. Sensitivity, specificity, and accuracy were 52, 78, and 62% for mMRI, and 81, 85, and 82% for qMRI, respectively. TIC visual inspection may be one of the potential biomarkers over morphological analysis using DCE-MRI data to assess pathological response after pCRT in LARC.

Quantification of hand synovitis in rheumatoid arthritis: Arterial mask subtraction reinforced with mutual information can improve accuracy of pixel-by-pixel time-intensity curve shape analysis in dynamic MRI.

Synovitis, which is a hallmark of rheumatoid arthritis (RA), needs to be precisely quantified to determine the treatment plan. Time-intensity curve (TIC) shape analysis is an objective assessment method for characterizing the pixels as artery, inflamed synovium, or other tissues using dynamic contrast-enhanced MRI (DCE-MRI). To assess the feasibility of our original arterial mask subtraction method (AMSM) with mutual information (MI) for quantification of synovitis in RA. Prospective study. Ten RA patients (nine women and one man; mean age, 56.8 years; range, 38-67 years). 3T/DCE-MRI. After optimization of TIC shape analysis to the hand region, a combination of TIC shape analysis and AMSM was applied to synovial quantification. The MI between pre- and postcontrast images was utilized to determine the arterial mask phase objectively, which was compared with human subjective selection. The volume of objectively measured synovitis by software was compared with that of manual outlining by an experienced radiologist. Simple TIC shape analysis and TIC shape analysis combined with AMSM were compared in slices without synovitis according to subjective evaluation. Pearson's correlation coefficient, paired t-test and intraclass correlation coefficient (ICC). TIC shape analysis was successfully optimized in the hand region with a correlation coefficient of 0.725 (P < 0.01) with the results of manual assessment regarded as ground truth. Objective selection utilizing MI had substantial agreement (ICC = 0.734) with subjective selection. Correlation of synovial volumetry in combination with TIC shape analysis and AMSM with manual assessment was excellent (r = 0.922, P < 0.01). In addition, negative predictive ability in slices without synovitis pixels was significantly increased (P < 0.01). The combination of TIC shape analysis and image subtraction reinforced with MI can accurately quantify synovitis of RA in the hand by eliminating arterial pixels. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Construct validity of pixel-by-pixel DCE-MRI: Correlation with conventional MRI scores in juvenile idiopathic arthritis

ObjectivesTo assess the capability of the pixel-by-pixel DCE-MRI time intensity curve (TIC)-shape analysis method in the evaluation of juvenile idiopathic arthritis (JIA) disease activity by correlating DCE-MRI parameters with semi-quantitative conventional-MRI scores of synovitis. MethodsClinical, laboratory, and (DCE)-MRI datasets of 85 JIA patients were prospectively obtained. TIC-shapes of each voxel were classified into one of seven predefined color-coded TIC shape categories. Spatial information on the relative amount of TIC-shapes, maximal enhancement (ME), maximal initial slope (MIS), initial area under the curve (iAUC), time-to-peak (TTP), enhancing volume (EV) was calculated of the synovial membrane. The grade of synovitis was scored on conventional MR images by two readers using the validated JAMRIS system. The Bonferroni method was used to correct for multiple testing, therefore, a P value of <0.0056 is considered significant (0.05/9=0.0056). ResultsThe semi-quantitative JAMRIS synovitis score correlated substantially with the ME, EV, and iAUC (Rs=0.658, P<0.001; Rs=0.618, P<0.001; Rs=0.639, P<0.001), and moderately with MIS (Rs=0.453, P<0.001). A poor correlation was observed between the relative number of TIC-shapes 2–5 and the JAMRIS synovitis score (Rs=0.209, P=0.054; Rs=0.328, P=0.002; Rs=0.241, P=0.023; Rs=−0.241, P=0.026). ConclusionIn this explorative study, both TIC shape and semi-quantitative DCE-MRI analysis methods showed moderate to substantial correlations with conventional MRI scores of disease activity, indicating that this methods are feasible. Further research is warranted whether DCE-MRI holds potential to become an objective and quantitative method for the evaluation of disease activity in JIA.

