The height for screw index (HSI) predicts the development of C2 nerve dysfunction associated with C1 lateral mass screw fixation for atlantoaxial instability.

PURPOSE: To propose a new measure, the height for screw index (HSI), as a predictor of C2 nerve dysfunction in patients who have received posterior C1 lateral mass screw (C1LMS) fixation for atlantoaxial instability and to examine whether the HSI scores correlated with the development of C2 nerve dysfunction through retrospective analysis of 104 C1LMS inserted in 52 patients with atlantoaxial instability. METHODS: The medical records of patients who underwent C1LMS fixation were retrospectively reviewed. C1LMS, 3.5 mm in diameter, was inserted for atlantoaxial stabilization. The sagittal plane of the planned C1LMS trajectory was reconstructed from CT images. The HSI was defined as the difference in height between C2 ganglion and its corresponding foramen. C2 nerve function was assessed using a validated visual analog scale questionnaire. Each foramen receiving C1LMS was considered as a single unit and patients were categorized to group 1, HSI ≥4.0 mm; group 2, HSI <4.0 mm. RESULTS: The mean HSI score was 4.7 ± 0.8 mm (range 3.1-6.5 mm) with 85 (81.7 %) units in group 1, and 19 (18.3 %) units in group 2. Fourteen (13.5 %, 14/104) units developed C2 nerve dysfunction. C2 nerve dysfunction was reported in 4 units in group 1, and 10 units in group 2, respectively. The percentage of C2 nerve dysfunction was significantly higher in group 2 than that in group 1 (P < 0.001, Pearson Chi-square test). CONCLUSIONS: The HSI score correlates with the development of C2 nerve dysfunction in patients receiving C1LMS fixation for atlantoaxial instability and may be a useful predictor of C2 nerve dysfunction.

EADC Values in Diagnosis of Renal Lesions by 3.0 T Diffusion-Weighted Magnetic Resonance Imaging: Compared with the ADC Values.

Exponential apparent diffusion coefficient (EADC) is an indicator of diffusion-weighted imaging (DWI) and reflects the pathological changes of tissues quantitatively. However, no study has been investigated in the space-occupying kidney disease using EADC values. This study aims to evaluate the diagnostic role of EADC values at a high magnetic field strength (3.0 T) in kidney neoplastic lesions, compared with that of the ADC values. Ninety patients with suspected renal tumors (including 101 suspected renal lesions) and 20 healthy volunteers were performed MRI scanning. Diffusion-weighted imaging was performed with a single-shot spin-echo echo-planar imaging (SE-EPI) sequence at a diffusion gradient of b = 500 s/mm2. We found renal cell carcinoma (RCC) can be distinguished from angiomyolipoma, and clear cell carcinoma can be distinguished from non-clear cell carcinoma by EADC value. There was significant difference in overall EADC values between renal cell carcinoma (0.150 ± 0.059) and angiomyolipoma (0.270 ± 0.108) when b value was 500 s/mm2. When receiver operating characteristic (ROC) was higher than 0.192, the sensitivity and specificity of EADC value of renal cell carcinoma were 84.6 and 81.1 %, respectively. In conclusion, EADC map shows the internal structure of the kidney tumor more intuitively than the ADC map dose, and is also in line with the observation habits of the clinicians. EADC can be used as an effective imaging method for tumor diagnosis.

Feature selection based on unsupervised clustering evaluation for predicting neoadjuvant chemoradiation response for patients with locally advanced rectal cancer.

