Coefficient Maps Research Articles

Leptomeningeal Collateral Status by Signal Variance in Perfusion Magnetic Resonance Imaging: Association With Initial Stroke Severity and Early Functional Outcome After Thrombectomy

BackgroundCollaterals are the main determinants of the severity of cerebral ischemia and control the pace of the ischemic tissue damage in acute ischemic stroke. Assessment of collateral status remains a major challenge in stroke imaging. We evaluated a signal variance–based collateral vessel index in perfusion‐weighted imaging (CVIPWI) in terms of its association with initial stroke severity, presence of a mismatch for endovascular thrombectomy (EVT), and early functional outcome in patients with large‐vessel occlusion.MethodsT2*‐weighted time series from dynamic susceptibility contrast perfusion imaging were processed to calculate the CVIPWI. Ischemic cores were segmented automatically on apparent diffusion coefficient maps. The relationship between collateral status and the fulfilment of mismatch criteria for EVT as well as the association between the CVIPWIand functional outcome in patients undergoing EVT were analyzed. Furthermore, spatial patterns of pial collateralization were investigated.ResultsA total of 156 patients with large‐vessel occlusion were included in the final analysis. Higher CVIPWIand thus better collateral supply was associated with lower baseline National Institutes of Health Stroke Scale and smaller baseline infarct volumes (P=0.022 andP=0.002, respectively), and the CVIPWIvaried significantly among groups according to fulfillment of mismatch criteria for EVT (P<0.001). In patients undergoing EVT (n=105), the CVIPWIwas an independent predictor of favorable functional outcome (modified Rankin scale score of 0–2) at discharge in multivariate analysis (P=0.031). In patients with EVT who had successful reperfusion (n=79), good collateral status was associated with a higher rate of early neurological improvement (P=0.026) and better functional outcome at discharge (P=0.04) in shift analysis.ConclusionSignal variance–based CVIPWIrepresents a semiquantitative and objective, thus observer‐independent parameter for direct assessment of collateral status with clinical relevance. Its use may inform clinical decision‐making and may be of interest for clinical stroke trials.

Whole body MRI with DWI in people with NF1 and Schwannomatosis: Are qualitative and quantitative imaging features of peripheral lesions comparable to localized MRI?

PurposeTo compare the qualitative and quantitative features of peripheral lesions on localized (L) and whole-body (WB) magnetic resonance imaging (MRI) in people with neurofibromatosis type 1 (NF1) and schwannomatosis. Materials and methodsThis is a retrospective, HIPAA compliant study with twenty-seven patients (14 women, 13 men; mean age (years): 38 (3–67)) who underwent both L-MRI and WB-MRI without interval treatment. WB-MRI and L-MRI were comprised of T1-weighted, fat suppressed (FS) T2-weighted or short tau inversion recovery (STIR), diffusion-weighted imaging (DWI) using b-values of 50, 400, and 800 s/mm2, apparent diffusion coefficient (ADC) mapping and pre- and post-contrast FST1 sequences. Two readers recorded qualitative (T1 and T2/STIR signal intensity and heterogeneity, contrast enhancement and heterogeneity, perilesional enhancement, presence of a target sign and perilesional edema) and quantitative (size, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), ADC) features of peripheral lesions on L-MRI and WB-MRI.Descriptive statistics, Wilcoxon signed-rank test and McNemar’s test were used. ResultsThere were 31 peripheral lesions identified in 27 subjects, (mean size: 3.1 cm (range: 1–8.1 cm) on both L-MRI and WB-MRI).There were no differences in T1 signal and heterogeneity and T2/STIR signal and heterogeneity between WB-MRI and L-MRI ((p = 0.180, 0.083, 0.317 and 0.157 respectively). There were also no differences in contrast enhancement, heterogeneity and perilesional enhancement between WB-MRI and L-MRI (p = 1.000, 0.380 and 1.000 respectively). Presence of a target sign and perilesional edema did not differ between WB-MRI and L-MRI (p = 1.000 and 0.500 respectively). Craniocaudal (CC), mediolateral (ML) and anteroposterior (AP) size measurements on WB-MRI did not differ from CC, ML and AP size measurements on L-MRI (p = 0.597, 0.128 and 0.783 respectively). SNR on WB-DWI did not differ from SNR on L-DWI for b50, b400 and b800 images (p = 0.285, 0.166, and 0.974 respectively), and CNR on WB-DWI did not differ from CNR on L-DWI for b50, b400 and b800 images (p = 0.600, 0.124, and 0.787 respectively). There was no significant difference in minimum, mean and maximum ADC values between WB-DWI and L-DWI (p = 0.234, 0.481, and 0.441 respectively). Median minimum, mean and maximum ADC (×10(-3)mm(2)/s) differences between WB-DWI and L-DWI were 0.0 (range −1 to 0.7), 0.0 (range −0.5 to 0.6), and 0.1 (range −1.2 to 0.8) respectively. Relative ADC difference averages were 29.1% for minimum values, 10.1% for mean values, and 14.8% for maximum values. ConclusionWB-MRI yields qualitative and quantitative features for peripheral lesions, including DWI and ADC measurements, that are comparable to L-MRI scans. WB-DWI can be reliably used for the assessment of peripheral nerve sheath tumors, obviating the need for a repeat follow-up L-DWI acquisition.