Diffusion-weighted imaging for assessment of synovial inflammation in juvenile idiopathic arthritis: a promising imaging biomarker as an alternative to gadolinium-based contrast agents

ObjectivesTo compare dynamic-contrast-enhanced MRI (DCE) and diffusion-weighted imaging (DWI) in quantifying synovial inflammation in juvenile idiopathic arthritis (JIA).MethodsRegions of interest (ROI) were drawn in the synovium of JIA patients on T1 DCE and T2 DWI, followed by extraction of the maximum enhancement (ME), maximum initial slope (MIS), time to peak (TTP), % of different time intensity curve shapes (TIC) and apparent diffusion coefficient (ADC) of the ROIs. Mann-Whitney-U test was used for comparing parameters between MRI-active and -inactive patients (defined by the juvenile arthritis MRI scoring system). Spearman’s rank was used to analyse the correlation between DCE and DWI.ResultsThirty-five JIA patients (18 MRI active and 17 MRI inactive) were included. Median age was 13.1 years and 71% were female. ME, MIS, TTP, % TIC 5 and ADC were significantly different in MRI-active versus MRI-inactive JIA with median ADC 1.49 × 10-3mm2/s in MRI-active and 1.25 × 10-3mm2/s in MRI-inactive JIA, p = 0.001, 95% confidence interval of difference in medians =0.11-0.53 × 10-3mm2/s. ADC correlated to ME, MIS and TIC 5 shapes (r = 0.62, r = 0.45, r = -0.51, respectively, all p < 0.05).ConclusionsSimilar to DCE parameters, DWI-derived ADC is significantly different in MRI-active JIA as compared to MRI-inactive JIA. The non-invasiveness of DWI combined with its possibility to detect synovial inflammation shows the potential of DWI.Key Points• MRI can quantify: dynamic contrast-enhanced and diffusion-weighted MRI can quantify synovitis• Both DWI and DCE can differentiate active from inactive JIA• The DWI-derived apparent diffusion coefficient (ADC) is higher in active JIA• DWI is non-invasive and thus safer and more patient-friendly• DWI is a potentially powerful and non-invasive imaging biomarker for JIA

Exploring Targeted Contrast-Enhanced Ultrasound to Detect Neural Inflammation

Targeted contrast-enhanced ultrasound (TCEUS) is an innovative method of molecular imaging used for detection of inflammatory biomarkers in vivo. By targeting ultrasound contrast to cell adhesion molecules (CAMs), which are known inflammatory markers within neural tissue, a more direct evaluation of neural inflammation can be made. Due to the novel nature of TCEUS, standardized methods of image analysis do not yet exist. Time intensity curve (TIC) shape analysis is currently used in magnetic resonance contrast imaging to determine temporal behavior of perfusion. Therefore, the presented research attempts to determine TIC shape analysis utility in TCEUS imaging by applying it to TCEUS scans targeted to CAMs present in neural inflammation. This was done in an animal model that underwent a traumatic spinal cord injury to induce inflammation ( n = 31). Subjects were divided into four groups, each receiving a TCEUS targeted to a different CAM seven days after surgery (P-selectin, intracellular adhesion molecule 1 [ICAM-1], vascular cell adhesion molecule 1 [VCAM-1], and control). TICs were generated using average pixel intensity within the injured region of the spinal cord. TIC shape analysis found similar curves were produced while targeting P-selectin and VCAM-1, both demonstrating rapid and sustained enhancement. Control injections demonstrated no enhancement. ICAM-1 injections demonstrated limited enhancement and a shape similar to the control.

Accurate quantitative assessment of synovitis in rheumatoid arthritis using pixel-by-pixel, time-intensity curve shape analysis.

