Combining two or more imaging modalities to provide complementary information has become commonplace in clinical practice and in preclinical and basic biomedical research. By incorporating the structural information provided by computed tomography (CT) or magnetic resonance imaging (MRI), the ill poseness nature of bioluminescence tomography (BLT) can be reduced significantly, thus improve the accuracies of reconstruction and in vivo quantification. In this paper, we present a small animal imaging system combining multi-view and multi-spectral BLT with MRI. The independent MRI-compatible optical device is placed at the end of the clinical MRI scanner. The small animal is transferred between the light tight chamber of the optical device and the animal coil of MRI via a guide rail during the experiment. After the optical imaging and MRI scanning procedures are finished, the optical images are mapped onto the MRI surface by interactive registration between boundary of optical images and silhouette of MRI. Then, incorporating the MRI structural information, a heterogeneous reconstruction algorithm based on finite element method (FEM) with L 1 normalization is used to reconstruct the position, power and region of the light source. In order to validate the feasibility of the system, we conducted experiments of nude mice model implanted with artificial light source and quantitative analysis of tumor inoculation model with MDA-231-GFP-luc. Preliminary results suggest the feasibility and effectiveness of the prototype system.

BackgroundPreoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning.PurposeTo investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA.Study TypeRetrospective.PopulationOne hundred and fifty‐six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings.Field Strength/SequenceT1‐weighted imaging (T1WI), contrast‐enhanced T1WI (T1CE), and T2‐weighted imaging (T2WI) using spin‐echo sequences with a 3.0‐T scanner.AssessmentAn automated three‐dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top‐discriminative features were identified using the minimum‐redundancy maximum‐relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency.Statistical TestsMann–Whitney U‐test and Chi‐square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P‐value<0.05 was considered statistically significant.ResultsEleven mpMRI‐based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87–0.92), ACC of 0.87 (CI: 0.84–0.89), SEN of 0.83 (CI: 0.81–0.85), and SPE of 0.87 (CI: 0.85–0.99) in the test cohort.Data ConclusionRadiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency.Level of Evidence3Technical EfficacyStage 2

BackgroundThe aim of this study was to evaluate the early anti-tumor efficiency of different therapeutic agents with a combination of multi-b-value DWI, DCE-MRI and texture analysis.MethodsEighteen 4 T1 homograft tumor models were divided into control, paclitaxel monotherapy and paclitaxel and bevacizumab combination therapy groups (n = 6) that underwent multi-b-value DWI, DCE-MRI and texture analysis before and 15 days after treatment.ResultsAfter treatment, the tumors in the control group were significantly larger than those in the combination group (P = 0.018). In multi-b-value DWI, the ADCslow obviously increased in the combination group compared to that in the others (P < 0.01). The f increased in the control and paclitaxel groups, but the combination group showed a significant decrease versus the others (P < 0.02). Additionally, in DCE-MRI, the decreasing Ktrans showed an evident difference between the combination and control groups (P = 0.003) due to the latter’s increasing Ktrans. The intra-group comparisons of tumor texture in pre-, mid- and post-treatments showed that the entropy had all significantly increased in all groups (P < 0.01, SSF = 0–6), though the MPP, mean and SD increased only in the combination group (PMPP,mean,SD < 0.05, SSF = 4–6). Moreover, the inter-group comparisons revealed that the mean and MPP exhibited significant differences after treatment (Pmean,MPP < 0.05, SSF = 0–3).ConclusionAll these results suggest some strong correlations among DWI, DCE and texture analysis, which are beneficial for further study and clinical research.