Reference Values of Native T1 at 3T Cardiac Magnetic Resonance—Standardization Considerations between Different Vendors

This study aimed at establishing native T1 reference values for a Canon Vantage Galan 3T system and comparing them with previously published values from different vendors. A total of 20 healthy volunteers (55% Women; 33.9 ± 11.1 years) underwent left ventricular T1 mapping at 3T MR. A MOLLI 5(3)3 sequence was used, acquiring three short-axis slices. Native T1 values are shown as means (±standard deviation) and Student’s independent samples t-test was used to test gender differences in T1 values. Pearson’s correlation coefficient analysis was used to compare two processes of T1 analysis. The results show a global native T1 mean value of 1124.9 ± 55.2 ms (exponential analysis), that of women being statistically higher than men (1163 ± 30.5 vs. 1077.9 ± 39.5 ms, respectively; p < 0.001). There were no specific tendencies for T1 times in different ventricular slices. We found a strong correlation (0.977, p < 0.001) with T1 times derived from parametric maps (1136.4 ± 60.2 ms). Native T1 reference values for a Canon 3T scanner were provided, and they are on par with those already reported from other vendors for a similar sequence. We also found a correlation between native T1 and gender, with higher values for women.

68Ga-P15-041, A Novel Bone Imaging Agent for Diagnosis of Bone Metastases

Objectives68Ga-P15-041 (68Ga-HBED-CC-BP) is a novel bone-seeking PET radiotracer, which can be readily prepared by using a simple kit formulation and an in-house 68Ga/68Ge generator. The aim of this study is to assess the potential human application of 68Ga-P15-041 for clinical PET/CT imaging and to compare its efficacy to detect bone metastases of different cancers with 99mTc-MDP whole-body bone scintigraphy (WBBS).MethodsInitial kinetic study using Patlak analysis and parametric maps were performed in five histopathologically proven cancer patients (three males, two females) using 68Ga-P15-041 PET/CT scan only. Another group of 51 histopathologically proven cancer patients (22 males, 29 females) underwent both 99mTc-MDP WBBS and 68Ga-P15-041 PET/CT scans within a week, sequentially. Using either pathology examination or follow-up CT or MRI scans as the gold standard, the diagnostic efficacy and receiver operating characteristic curve (ROC) of the two methods in identifying bone metastases were compared (p &lt;0.05, statistically significant).ResultsFifty-one patients were imaged, and 174 bone metastatic sites were identified. 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS detected 162 and 81 metastases, respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 68Ga-P15-041 PET/CT and 99mTc-MDP WBBS were 93.1% vs 81.8%, 89.8% vs 90.7%, 77.5% vs 69.2%, 97.2% vs 93.4% and 90.7% vs 88.4%, respectively. Our results showed that the mean of SUVmax was significantly higher in metastases than that in benign lesions, 15.1 ± 6.9 vs. 5.6 ± 1.3 (P &lt;0.001). Using SUVmax = 7.6 as the cut-off value by PET/CT, it was possible to predict the occurrence of metastases (AUC = 0.976; P &lt;0.001; 95% CI: 0.946–0.999). However, it was impossible to distinguish osteoblastic bone metastases from osteolytic bone lesions. Parametric maps based on Patlak analysis provided excellent images and highly valuable quantitative information.Conclusions68Ga-P15-041 PET/CT, offering a rapid bone scan and high contrast images in minutes, is superior to the current method of choice in detecting bone metastases. It is reasonable to suggest that 68Ga-P15-041 PET/CT could become a valuable routine nuclear medicine procedure in providing excellent images for detecting bone metastases in cancer patients. 68Ga-P15-041 could become a valuable addition expanding the collection of 68Ga-based routine nuclear medicine procedures where 18F fluoride is not currently available.

Exploring arterial tissue microstructural organization using non-Gaussian diffusion magnetic resonance schemes

The purpose of this study was to characterize the alterations in microstructural organization of arterial tissue using higher-order diffusion magnetic resonance schemes. Three porcine carotid artery models namely; native, collagenase treated and decellularized, were used to estimate the contribution of collagen and smooth muscle cells (SMC) on diffusion signal attenuation using gaussian and non-gaussian schemes. The samples were imaged in a 7 T preclinical scanner. High spatial and angular resolution diffusion weighted images (DWIs) were acquired using two multi-shell (max b-value = 3000 s/mm2) acquisition protocols. The processed DWIs were fitted using monoexponential, stretched-exponential, kurtosis and bi-exponential schemes. Directionally variant and invariant microstructural parametric maps of the three artery models were obtained from the diffusion schemes. The parametric maps were used to assess the sensitivity of each diffusion scheme to collagen and SMC composition in arterial microstructural environment. The inter-model comparison showed significant differences across the considered models. The bi-exponential scheme based slow diffusion compartment (Ds) was highest in the absence of collagen, compared to native and decellularized microenvironments. In intra-model comparison, kurtosis along the radial direction was the highest. Overall, the results of this study demonstrate the efficacy of higher order dMRI schemes in mapping constituent specific alterations in arterial microstructure.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning

BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and MethodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

Short-duration dynamic [18F]DCFPyL PET and CT perfusion imaging to localize dominant intraprostatic lesions in prostate cancer: validation against digital histopathology and comparison to [18F]DCFPyL PET/MR at 120 minutes

Purpose Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. Methods Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. Results [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of < 10%. Compared to digital histopathology, the detected DILs had a mean dice similarity coefficient of 0.8 for the Ki and k4 model and 0.7 for SUVLate. Conclusions We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. Clinical trial registration number: NCT04009174 (ClinicalTrials.gov).

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.

Investigating the Discrimination Ability of 3D Convolutional Neural Networks Applied to Altered Brain MRI Parametric Maps

Convolutional neural networks are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. In the present study, we tested the ability of 3D convolutional neural networks to discriminate between whole-brain parametric maps obtained from diffusion-weighted magnetic resonance imaging. Original parametric maps were subjected to intensity-based region-specific alterations, to create altered maps. To analyze how position, size and intensity of altered regions affected the networks’ learning process, we generated monoregion and biregion maps by systematically modifying the size and intensity of one or two brain regions in each image. We assessed network performance over a range of intensity increases and combinations of maps, carrying out 10-fold cross-validation and using a hold-out set for testing. We then tested the networks trained with monoregion images on the corresponding biregion images and vice versa. Results showed an inversely proportional link between size and intensity for the monoregion networks, in that the larger the region, the smaller the increase in intensity needed to achieve good performances. Accuracy was better for biregion networks than for their monoregion counterparts, showing that altering more than one region in the brain can improve discrimination. Monoregion networks correctly detected their target region in biregion maps, whereas biregion networks could only detect one of the two target regions at most. Biregion networks therefore learned a more complex pattern that was absent from the monoregion images. This deep learning approach could be tailored to explore the behavior of other convolutional neural networks for other regions of interest. <br>