To improve on the reproducibility and sensitivity of the assessment of patients with rheumatoid arthritis (RA), two semi-automated measurement methods of the area of enhancing pannus (AEP), based on thresholding (AEP_THRES) and pixel-by-pixel time-intensity curve analysis (AEP_TIC), were evaluated as an alternative for the gold-standard manual contouring method (AEP_MANUAL). 8 patients (7 females and 1 male) with RA of the wrist or finger joints participated in the study. A three-dimensional contrast-enhanced dynamic sequence was used at 3 T. After identifying the most relevant time-intensity curve (TIC) shape in terms of synovitis by comparing with the synovitis score using the RA-MRI scoring system, three different approaches for measuring the AEP were performed. Spearman's test of rank correlation was used to compare AEPs via two semi-automated methods (AEP_THRES and AEP_TIC) against manual segmentation (AEP_MANUAL) in the entire hand region as well as the wrist and the finger regions. The TIC shape of "washout after fast initial enhancement" had excellent correlation with synovitis score (r = 0.809). The correlation coefficient between AEP_TIC and AEP_MANUAL was evaluated to be better than that of AEP_THRES and AEP_MANUAL in the wrist region (AEP_THRES: r = 0.716, AEP_TIC: r = 0.815), whereas these were of comparable accuracy for the entire hand and the finger regions. This study suggests that TIC analysis may be an alternative to manual contouring for pannus quantification and provides important clinical information of the extent of the disease in patients with RA. TIC shape analysis can be applied for new quantitative assessment for RA synovitis in the wrist.

Renal blood perfusion in GK rats using targeted contrast enhanced ultrasonography.

To explore application of targeted contrast enhanced ultrasonography in diagnosis of early stage vascular endothelial injury and diabetic nephropathy. Targeted SonoVue-TM microbubble was prepared by attaching anti-TM monoclonal antibody to the surface of ordinary microbubble SonoVue by biotin - avidin bridge method and ultrasonic instrument was used to evaluate the developing situation of targeted microbubble invitro. Twenty 12-week-old male GK rats and 20 Wistar rats were enrolled in this study, and were randomly divided into targeted angiography group and ordinary angiography group. Targeted microbubbles SonoVue-TM or general microbubble SonoVue were rapidly injected to the rats via tail vein; the developing situation of the two contrast agents in rats kidneys was dynamically observed. Time-intensity curve was used to analyze rat kidney perfusion characteristics in different groups. Targeted ultrasound microbubble SonoVue-TM was successfully constructed, and it could be used to develop an external image. Targeted microbubbles SonoVue-TM enabled clear development of experimental rat kidney. Time-intensity curve shapes of rat kidney of the two groups showed as single apex with steep ascending and slowly descending branch. Compared with the control group, the rising slope of the GK rat renal cortex, medulla in targeted angiography group increased (P<0.05); the peak intensity of medulla increased (P<0.05), and the total area under the curve of medulla increased (P<0.05). Compared with control group, the ascending branch of the GK rat in renal cortex, medulla in ordinary angiography group increased (P<0.05). The peak intensity of the curve increased (P<0.05), and the total area under the curve increased (P<0.05). Compared with the ordinary angiography group, the peak of GK rat medulla curve in targeted angiography group intensity increased (P<0.05), and the total area under the curve increased (P<0.05). Targeted microbubbles SonoVue-TM can make a clear development of experimental rat kidney, its stable performance meet the requirement of ultrasonic observation time limit, and it can reflect early changes of blood perfusion in GK rat kindey.

