Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion.

PURPOSE: To evaluate and quantify inter-directional and inter-acquisition variation in diffusion-weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations. METHODS: Ten patients with biopsy-proven prostate cancer were studied under an Institutional Review Board-approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter-acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion-induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft-max function to the set of acquisitions per-voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high. RESULTS: Inter-acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of 98 . 8 % $$ 98.8\% $$ with a false positive rate of 3 . 9 % $$ 3.9\% $$ . CONCLUSION: Motion-induced signal loss makes conventional signal-averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem.

Right Ventricular Thrombus on Echocardiography.

Right ventricular thrombi (RVTs) have been almost exclusively studied in patients with pulmonary embolism (PE). The implications of an isolated RVT, a finding typically encountered on transthoracic echocardiography (TTE), are lacking. In this study, we sought to identify the echocardiographic and clinical features associated with the presence of RVTs. Between 1998 and 2023, 138 patients with RVT documented on TTE were retrospectively identified. Demographic data, presence of intracardiac devices, hypercoagulable conditions, history of deep vein thrombosis (DVT), PE, and/or left ventricular thrombus were abstracted from electronic chart review. Measurements of right and left ventricular size, and function were performed on TTE. Of the total population of patients with RVT, <1/2 had intracardiac devices, 29% had a documented hypercoagulable state (e.g., cancer or a clotting disorder). Most patients had dilated (77%) and dysfunctional (72%) right ventricles. Approximately 50% of RVTs were discovered in nonstandard imaging planes, suggesting that the presence of RVT is likely underestimated in clinical practice. Of those evaluated for PE, 80% had PE. Of those evaluated for DVT, 53% had DVT. In conclusion, further investigations are warranted to better guide when to investigate the right ventricle for RVTs on TTE and the impact of RVTs on patient outcomes.

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers.

BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography, current methods are prone to interobserver variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers. METHODS: We used 15,746 transthoracic echocardiography studies-including 25,529 apical videos-which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical transthoracic echocardiography reports. Within the test cohort, DL model accuracy was compared to 6 expert and 3 novice readers using F1 score evaluation, with the ground truth of RWMA defined by expert readers. Significance between the DL model and novices was assessed using the permutation test. RESULTS: Within the test cohort, the DL model accurately identified any RWMA with an area under the curve of 0.96 (0.92-0.98). The mean F1 scores of the experts and the DL model were numerically similar for 6 of 7 regions: anterior (86 vs 84), anterolateral (80 vs 74), inferolateral (83 vs 87), inferoseptal (86 vs 86), apical (88 vs 87), inferior (79 vs 81), and any RWMA (90 vs 94), respectively, while in the anteroseptal region, the F1 score of the DL model was lower than the experts (75 vs 89). Using F1 scores, the DL model outperformed both novices 1 (P = .002) and 2 (P = .02) for the detection of any RWMA. CONCLUSIONS: Deep learning provides accurate detection of RWMA, which was comparable to experts and outperformed a majority of novices. Deep learning may improve the efficiency of RWMA assessment and serve as a teaching tool for novices.

An Interactive App with Multi-parametric MRI - Whole-Mount Histology Correlation for Enhanced Prostate MRI Training of Radiology Residents.