Die multiparametrische MRT der Prostata: Anforderungen und Grundlagen der Befundung

Multiparametric MRI (mpMRI) is one of the primary diagnostic tools for detecting clinically relevant prostate cancer. It should be routinely used in addition to urological investigations owing to its higher diagnostic yield than systematic biopsies. However, combining targeted and systematic biopsies achieves the highest diagnostic rate. The Prostate Imaging Reporting and Data System (PI-RADS Version2.1) standardizes the acquisition and interpretation of mpMRI of the prostate. It consists of high-resolution T2- and diffusion-weighted images, the corresponding apparent diffusion coefficient (ADC) maps, and adynamic contrast-enhanced sequence. Reports describe the increasing likelihood of clinically significant prostate cancer with PI-RADS categories 1-5. The MRI sequence determining the PI-RADS category of alesion depends on its location within the prostate: in the transitional zone, the T2-weighted sequence and, in the peripheral zone, the diffusion-weighted sequence are the primary determinants. The diffusion-weighted and contrast-enhanced sequences provide secondary classification for the transitional and peripheral zones, respectively. This review summarizes and illustrates the diagnostic criteria defined in PI-RADS 2.1. In addition, evidence for mpMRI of the prostate, its indication and implementation are described.

Synchronization of Hyperchaotic Systems Based on Intermittent Control and its Application in Secure Communication

The synchronization of master-slave hyperchaotic systems is investigated by intermittent control and proved by the Lyapunov stable theory. Meanwhile, a new secure communication scheme is designed for the continuous and digital information. The encrypted information is transmitted to receiver through the intermittent controller, which reduces the disturbance to the synchronization of master-slave systems and improves the security and reliability of secure communication. Before transmitting to the receiver, the continuous signal is firstly modulated and masked by the chaotic signals. Furthermore, an encryption algorithm for the digital information of color image is proposed by the pseudo-random sequences of Chen hyperchaotic system, and then the encrypted image is modulated and masked by the variables of the master system. The original image can be decrypted successfully at the receiving end after the slave system is synchronized with the master system. Finally, the feasibility and effectiveness of this scheme are verified by simulation experiments. In addition, the security analysis of the image encryption algorithm is also discussed, such as key sensitivity, correlation coefficient, NIST test, and return map.