Accurate response prediction allows for personalized cancer treatment of locally advanced rectal cancer (LARC) with neoadjuvant chemoradiation. In this work, we designed a convolutional neural network (CNN) feature extractor with switchable 3D and 2D convolutional kernels to extract deep learning features for response prediction. Compared with radiomics features, convolutional kernels may adaptively extract local or global image features from multi-modal MR sequences without the need of feature predefinition. We then developed an unsupervised clustering based evaluation method to improve the feature selection operation in the feature space formed by the combination of CNN features and radiomics features. While normal process of feature selection generally includes the operations of classifier training and classification execution, the process needs to be repeated many times after new feature combinations were found to evaluate the model performance, which incurs a significant time cost. To address this issue, we proposed a cost effective process to use a constructed unsupervised clustering analysis indicator to replace the classifier training process by indirectly evaluating the quality of new found feature combinations in feature selection process. We evaluated the proposed method using 43 LARC patients underwent neoadjuvant chemoradiation. Our prediction model achieved accuracy, area-under-curve (AUC), sensitivity and specificity of 0.852, 0.871, 0.868, and 0.735 respectively. Compared with traditional radiomics methods, the prediction models (AUC = 0.846) based on deep learning-based feature sets are significantly better than traditional radiomics methods (AUC = 0.714). The experiments also showed following findings: (1) the features with higher predictive power are mainly from high-order abstract features extracted by CNN on ADC images and T2 images; (2) both ADC_Radiomics and ADC_CNN features are more advantageous for predicting treatment responses than the radiomics and CNN features extracted from T2 images; (3) 3D CNN features are more effective than 2D CNN features in the treatment response prediction. The proposed unsupervised clustering indicator is feasible with low computational cost, which facilitates the discovery of valuable solutions by highlighting the correlation and complementarity between different types of features.

A subregion-based survival prediction framework for GBM via multi-sequence MRI space optimization and clustering-based feature bundling and construction.

Aiming at accurate survival prediction of Glioblastoma (GBM) patients following radiation therapy, we developed a subregion-based survival prediction framework via a novel feature construction method on multi-sequence MRIs. The proposed method consists of two main steps: (1) a feature space optimization algorithm to determine the most appropriate matching relation derived between multi-sequence MRIs and tumor subregions, for using multimodal image data more reasonable; (2) a clustering-based feature bundling and construction algorithm to compress the high-dimensional extracted radiomic features and construct a smaller but effective set of features, for accurate prediction model construction. For each tumor subregion, a total of 680 radiomic features were extracted from one MRI sequence using Pyradiomics. Additional 71 geometric features and clinical information were collected resulting in an extreme high-dimensional feature space of 8231 to train and evaluate the survival prediction at 1 year, and the more challenging overall survival prediction. The framework was developed based on 98 GBM patients from the BraTS 2020 dataset under five-fold cross-validation, and tested on an external cohort of 19 GBM patients randomly selected from the same dataset. Finally, we identified the best matching relationship between each subregion and its corresponding MRI sequence, a subset of 235 features (out of 8231 features) were generated by the proposed feature bundling and construction framework. The subregion-based survival prediction framework achieved AUCs of 0.998 and 0.983 on the training and independent test cohort respectively for 1 year survival prediction, compared to AUCs of 0.940 and 0.923 for survival prediction using the 8231 initial extracted features for training and validation cohorts respectively. Finally, we further constructed an effective stacking structure ensemble regressor to predict the overall survival with the C-index of 0.872. The proposed subregion-based survival prediction framework allow us to better stratified patients towards personalized treatment of GBM.

Illustration of the obstacles in computerized lung segmentation using examples.