RATIONALE AND OBJECTIVES: To validate the educational value of a newly created learning application in enhancing prostate MRI training of radiologists for detecting prostate cancer using an observer study. MATERIALS AND METHODS: An interactive learning app, LearnRadiology, was developed using a web-based framework to display multi-parametric prostate MRI images with whole-mount histology for 20 cases curated for unique pathology and teaching points. Twenty new prostate MRI cases, different from the ones used in the web app, were uploaded on 3D Slicer. Three radiologists (R1: radiologist; R2, R3: residents) blinded to pathology results were asked to mark areas suspected of cancer and provide a confidence score (1-5, with 5 being high confidence level). Then after a minimum memory washout period of 1 month, the same radiologists used the learning app and then repeated the same observer study. The diagnostic performance for detecting cancers before and after accessing the learning app was measured by correlating MRI with whole-mount pathology by an independent reviewer. RESULTS: The 20 subjects included in the observer study had 39 cancer lesions (13 Gleason 3 + 3, 17 Gleason 3 + 4, 7 Gleason 4 + 3, and 2 Gleason 4 + 5 lesions). The sensitivity (R1: 54% â†’ 64%, P = 0.08; R2: 44% â†’ 59%, P = 0.03; R3: 62% â†’ 72%, P = 0.04) and positive predictive value (R1: 68% â†’ 76%, P = 0.23; R2: 52% â†’ 79%, P = 0.01; R3: 48% â†’ 65%, P = 0.04) for all 3 radiologists improved after using the teaching app. The confidence score for true positive cancer lesion also improved significantly (R1: 4.0 ± 1.0 â†’ 4.3 ± 0.8; R2: 3.1 ± 0.8 â†’ 4.0 ± 1.1; R3: 2.8 ± 1.2 â†’ 4.1 ± 1.1; P < 0.05). CONCLUSION: The web-based and interactive LearnRadiology app learning resource can support medical student and postgraduate education by improving diagnostic performance of trainees for detecting prostate cancer.

Physically implausible signals as a quantitative quality assessment metric in prostate diffusion-weighted MR imaging.

PURPOSE: To provide a quantitative assessment of diffusion-weighted MR images of the prostate through identification of PIDS which clearly represents artifacts in the data. We calculated the percentage and distribution of PIDS in prostate DWI and compare the amount of PIDS between mpMRI images obtained with and without an endorectal coil. METHODS: This IRB approved retrospective study (from 03/03/2014 to 03/10/2020), included 40 patients scanned with endorectal coil (ERC) and 40 without ER coil (NERC). PIDS contains any voxel where: (1) the diffusion signal increases despite an increase in b-value; and/or (2) apparent diffusion coefficient (ADC) is more than 3.0 µm2/ms (the ADC of pure water at 37 °C and it is physically implausible for any material to have a higher ADC). PIDS for transition zone (TZ) and peripheral zone (PZ) was calculated using an in-house MATLAB program. DWI images were quantitatively inspected for noise, motion, and distortion. T-test was used to compare the difference between PIDS levels in ERC versus NERC and ANOVA to compare the PIDS levels in the anatomic zones. The images were evaluated by a fellowship-trained radiologist in Abdominal Imaging with more than 10 years of experience in reading prostate MRI. This was tested only in prostate in this study. RESULTS: 80 patients (58 ± 8 years old, 80 men) were evaluated. The percentage of voxels exhibiting PIDS was 17.1 ± 8.1% for the ERC cohort and 22.2 ± 15.5% for the NERC cohort. PIDS for NERC versus ERC were not significantly different (p = 0.14). The apex and base showed similar percentages of PIDS in ERC (p = 0.30) and NERC (p = 0.86). The mid (13.8 ± 8.6%) in ERC showed lower values (p = 0.02) of PIDS compared to apex (19.9 ± 11.1%) and base (17.5 ± 8.3%). CONCLUSION: PIDS maps provide a spatially resolved quantitative quality assessment for prostate DWI. Average PIDS over the entire prostate were similar for the ERC and NERC cohorts, and did not differ significantly across prostate zones. However, for many of the patients, PIDS was focally much higher in specific prostate zones. PIDS assessment can guide Radiologist's evaluation of images and the development of improved DWI sequences.

Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy.

RATIONALE AND OBJECTIVES: To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT. MATERIALS AND METHODS: Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy. RESULTS: Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05). CONCLUSION: Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor.

Single-cell metabolic imaging reveals a SLC2A3-dependent glycolytic burst in motile endothelial cells.