Standardized Index of Shape (SIS): a quantitative DCE-MRI parameter to discriminate responders by non-responders after neoadjuvant therapy in LARC

To investigate the potential of DCE-MRI to discriminate responders from non-responders after neoadjuvant chemo-radiotherapy (CRT) for locally advanced rectal cancer (LARC). We investigated several shape parameters for the time-intensity curve (TIC) in order to identify the best combination of parameters between two linear parameter classifiers. Seventy-four consecutive patients with LARC were enrolled in a prospective study approved by our ethics committee. Each patient gave written informed consent. After surgery, pathological TNM and tumour regression grade (TRG) were estimated. DCE-MRI semi-quantitative analysis (sqMRI) was performed to identify the best parameter or parameter combination to discriminate responders from non-responders in response monitoring to CRT. Percentage changes of TIC shape descriptors from the baseline to the presurgical scan were assessed and correlated with TRG. Receiver operating characteristic analysis and linear classifier were applied. Forty-six patients (62.2%) were classified as responders, while 28 subjects (37.8%) were considered as non-responders. sqMRI reached a sensitivity of 93.5% and a specificity of 82.1% combining the percentage change in Maximum Signal Difference (ΔMSD) and Wash-out Slope (ΔWOS), the Standardized Index of Shape (SIS). SIS obtains the best result in discriminating responders from non-responders after CRT in LARC, with a cut-off value of -3.0%. • DCE-MRI shape descriptors are investigated to assess preoperative CRT response in LARC. • Identification of the best TIC shape descriptors combination through a linear classifier. • Identification of a single MRI index to predict neoadjuvant treatment response.

Pixel-by-pixel analysis of DCE-MRI curve shape patterns in knees of active and inactive juvenile idiopathic arthritis patients.

To compare DCE-MRI parameters and the relative number of time-intensity curve (TIC) shapes as derived from pixel-by-pixel DCE-MRI TIC shape analysis between knees of clinically active and inactive juvenile idiopathic arthritis (JIA) patients. DCE-MRI data sets were prospectively obtained. Patients were classified into two clinical groups: active disease (n = 43) and inactive disease (n = 34). Parametric maps, showing seven different TIC shape types, were created per slice. Statistical measures of different TIC shapes, maximal enhancement (ME), maximal initial slope (MIS), initial area under the curve (iAUC), time-to-peak (TTP), enhancing volume (EV), volume transfer constant (K(trans)), extravascular space fractional volume (V(e)) and reverse volume transfer constant (k(ep)) of each voxel were calculated in a three-dimensional volume-of-interest of the synovial membrane. Imaging findings from 77 JIA patients were analysed. Significantly higher numbers of TIC shape 4 (P = 0.008), median ME (P = 0.015), MIS (P = 0.001) and iAUC (P = 0.002) were observed in clinically active compared with inactive patients. TIC shape 5 showed higher presence in the clinically inactive patients (P = 0.036). The pixel-by-pixel DCE-MRI TIC shape analysis method proved capable of differentiating clinically active from inactive JIA patients by the difference in the number of TIC shapes, as well as the descriptive parameters ME, MIS and iAUC. • The pixel-by-pixel TIC shape method differentiates clinically active and inactive JIA patients • Significantly higher numbers of TIC shape 4 were observed in clinically active patients • DCE-MRI parameters ME, MIS and iAUC differ between active and inactive patients • The pixel-by-pixel analysis method allows direct visualization of the heterogeneously distributed disease • The DCE-MRI TIC shape method may serve as a quantitative outcome measure.

Internal Jugular Vein Blood Flow in Multiple Sclerosis Patients and Matched Controls

The aim of the study was to investigate the Internal Jugular Veins dynamics using contrast enhanced ultrasonography in Multiple Sclerosis patients, clinically isolated syndrome patients and healthy controls. Contrast enhanced ultrasonography imaging of the Internal Jugular Vein was performed in fifty-eight patients with Multiple Sclerosis, seven clinically isolated syndrome patients and in thirteen healthy controls. Time-intensity curves were quantified using a semi-automated method and compared with clinical disease outcomes. Wash-out parameters were calculated and six Time-intensity curves shapes were created. Significantly reduction of wash-out rate in Internal Jugular Veins was detected in Multiple Sclerosis patients compared to healthy controls [22.2% (2.7%–65.9%) vs. 33.4% (16.2%–76.8%); P<0.005]. Internal Jugular Vein enhancement was heterogeneous in patients with Multiple Sclerosis and consisted of slow wash-out Time-intensity curves shapes, compared with almost only one type of Time-intensity curves shape in control subjects that correspond to fast enhancement and fast wash-out. The vein wash-in parameters were similar in Multiple Sclerosis group compared with controls. A significant correlation was found between Internal Jugular Vein wash-out and level of disability (R = −0.402, p<0.05). Contrast enhanced ultrasonography of the Internal Jugular Vein with time intensity curve analysis revealed alterations of cerebral venous outflow in Multiple Sclerosis patients, however mechanisms that determine this condition remains unclear.