PURPOSE: Automated lung volume segmentation is often a preprocessing step in quantitative lung computed tomography (CT) image analysis. The objective of this study is to identify the obstacles in computerized lung volume segmentation and illustrate those explicitly using real examples. Awareness of these "difficult" cases may be helpful for the development of a robust and consistent lung segmentation algorithm. METHODS: We collected a large diverse dataset consisting of 2768 chest CT examinations acquired on 2292 subjects from various sources. These examinations cover a wide range of diseases, including lung cancer, chronic obstructive pulmonary disease, human immunodeficiency virus, pulmonary embolism, pneumonia, asthma, and interstitial lung disease (ILD). The CT acquisition protocols, including dose, scanners, and reconstruction kernels, vary significantly. After the application of a "neutral" thresholding-based approach to the collected CT examinations in a batch manner, the failed cases were subjectively identified and classified into different subgroups. RESULTS: Totally, 121 failed examinations are identified, corresponding to a failure ratio of 4.4%. These failed cases are summarized as 11 different subgroups, which is further classified into 3 broad categories: (1) failure caused by diseases, (2) failure caused by anatomy variability, and (3) failure caused by external factors. The failure percentages in these categories are 62.0%, 32.2%, and 5.8%, respectively. CONCLUSIONS: The presence of specific lung diseases (e.g., pulmonary nodules, ILD, and pneumonia) is the primary issue in computerized lung segmentation. The segmentation failures caused by external factors and anatomy variety are relatively low but unavoidable in practice. It is desirable to develop robust schemes to handle these issues in a single pass when a large number of CT examinations need to be analyzed.

Biomarkers of joint metabolism and bone mineral density are associated with early knee osteoarthritis in premenopausal females.

INTRODUCTION: Biomarkers of bone and cartilage metabolism were proposed as early diagnosis indicators for knee osteoarthritis (OA), however, which were influenced by disease stage, age, and menopause state. Accurate diagnosis indicators are eagerly awaited. The current study aims to investigate associations of joint metabolism biomarkers and bone mineral density (BMD) with early knee OA in males and premenopausal females before age 50 years. METHOD: A total of 189 patients aged before 50 years with early knee OA and 152 healthy participants were enrolled. Levels of bone biomarkers (PINP, OC, and CTX-I) and cartilage biomarkers (PIIANP, COMP, CTX-II, and MMP-3) were assessed. BMD was measured at the lumbar, femoral neck, and hip. Multivariate regression analyses were performed to evaluate the relationship between biomarkers, BMD, and early knee OA. RESULTS: Serum COMP, urine CTX-II and BMD at femoral neck and hip were increased in premenopausal patients as compared to control; with serum PINP and OC reduced. Meanwhile, serum COMP, urine CTX-II, and BMD at femoral neck and hip showed positive associations with premenopausal early knee OA, while serum PINP had negative association. However, in male patients, only serum COMP was higher than control, and no association of biomarkers or BMD was found with early knee OA. CONCLUSIONS: The joint metabolism biomarkers and BMD showed multiple associations with early knee OA in premenopausal females, but not in males aged before 50 years. It was suggested that sex differences should be taken into account when evaluating cartilage and bone metabolism in early knee OA. Key Points • The joint metabolism biomarkers and BMD are associated with early knee OA in premenopausal females, but not in males aged before 50 years. • Sex differences should be taken into account when evaluating cartilage and bone metabolism in early knee OA.

A hybrid feature selection-based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images.

PURPOSE: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. METHODS: We proposed an evolutionary feature selection-based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2-FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416-dimension radiomic features extracted from four sequences of MRIs and 128-dimension high-order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including nonenhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross validation, the model was independently validated using the rest 35 patients from the same database. RESULTS: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state-of-the-art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. CONCLUSIONS: We demonstrated a robust and computational cost-effective hybrid segmentation approach for glioma and its subregions on multi-sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

A hybrid feature selection-based brain tumor detection and segmentation in multiparametric magnetic resonance imaging.

PURPOSE: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. METHODS: We proposed an evolutionary feature selection-based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2-FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416-dimensions radiomics features extracted from four sequences of MRIs and 128-dimensions high-order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non-enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross-validation, and the model was independently validated using the rest 35 patients from the same database. RESULTS: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state-of-the-art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. CONCLUSIONS: We demonstrated a robust and computational cost-effective hybrid segmentation approach for glioma and its subregions on multi-sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

Clinical Applications of DSC-MRI Parameters Assess Angiogenesis and Differentiate Malignant From Benign Soft Tissue Tumors in Limbs.