Association between the extent of diffusion restriction on brain diffusion-weighted imaging and neurological outcomes after an out-of-hospital cardiac arrest

BackgroundThis study evaluated the association between the extent of diffusion restriction on brain diffusion-weighted imaging (DWI) and neurological outcomes in patients who underwent targeted temperature management (TTM) after an out-of-hospital cardiac arrest (OHCA). MethodsPatients who underwent brain magnetic resonance imaging within 10 days of OHCA between 2012 and 2021 were analysed. The extent of diffusion restriction was described according to the modified DWI Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS). The 35 predefined brain regions were assigned a score if diffuse signal changes were concordantly present in DWI scans and apparent diffusion coefficient maps. The primary outcome was an unfavourable neurological outcome at 6 months. The sensitivity, specificity, and receiver operating characteristic (ROC) curves for the measured parameters were analysed. Cut-off values were determined to predict the primary outcome. The predictive cut-off DWI-ASPECTS was internally validated using five-fold cross-validation. ResultsOf the 301 patients, 108 (35.9%) had 6-month favourable neurological outcomes. Patients with unfavourable outcomes had higher whole-brain DWI-ASPECTS (median, 31 [26–33] vs. 0 [0–1], P < 0.001) than those with favourable outcomes. The area under the ROC curve (AUROC) of whole-brain DWI-ASPECTS was 0.957 (95% confidence interval [CI] 0.928–0.977). A cut-off value of ≥8 for unfavourable neurological outcomes had specificity and sensitivity of 100% (95% CI 96.6–100) and 89.6% (95% CI 84.4–93.6), respectively. The mean AUROC was 0.956. ConclusionMore extensive diffusion restriction on DWI-ASPECTS in patients with OHCA who underwent TTM was associated with 6-month unfavourable neurological outcomes.Running title: Diffusion restriction and neurological outcomes after cardiac arrest.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.

This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram. This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SI tumor ), normal liver (SI liver ), and background noise (SI background ) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated. The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level ( P = 0.010), age ( P = 0.015), and signal noise ratio ( P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level ( P = 0.011), age ( P = 0.019), and rad score ( P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively. Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

Osseous-Tissue Tumor Reporting and Data System With Diffusion-Weighted Imaging of Bone Tumors-An Interreader Analysis and Whether It Adds Incremental Value on Tumor Grading Over Conventional Magnetic Resonance Imaging.

The aim of the study is to determine whether the use of diffusion-weighted imaging (DWI) provides incremental increase in performance in the osseous-tissue tumor reporting and data system (OT-RADS) with the hypothesis that use of DWI improves interreader agreement and diagnostic accuracy. In this multireader cross-sectional validation study, multiple musculoskeletal radiologists reviewed osseous tumors with DW images and apparent diffusion coefficient maps. Four blinded readers categorized each lesion using the OT-RADS categorizations. Intraclass correlation (ICC) and Conger κ were used. Diagnostic performance measures including area under the receiver operating curve were reported. These measures were then compared with the previously published work that validated OT-RADS but did not include incremental value assessment of DWI. One hundred thirty-three osseous tumors of the upper and lower extremities (76 benign, 57 malignant) were tested. Interreader agreement for OT-RADS with DWI (ICC = 0.69) was slightly lower (not statistically different) from the previously published work that did not incorporate DWI (ICC = 0.78, P > 0.05). The mean sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating curve including DWI of the 4 readers were 0.80, 0.95, 0.96, 0.79, and 0.91, respectively. In the previously published work without DWI, the mean values of the readers were 0.96, 0.79, 0.78, 0.96, and 0.94, respectively. The addition of DWI to the OT-RADS system does not allow significantly improved area under the curve diagnostic performance measure. Conventional magnetic resonance imaging can be prudently used for OT-RADS for reliable and accurate characterization of bone tumors.

Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators

Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.

Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery.

To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED. 13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution. There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution. ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.

Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet.