Use of dynamic contrast enhanced time intensity curve shape analysis in MRI: theory and practice

Use of dynamic contrast enhanced time intensity curve shape analysis in MRI: theory and practice Cristina Lavini,1 Maarten S Buiter,2 Mario Maas1 1Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands; 2Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands Abstract: The analysis of dynamic contrast enhanced data using the classification of the time intensity curve (TIC) shape is widely employed both in its region of interest and pixel by pixel variants. While its application in breast imaging is established and documented by a large amount of scientific works, its use for other body parts is still scattered and there is no consensus as to whether the method can be used alone to perform differential diagnosis in cancer or in inflammatory diseases. In this review we evaluate all the literature which makes use of TIC shape analysis in tissues other than breast, discuss the results, highlight the possible shortcomings, and suggest directions for future research. Keywords: DCE-MRI, TIC shape, pattern recognition, review

Characteristics of synovial inflammation in early arthritis analysed by pixel-by-pixel time-intensity curve shape analysis

Dynamic contrast-enhanced (DCE-MRI) time-intensity curve (TIC) shape analysis has previously been used in oncology, where fast initial enhancement and early washout are associated with malignancy. As RA synovium has some tumour-like features, we investigated DCE-MRI TIC shape expression in early arthritis in relationship to diagnosis. Twenty-eight DMARD-naïve, early arthritis patients (<1 year) with inflammation of at least one knee joint were included. At baseline DCE-MRI of the inflamed knee joint was performed, and the TIC shape type expression, maximal enhancement, maximum slope of increase and total volume of enhancing pixels were calculated. In addition, disease activity parameters were determined. At 2 years of follow-up, patients were classified as RA or non-RA according to established classification criteria. Type 4 TIC shape, characterized by fast initial enhancement followed by a quick washout phase, was significantly higher in patients fulfilling classification criteria for RA after 2 years of follow-up compared with non-RA patients (15.6 and 7.9%, respectively, P = 0.02). All other DCE-MRI parameters showed no differences between the groups, highlighting the specificity of this observation. A high expression of aggressive DCE-MRI TIC shape Type 4 is associated with RA. Our results are consistent with the view that increased vascularity plays a key role in the pathogenesis of RA. This study underlines the rationale for further studies investigating the prospect of DCE-MRI TIC shape analysis as a diagnostic tool in early arthritis and the relationship with development of destructive disease.

Model‐based, semiquantitative and time intensity curve shape analysis of dynamic contrast‐enhanced MRI: A comparison in patients undergoing antiangiogenic treatment for recurrent glioma

To compare time intensity curve (TIC)-shape analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data with model-based analysis and semiquantitative analysis in patients with high-grade glioma treated with the antiangiogenic drug bevacizumab. Fifteen patients had a pretreatment and at least one posttreatment DCE-MRI. We applied a pixel-by-pixel TIC shape analysis, where TICs are classified into five different types according to their shape, and calculated the occurrence of each TIC type in the region of interest (ROI). The results were compared to the pharmacokinetic model (PKM) parameters K(trans) , K(ep) , V(e) , and V(i) , and with the semiquantitative parameters maximum enhancement (ME) and initial slope of increase (ISI). The relative amount of type 2 and 4 TIC shape significantly correlated with the parameter K(ep) but not with K(trans) or V(e) . The PKM parameter V(e) and the semiquantitative parameters ME and ISI showed significant changes after treatment. None of the TIC shapes individually showed significant changes. The semiquantitative parameters ME and ISI are more sensitive to the effect of the bevacizumab than K(trans) and V(e) . The pixel-by-pixel TIC shape analysis parameters are not sensitive to the effect of bevacizumab, although they can be seen as surrogates for the PKM parameter K(ep) .