OBJECTIVE: To investigate the correlation between dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) parameters and angiogenesis and to explore prospectively the feasibility of using DSC-MRI to differentiate malignant from benign soft tissue tumors (STTs) in limbs. METHODS: This prospective study included 33 patients with STTs in limbs who underwent DSC-MRI after bolus Gd-DTPA infusion. All STTs were confirmed by pathological examination after surgery and microvessel density (MVD), vascular endothelial growth factor (VEGF) expression, were evaluated by immune-histochemical analysis. Semiquantitative DSC-MRI parameters, including negative enhancement integral (NEI), maximum slopes of decrease (MSD) and increase (MSI), and mean time to enhancement were calculated by postprocessing in workstation. The correlation was analyzed between DSC-MRI parameters and angiogenesis factors. Then, the DSC-MRI parameters were compared between benign and malignant STTs and evaluated for diagnostic efficiency by receiver operating characteristic. RESULTS: The 33 evaluated tumors were consisted of 13 benign and 20 malignant STTs in limbs. Significant positive correlations were observed between NEI, MSD, MSI and MVD, VEGF (p < 0.05). However, mean time to enhancement had no correlation with MVD and VEGF. The benign and malignant STTs differed significantly in terms of NEI, MSD, and MSI (p < 0.05). The areas under the curve (AUC) of NEI, MSD, and MSI were 0.915, 0.862, and 0.815 for discriminating between benign and malignant STTs, respectively. CONCLUSION: DSC-MRI parameters are positively correlated with MVD and VEGF, which can evaluate angiogenesis indirectly. Furthermore, DSC-MRI can be considered as one of assistant noninvasive MR imaging technique in differentiation between benign and malignant STTs in limbs.

Lung cancer therapy using doxorubicin and curcumin combination: Targeted prodrug based, pH sensitive nanomedicine.

Lung cancer is the leading cause of cancer death worldwide. To overcome the toxic side effects and multidrug resistance (MDR) during doxorubicin (DOX) chemotherapy, a urokinase plasminogen activator receptor (uPAR) targeting U11 peptide decorated, pH-sensitive, dual drugs co-encapsulated nanoparticles (NPs) system is employed in this study. A U11 peptide conjugated, pH-sensitive DOX prodrug (U11-DOX) was synthesized and used as materials to produce NPs. A curcumin (CUR) and U11-DOX co-encapsulated NPs system (U11-DOX/CUR NPs) was constructed to treat lung cancer. After the characterization of biophysical properties of this NPs system, synergistic chemotherapeutic efficacy was evaluated in both cultured cancer cells and tumor-bearing animal model. U11-DOX/CUR NPs had a uniformly spherical shape with a core-shell structure. The mean particle size and zeta potential of the U11-DOX/CUR NPs was 121.3 nm and -33.5 mV, with a DOX and CUR EE of 81.7 and 90.5%, respectively. The DOX release from U11-DOX/CUR NPs was 83.5, 55.2, and 32.8% correspondence to the pH of 5.0, 6.0 and 7.4. Cellular uptake efficiency of U11-DOX/CUR NPs was significantly higher than non U11 peptide decorated DOX/CUR NPs. U11-DOX/CUR NPs displayed a pronounced synergy effects in vitro and an obvious tumor tissue accumulation efficiency in vivo. In vivo antitumor experiment showed that U11-DOX/CUR NPs could inhibit the tumor growth to a level of 85%.In vitro and in vivo studies demonstrated that U11-DOX/CUR NPs is a sustained released, pH responsive, synergistic antitumor system. This study suggests that the U11-DOX/CUR NPs have promising potential for combination treatment of lung cancer.

A meta-analysis of the accuracy of prostate cancer studies which use magnetic resonance spectroscopy as a diagnostic tool.