To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on GoogLeNet was proposed. After installing mining pickups in key monitoring areas of coal mines to collect the sounds of the working equipment and the environment, the collected sound was analyzed by continuous wavelet to obtain its scale coefficient map. This was then imported into GoogLeNet to obtain the recognition model of coal mine gas and coal dust explosions. The test sound was obtained by continuous wavelet analysis to obtain the scale coefficient map, brought into the completed training recognition model to obtain the sound signal class, and verified by experiment. Firstly, the scale coefficient map extracted from the sound signal by continuous wavelet analysis showed that the similarity between the subjective and objective indicators of the wavelet coefficient maps of the gas explosion sound and coal dust explosion sound was higher, but the difference between these and the rest of the coal mine sounds was clearer, helping to effectively distinguish gas and coal dust explosion sounds from other sounds. Secondly, the experimental results of GoogLeNet parameters can be obtained. When the dropout parameter is 0.5 and the initial learning rate is 0.001, the recognition effect of the model established by GoogLeNet was optimal. According to the selected parameters, the training loss, testing loss, training recognition rate, and testing recognition rate of the model are all in line with expectations. Finally, the experimental recognition results show that the recognition rate of the proposed method is 97.38%, the recall rate is 86.1%, and the accuracy rate is 100% for the case of a 9:1 ratio of test data to training data, and the overall recognition effect of the proposed GoogLeNet is significantly better than that of vgg and Alexnet, which can effectively solve the problem of under-sampling of coal mine gas and coal dust explosion sounds and can meet the need for the intelligent recognition of coal mine gas and dust explosions.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS). In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS. We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model. We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC. We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF.

High-throughput determination of the interdiffusion coefficients in fcc Cu–Ni–Al–Zn alloys

Nine solid state Cu–Ni–Al–Zn quaternary diffusion couples were prepared, from which the concentration distributions of Cu, Ni, Al and Zn at 1123, 1173, and 1223 K were determined by using the electron probe microanalyzer (EPMA) technique. Utilizing the measured concentration profiles, the mobilities in fcc Cu–Ni–Al–Zn system were obtained, from which the interdiffusion coefficients were determined in a high-throughput manner with numerical inverse method. It showed that the main interdiffusivities satisfy D˜ZnZnCu>D˜AlAlCu>D˜NiNiCu over the investigated composition and temperature ranges. The influences of individual components on the diffusion rates were evaluated based on the determined interdiffusivities. In addition, three-dimensional main interdiffusion coefficient maps were constructed. The effects of composition on the main diffusion coefficients were analyzed, showing that D˜AlAlCu and D˜ZnZnCu increase with the increasing Al and Zn additions but decrease with increasing Ni content, while D˜NiNiCu has a more complex trend as the concentrations of Al, Ni and Zn change. According to the obtained diffusion coefficients, ways to optimize the temperature and time during homogenization heat treatment of different series of Cu–Ni–Al–Zn alloys were determined.

Delineation of recurrent glioblastoma by whole brain spectroscopic magnetic resonance imaging

BackgroundGlioblastoma (GBM) cellularity correlates with whole brain spectroscopic MRI (sMRI) generated relative choline to N-Acetyl-Aspartate ratio (rChoNAA) mapping. In recurrent GBM (rGBM), tumor volume (TV) delineation is challenging and rChoNAA maps may assist with re-RT targeting.MethodsFourteen rGBM patients underwent sMRI in a prospective study. Whole brain sMRI was performed to generate rChoNAA maps. TVs were delineated by the union of rChoNAA ratio over 2 (rChoNAA > 2) on sMRI and T1PC. rChoNAA > 2 volumes were compared with multiparametric MRI sequences including T1PC, T2/FLAIR, diffusion-restriction on apparent diffusion coefficient (ADC) maps, and perfusion relative cerebral blood volume (rCBV).ResultsrChoNAA > 2 (mean 27.6 cc, range 6.6–79.1 cc) was different from other imaging modalities (P ≤ 0.05). Mean T1PC volumes were 10.7 cc (range 1.2–31.4 cc). The mean non-overlapping volume of rChoNAA > 2 and T1PC was 29.2 cm3. rChoNAA > 2 was 287% larger (range 23% smaller–873% larger) than T1PC. T2/FLAIR volumes (mean 111.7 cc, range 19.0–232.7 cc) were much larger than other modalities. rCBV volumes (mean 6.2 cc, range 0.2–19.1 cc) and ADC volumes were tiny (mean 0.8 cc, range 0–3.7 cc). Eight in-field failures were observed. Three patients failed outside T1PC but within rChoNAA > 2. No grade 3 toxicities attributable to re-RT were observed. Median progression-free and overall survival for re-RT patients were 6.5 and 7.1 months, respectively.ConclusionsTreatment of rGBM may be optimized by sMRI, and failure patterns suggest benefit for dose-escalation within sMRI-delineated volumes. Dose-escalation and radiologic-pathologic studies are underway to confirm the utility of sMRI in rGBM.