Rheumatoid Synovial Inflammation: Pixel-by-Pixel Dynamic Contrast-enhanced MR Imaging Time-Intensity Curve Shape Analysis—A Feasibility Study

To analyze the distribution of different shapes of time-intensity curves (TICs) in synovial tissue of patients with rheumatoid arthritis (RA) and to compare relative numbers of TIC shapes between patients with RA and healthy control subjects. This prospective study was approved by the institutional review board; patients and control subjects gave written informed consent. Dynamic contrast material-enhanced magnetic resonance (MR) imaging of the knee joint in five patients with early RA and in five control subjects was performed. Parametric maps showing seven TIC shape types were created. Spatial information of the synovial TIC shape distribution pattern and relative number of TIC shapes were calculated on a three-dimensional region of interest. Relative TIC shape numbers were compared by using a nonparametric Mann-Whitney U test. Synovial enhancement in patients with RA consisted of type 2 TIC shapes (slow enhancement) with heterogeneous zones of types 3 (fast enhancement followed by plateau phase), 4 (fast enhancement followed by early washout phase), and 5 (fast enhancement followed by slow enhancement increase) TIC shapes, compared with almost only type 2 TIC shapes in control subjects. The heterogeneous zones were seen in the lateral and medial knee compartments and around the cruciate ligaments. A significantly higher relative number of type 4 TIC shapes was observed in the patient group compared with the control group (16.5% vs 6.9%, P = .008). The pixel-by-pixel dynamic contrast-enhanced MR imaging TIC shape analysis may help distinguish patients with RA from control subjects on the basis of the relative number of type 4 TIC shapes. This study provides the rationale for future research to evaluate the utility of this approach in clinical practice.

Role of MRI in osteosarcoma for chemotherapy response evaluation and prediction: Correlation with histological necrosis

10540 Background:Histologic necrosis (HN), the current gold standard for response evaluation in osteosarcoma (OS), is accessible only after neoadjuvant chemotherapy (NACT) and may get affected by confounding factors. Thus, it would be useful to have surrogate markers of response evaluation and prognostication using magnetic resonance imaging (MRI) to individualize therapy. Method:Thirty-one treatment naïve OS patients received 3 cycles of NACT followed by surgery during 2006–2008. All patients underwent baseline and post-NACT conventional(C), diffusion-weighted (DW) and dynamic contrast enhanced (DCE) MRI. Taking ‘Huvos grading’ (good response &gt;/= 90% HN) as reference standard, various parameters of MRI were compared with it. Tumor considered as ellipsoidal; volume (V), average tumor plane (ATP) and relative(r)-ATP (ATP/body surface area) were calculated using standard formula for ellipse. Receiver operating characteristic curves were generated to assess the best threshold and predictability. Sensitivity and specificity were calculated along with 95% confidence limits. After deriving thresholds for each parameter in univariable analysis, multivariable analysis was carried out. Results: Both pre-and post NACT, absolute and relative size parameters were well correlated with HN, though post NACT change in parameters did not. Apparent diffusion coefficient (ADC), either pre-and post NACT measurements or change following chemotherapy were not correlating well. However, on adjusting for volume, significant correlation was found. Thus, we could derive a new parameter diffusion per unit volume (DV= ADC /V). Change in shape of time intensity curve did not show significant correlation. Conclusions: In OS, NACT response can be assessed and predicted by conventional and DW- MRI early in the disease course which correlates well with HN. DV seems to be a sensitive substitute for response evaluation. For DCE-MRI, more sophisticated models in future studies might be useful. [Table: see text] No significant financial relationships to disclose.