Single-cell motility is spatially heterogeneous and driven by metabolic energy. Directly linking cell motility to cell metabolism is technically challenging but biologically important. Here, we use single-cell metabolic imaging to measure glycolysis in individual endothelial cells with genetically encoded biosensors capable of deciphering metabolic heterogeneity at subcellular resolution. We show that cellular glycolysis fuels endothelial activation, migration and contraction and that sites of high lactate production colocalize with active cytoskeletal remodelling within an endothelial cell. Mechanistically, RhoA induces endothelial glycolysis for the phosphorylation of cofilin and myosin light chain in order to reorganize the cytoskeleton and thus control cell motility; RhoA activation triggers a glycolytic burst through the translocation of the glucose transporter SLC2A3/GLUT3 to fuel the cellular contractile machinery, as demonstrated across multiple endothelial cell types. Our data indicate that Rho-GTPase signalling coordinates energy metabolism with cytoskeleton remodelling to regulate endothelial cell motility.

Performance of T2 Maps in the Detection of Prostate Cancer.

RATIONALE AND OBJECTIVES: This study compares the performance of T2 maps in the detection of prostate cancer (PCa) in comparison to T2-weighted (T2W) magnetic resonance images. MATERIALS AND METHODS: The prospective study was institutional review board approved. Consenting patients (n = 45) with histologic confirmed PCa underwent preoperative 3-T magnetic resonance imaging with or without endorectal coil. Two radiologists, working independently, marked regions of interests (ROIs) on PCa lesions separately on T2W images and T2 maps. Each ROI was assigned a score of 1-5 based on the confidence in accurately detecting cancer, with 5 being the highest confidence. Subsequently, the histologically confirmed PCa lesions (n = 112) on whole-mount sections were matched with ROIs to calculate sensitivity, positive predictive value (PPV), and radiologist confidence score. Quantitative T2 values of PCa and benign tissue ROIs were measured. RESULTS: Sensitivity and confidence score for PCa detection were similar for T2W images (51%, 4.5 ± 0.8) and T2 maps (52%, 4.5 ± 0.6). However, PPV was significantly higher (P = .001) for T2 maps (88%) compared to T2W (72%) images. The use of endorectal coils nominally improved sensitivity (T2W: 55 vs 47%, T2 map: 54% vs 48%) compared to the use of no endorectal coils, but not the PPV and the confidence score. Quantitative T2 values for PCa (105 ± 28 milliseconds) were significantly (P = 9.3 × 10-14) lower than benign peripheral zone tissue (211 ± 71 milliseconds), with moderate significant correlation with Gleason score (ρ = -0.284). CONCLUSIONS: Our study shows that review of T2 maps by radiologists has similar sensitivity but higher PPV compared to T2W images. Additional quantitative information obtained from T2 maps is helpful in differentiating cancer from normal prostate tissue and determining its aggressiveness.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer.

RATIONALE AND OBJECTIVES: This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. MATERIALS AND METHODS: Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (ß), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. RESULTS: Cancer (α = 6.45 ± 4.71 s-1, ß = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, ß = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. CONCLUSION: Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging.

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Strong reflector-based beamforming in ultrasound medical imaging.

This paper investigates the use of sparse priors in creating original two-dimensional beamforming methods for ultrasound imaging. The proposed approaches detect the strong reflectors from the scanned medium based on the well known Bayesian Information Criteria used in statistical modeling. Moreover, they allow a parametric selection of the level of speckle in the final beamformed image. These methods are applied on simulated data and on recorded experimental data. Their performance is evaluated considering the standard image quality metrics: contrast ratio (CR), contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A comparison is made with the classical delay-and-sum and minimum variance beamforming methods to confirm the ability of the proposed methods to precisely detect the number and the position of the strong reflectors in a sparse medium and to accurately reduce the speckle and highly enhance the contrast in a non-sparse medium. We confirm that our methods improve the contrast of the final image for both simulated and experimental data. In all experiments, the proposed approaches tend to preserve the speckle, which can be of major interest in clinical examinations, as it can contain useful information. In sparse mediums we achieve a highly improvement in contrast compared with the classical methods.