OBJECTIVE: We aimed to do a meta-analysis of the existing literature to assess the accuracy of prostate cancer studies which use magnetic resonance spectroscopy (MRS) as a diagnostic tool. MATERIALS AND METHODS: Prospectively, independent, blind studies were selected from the Cochrane library, Pubmed, and other network databases. The criteria for inclusion and exclusion in this study referenced the criteria of diagnostic research published by the Cochrane center. The statistical analysis was adopted by using Meta-Test version 6.0. Using the homogeneity test, a statistical effect model was chosen to calculate different pooled weighted values of sensitivity, specificity, and the corresponding 95% confidence intervals (95% CI). The summary receiver operating characteristic (SROC) curves method was used to assess the results. RESULTS: We chose two cut-off values (0.75 and 0.86) as the diagnostic criteria for discriminating between benign and malignant. In the first diagnostic criterion, the pooled weighted sensitivity, specificity, and corresponding 95% CI (expressed as area under curve [AUC]) were 0.82 (0.73, 0.89), 0.68 (0.58, 0.76), and 83.4% (74.97, 91.83). In the second criterion, the pooled weighted sensitivity, specificity, and corresponding 95% CI were 0.64 (0.55, 0.72), 0.86 (0.79, 0.91) and 82.7% (68.73, 96.68). CONCLUSION: As a new method in the diagnostic of prostate cancer, MRS has a better applied value compared to other common modalities. Ultimately, large scale RCT (randomized controlled trial) randomized controlled trial studies are necessary to assess its clinical value.

Severity and distribution of cartilage damage and bone marrow edema in the patellofemoral and tibiofemoral joints in knee osteoarthritis determined by MRI.

The aim of the present study was to analyze the distribution and severity of cartilage damage (CD) and bone marrow edema (BME) of the patellofemoral and tibiofemoral joints (PFJ and TFJ, respectively) in patients with knee osteoarthritis (OA), and to determine whether a correlation exists between BME and CD in knee OA, using magnetic resonance imaging (MRI). Forty-five patients diagnosed with knee OA (KOA group) and 20 healthy individuals (control group) underwent sagittal multi-echo recalled gradient echo sequence scans, in addition to four conventional MR sequence scans. Knee joints were divided into 15 subregions by the whole-organ MRI scoring method. MRIs of each subregion were analyzed for the presence of CD, CD score and BME score. The knee joint activity functional score was determined using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) in the KOA group. Statistical analyses were used to compare the CD incidence; CD score and BME score between the PFJ and TFJ. Whether a correlation existed among body mass index, BME score, WOMAC pain score and CD score was also examined. Among the 675 subregions analyzed in the KOA group, 131 exhibited CD (CD score, 1-6). These 131 subregions were primarily in the PFJ (80/131, 61.07%), with the remainder in the TFJ (51/131, 38.93%). Thirty-three subregions had a CD score of 1, including 24 PFJ subregions (72.73%) and 9 TFJ subregions (27.27%). Among the 103 subregions with BME, the PFJ accounted for 60 (58.25%) and the TFJ for 43 (41.75%). A significant positive correlation was found between the BME and CD scores. In conclusion, CD and BME occurred earlier and more often in the PFJ compared to the TFJ in knee OA, and BME is an indirect sign of CD.

Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches.