The Role of 3 Tesla Diffusion Weighted-Magnetic Resonance Imaging and Apparent Diffusion Coefficient Mapping with Aging and Gendering in Primary Parotid Tumors: Preoperative Foresight of Histopathological Subtypes

The Role of 3 Tesla Diffusion Weighted-Magnetic Resonance Imaging and Apparent Diffusion Coefficient Mapping with Aging and Gendering in Primary Parotid Tumors: Preoperative Foresight of Histopathological Subtypes

Texture analysis of diffusion kurtosis imaging for differentiating malignant from benign sinonasal lesions: added value to conventional imaging features.

To evaluate the performance of texture analysis (TA) of diffusion kurtosis imaging (DKI) in differentiating malignant from benign sinonasal lesions, and its added value to the conventional imaging features. Fifty-eight patients with malignant and 40 patients with benign sinonasal lesions were retrospectively enrolled. Conventional CT and MRI features were reviewed. Texture parameters were obtained and compared between two groups. Multivariate logistic regression analysis was used to identify the most valuable variables. Receiver operating characteristic curves were performed to assess the differentiating performance of independent variables and their combination. There were significant differences in tumor necrosis, bone erosion and soft tissue invasion between the two groups (all p < 0.05). There were significant differences in the 10th and entropy of Apparent diffusion coefficient map, the mean, 10th and entropy of D map, the mean and 90th of K map between the two groups (all p < 0.002). The bone erosion, entropy of D, and mean of K were independent variables associated with malignant tumors. Receiver operating characteristic analyses indicated that the combination of three features possessed better differentiating performance than bone erosion alone (p = 0.003). TA of DKI could supply incremental value to conventional imaging features for pre-operative differential diagnosis between benign and malignant sinonasal lesions. The present study is the first to combine conventional imaging features and the TA of DKI in the differential diagnosis between benign and malignant sinonasal lesions. Our findings suggest that TA of DKI could supply incremental value to conventional imaging features.

Surveillance Value of Apparent Diffusion Coefficient Maps: Multiparametric MRI in Active Surveillance of Prostate Cancer.

This study aims to establish the value of apparent diffusion coefficient maps and other magnetic resonance sequences for active surveillance of prostate cancer. The study included 530 men with an average age of 66, who were under surveillance for prostate cancer. We have used multiparametric magnetic resonance imaging with subsequent transperineal biopsy (TPB) to verify the imaging findings. We have observed a level of agreement of 67.30% between the apparent diffusion coefficient (ADC) maps, other magnetic resonance sequences, and the biopsy results. The sensitivity of the apparent diffusion coefficient is 97.14%, and the specificity is 37.50%. According to our data, apparent diffusion coefficient is the most accurate sequence, followed by diffusion imaging in prostate cancer detection. Based on our findings we advocate that the apparent diffusion coefficient should be included as an essential part of magnetic resonance scanning protocols for prostate cancer in at least bi-parametric settings. The best option will be apparent diffusion coefficient combined with diffusion imaging and T2 sequences. Further large-scale prospective controlled studies are required to define the precise role of multiparametric and bi-parametric magnetic resonance in the active surveillance of prostate cancer.

Diffusion-weighted Breast MRI at 3 Tesla: Improved Lesion Visibility and Image Quality with a Combination of Water-excitation and Spectral Fat Saturation

In breast MRI with diffusion-weighted imaging (DWI), fat suppression is essential for eliminating the dominant lipid signal. This investigation evaluates a combined water-excitation-spectral-fatsat method (WEXfs) versus standard spectral attenuated inversion recovery (SPAIR) in high-resolution 3-Tesla breast MRI. Multiparametric breast MRI with 2 echo-planar DWI sequences was performed in 83 patients (50.1 ± 12.6 years) employing either WEXfs or SPAIR for fat signal suppression. Three radiologists assessed overall DWI quality and delineability of 88 focal lesions (28 malignant, 60 benign) on images with b values of 800 and 1600 s/mm2, as well as apparent diffusion coefficient (ADC) maps. For each fat suppression method and b value, the longest lesion diameter was determined in addition to measuring the signal intensity in DWI and ADC value in standardized regions of interest. Regardless of b values, image quality (all p < 0.001) and lesion delineability (all p ≤ 0.003) with WEXfs-DWI were deemed superior compared to SPAIR-DWI in benign and malignant lesions. Irrespective of lesion characterization, WEXfs-DWI provided superior signal-to-noise, contrast-to-noise and signal-intensity ratios with 1600 s/mm2 (all p ≤ 0.05). The lesion size difference between contrast-enhanced T1 subtraction images and DWI was smaller for WEXfs compared to SPAIR fat suppression (all p ≤ 0.007). The mean ADC value in malignant lesions was lower for WEXfs-DWI (p < 0.001), while no significant ADC difference was ascertained between both techniques in benign lesions (p=0.947). WEXfs-DWI provides better subjective and objective image quality than standard SPAIR-DWI, resulting in a more accurate estimation of benign and malignant lesion size.

Multi-channel GAN-based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction.

Diffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging. This study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors. The performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model. The current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.

Concurrent immunotherapy and re-irradiation utilizing stereotactic body radiotherapy for recurrent high-grade gliomas.

Clinical trials evaluating immune checkpoint inhibition (ICI) in recurrent high-grade gliomas (rHGG) report 7%-20% 6-month progression-free survival (PFS), while re-irradiation demonstrates 28%-39% 6-month PFS. We evaluate outcomes of patients treated with ICI and concurrent re-irradiation utilizing stereotactic body radiotherapy/fractionated stereotactic radiosurgery (SBRT) compared to ICI monotherapy. Patients ≥18-years-old with rHGG (WHO grade III and IV) receiving ICI + SBRT or ICI monotherapy between January 1, 2016 and January 1, 2019 were included. Adverse events, 6-month PFS and overall survival (OS) were assessed. Log-rank tests were used to evaluate PFS and OS. Histogram analyses of apparent diffusion coefficient maps and dynamic contrast-enhanced magnetic resonance perfusion metrics were performed. Twenty-one patients with rHGG (ICI + SBRT: 16; ICI: 5) were included. The ICI + SBRT and ICI groups received a mean 7.25 and 6.2 ICI cycles, respectively. There were five grade 1, one grade 2 and no grade 3-5 AEs in the ICI + SBRT group, and four grade 1 and no grade 2-5 AEs in the ICI group. Median PFS was 2.85 and 1month for the ICI + SBRT and ICI groups; median OS was 7 and 6months among ICI + SBRT and ICI groups, respectively. There were significant differences in pre and posttreatment tumor volume in the cohort (12.35 vs. 20.51; p= .03), but not between treatment groups. In this heavily pretreated cohort, ICI with re-irradiation utilizing SBRT was well tolerated. Prospective studies are warranted to evaluate potential therapeutic benefits to re-irradiation with ICI + SBRT in rHGG.

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm.

Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases. To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value. Retrospective. A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n=480) and internal validation set (n=275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set. A 1.5T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI. The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence. Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant. The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes. Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas. Stage 2.