PURPOSE: To aid a consistent segmentation of pulmonary nodules, the authors describe a novel computerized scheme that utilizes a freehand sketching technique and an improved break-and-repair strategy. METHODS: This developed scheme consists of two primary parts. The first part is freehand sketch analysis, where the freehand sketching not only serves a natural way of specifying the location of a nodule, but also provides a mechanism for inferring adaptive information (e.g., the mass center, the density, and the size) in regard to the nodule. The second part is an improved break-and-repair strategy. The improvement avoids the time-consuming ray-triangle intersections using spherical bins and replaces the original global implicit surface reconstruction with a local implicit surface fitting and blending scheme. The performance of this scheme, including accuracy and consistence, was assessed using 50 CT examinations in the Lung Image Database Consortium (LIDC). For each of these examinations, a single nodule was selected under the aid of a publically available tool to assure these nodules were diverse in size, location, and density. Two radiologists were asked to use the developed tool to segment these nodules twice at different times (at least three months apart). A Hausdorff distance based method was used to assess the discrepancies (agreements) between the computerized results and the results by the four radiologists in the LIDC as well as the inter- and intrareader agreements in freehand sketching. RESULTS: The maximum and mean discrepancies in boundary outlines between the computerized scheme and the radiologists were 2.73 ± 1.32 mm and 1.01 ± 0.47 mm, respectively. When the nodules were classified (binned) into different size ranges, the maximum errors ranged from 1.91 to 4.13 mm; but smaller nodules had larger percentage discrepancies in term of size. Under the aid of the developed scheme, the inter- and intrareader variability in averaged maximum discrepancy across all types of pulmonary nodules were consistently smaller than 0.15 ± 0.07 mm. The computational cost in time of segmenting a pulmonary nodule ranged from 0.4 to 2.3 s with an average of 1.1 s for a typical desktop computer. CONCLUSIONS: The experiments showed that this scheme could achieve a reasonable performance in nodule segmentation and demonstrated the merits of incorporating freehand sketching into pulmonary nodule segmentation.

Age and sex related measurement of craniofacial soft tissue thickness and nasal profile in the Chinese population.

The purpose of this study was to acquire accurate data of craniofacial soft tissue thickness (CFSTT) and nasal profile in Chinese people of Han population. A total of 31 anatomical landmarks and 4 nasal profile parameters were determined using magnetic resonance imaging (MRI) in 425 subjects (233 males and 192 females). In the present study, the mean CFSTT values of male subjects exceeded those of female subjects at most anatomical landmarks except at seven (22.58%) and 6 out of the 7 landmarks were bilateral anatomical landmark points. The age-related and sex × age interactions were found to be statistically significant at all landmarks. Significant differences were found in the nasal profile data of males and females, and 15 out of 20 different groups had significant differences between sexes, and the mean values of nasal length, nasal height, nasal depth and nasal breadth in males were all greater than those in females. Furthermore, both CFSTT and nasal profile showed good correlation with age. The thickest CFSTT of male and female were found at the respective ages of 45-59 and 35-44, and the nasal profile becomes more constant after 24 years of age. CFSTT of the lower part of the face shows greater variation compared to the upper part, so special care needs to be applied when reconstructing the lower portion of the face. Our data on CFSTT and nasal profile for the Chinese Xi'an Han population is important in understanding craniofacial characteristics of the Chinese population and might be potentially helpful in forensic identification.

Paramagnetic particles carried by cell-penetrating peptide tracking of bone marrow mesenchymal stem cells, a research in vitro.

The ability to track the distribution and differentiation of stem cells by high-resolution imaging techniques would have significant clinical and research implications. In this study, a model cell-penetrating peptide was used to carry gadolinium particles for magnetic resonance imaging of the mesenchymal stem cells. The mesenchymal stem cells were isolated from rat bone marrow by Percoll and identified by osteogenic differentiation in vitro. The cell-penetrating peptides labeled with fluorescein-5-isothiocyanate and gadolinium were synthesized by a solid-phase peptide synthesis method and the relaxivity of cell-penetrating peptide-gadolinium paramagnetic conjugate on 400 MHz nuclear magnetic resonance was 5.7311 +/- 0.0122 m mol(-1) s(-1), higher than that of diethylenetriamine pentaacetic acid gadolinium (p < 0.05). Fluorescein imaging confirmed that this new peptide could internalize into the cytoplasm and nucleus. Gadolinium was efficiently internalized into mesenchymal stem cells by the peptide in a time- or concentration-dependent fashion, resulting in intercellular T1 relaxation enhancement, which was obviously detected by 1.5 T magnetic resonance imaging. Cytotoxicity assay and flow cytometric analysis showed the intercellular contrast medium incorporation did not affect cell viability and membrane potential gradient. The research in vitro suggests that the newly constructed peptides could be a vector for tracking mesenchymal stem cells.