Gregory S. Karczmar

K-means clustering–based quantitative analysis of ultrafast dynamic contrast-enhanced MRI kinetics enabled segmentation-free ductal carcinoma in situ (DCIS) grade classification; contrast agent uptake rate was associated with DCIS grades and performed best for identifying low-grade DCIS.

Background: There is increasing interest in high temporal resolution imaging techniques in breast magnetic resonance imaging (MRI) for breast cancer detection. The purpose of this study was to compare diagnostic performance and kinetic characteristics of gadoterate meglumine (Dotarem^®) and gadobutrol (Gadavist^®) in ultrafast breast dynamic contrast-enhanced MRI (DCE-MRI), focusing on semi-quantitative and pharmacokinetic parameters for differentiating malignant lesions from background parenchymal enhancement (BPE). Methods: Ultrafast DCE-MRI acquisition is a high temporal resolution (3.5–4.6 seconds) imaging technique used during the first minute following contrast injection to obtain three-dimensional (3D) whole breast images. All ultrafast images were motion-corrected by a non-rigid registration method. This retrospective study evaluated 60 lesions enhanced with Dotarem and 34 with Gadavist. Semi-automatic volumetric segmentation of the lesions was performed with 3D Slicer. Signal enhancement ratio over time was fitted with an empirical mathematic model (EMM) by using each voxel within the lesion and selected normal parenchymal regions of interest (ROIs). Quantitative pharmacokinetic parameters, including K^trans (volume transfer constant) and Ve [extravascular extracellular space (EES) fractional volume], are calculated with the Tofts model. The individual arterial input function (AIF) was traced over an ROI within the descending thoracic aorta that was utilized to calibrate the time course of average signal intensity. Diagnostic performance was measured by sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) at Youden-optimized thresholds. Inter-agent comparisons included Wilcoxon rank-sum testing and Cohen’s d effect sizes. Results: A total of 94 histopathologically confirmed breast lesions were analyzed, comprising 60 Dotarem-enhanced and 34 Gadavist-enhanced lesions. Gadavist demonstrated a steeper early wash-in slope (≈60% vs. 40% signal increase at 20 seconds) and higher absolute tumor enhancement. Dotarem exhibited greater tumor-to-BPE contrast due to lower BPE, improving lesion conspicuity. Amplitude-based kinetic parameters [upper limit of enhancement (A), uptake rate (α), maximal enhancement slope (A*α), area under the enhancement-time curve for the first 30 seconds (AUC30)] most effectively distinguished malignant lesions from BPE (Ptrans (all Ptrans (P vs. 0.58; P=0.036); however, the clinical significance of this difference remains uncertain. Conclusions: In ultrafast breast DCE-MRI, Gadavist demonstrated stronger early enhancement kinetics, whereas Dotarem provided improved tumor-to-BPE contrast and higher sensitivity for select kinetic parameters. These findings underscore the importance of tailoring contrast agent selection to specific diagnostic goals.

Background: There is increasing interest in high temporal resolution imaging techniques in breast magnetic resonance imaging (MRI) for breast cancer detection. The purpose of this study was to evaluate the clinical application of gadoterate meglumine (Dotarem) in abbreviated dynamic contrast-enhanced MRI (DCE-MRI) of the breast for cancer detection, focusing on quantitative kinetic analysis with ultrafast DCE-MRI. Methods: Ultrafast acquisition is a high temporal resolution (3.5–4.6 seconds) imaging technique used during the first minute following contrast injection to obtain three-dimensional (3D) whole breast images. All ultrafast DCE-MRIs were motion-corrected by a non-rigid registration method. A retrospective study was performed for 49 patients with 60 malignant lesions and 2 benign lesions. Background parenchymal enhancement (BPE) was evaluated in 47 of the patients because two patients had breast implants. Semi-automatic volumetric segmentation of the lesions was performed with 3D Slicer. Signal enhancement ratio (SER) over time was fitted with an empirical mathematical model (EMM) by using each voxel within the lesion and selected normal parenchymal regions of interest (ROIs). Quantitative pharmacokinetic parameters, including transfer constant (K^trans) and fractional volume of the extravascular extracellular space (v_e), are calculated with the Tofts model. The individual arterial input function (AIF) was traced over an ROI within the descending thoracic aorta that was utilized to calibrate the time course of average signal intensity. Mean values of kinetic parameters for malignant lesions and BPE were compared with Welch’s t-test for normally distributed variables and the Mann-Whitney U test for non-normally distributed variables. The average ratio of malignant lesions to BPE was computed. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity at the optimal cutoff point as determined by Youden’s index and 95% confidence interval (CI) were calculated for each parameter to separate the malignant lesions from the BPE. Results: Higher signal enhancement in malignant lesions than signal in BPE was observed in the time course of ultrafast DCE-MRI. The ratio of K^trans for malignant lesions to BPE is 15.58±14.35 [standard deviation (SD)]. The maximum enhancement slope (A*α), area under the enhancement-time curve over 30 seconds (AUC30), upper limit of initial contrast agent uptake (A), and K^trans demonstrated excellent discriminating performance for cancer detection, with corresponding AUCs of 0.963, 0.963, 0.955, and 0.938, respectively, listed in descending order of discriminative power. K^trans had the highest diagnostic sensitivity [98.3% (95% CI: 91.1–100%), PConclusions: Based on quantitative pharmacokinetic analysis of ultrafast DCE-MRI, the study demonstrated the viability of utilizing Dotarem as an intravenous gadolinium-based contrast agent for DCE-MRI of the breast with preserved clinical success for breast cancer detection.

The Physics-Informed Autoencoder model provided accurate and explainable measurements of prostate tissue microstructure using hybrid multidimensional MRI and was more robust to image noise compared with the conventional least-squares algorithm.

This study demonstrates that hybrid multidimensional MRI has the potential to improve MRI/US fusion biopsy results, provide more reliable detection of prostate cancer, and add complementary information to Prostate Imaging and Reporting Data System–based evaluation by expert radiologists.

Purpose: This study investigates whether quantitative MRI and histology of the prostate reveal differences between races, specifically African Americans (AAs) and Caucasian Americans (CAs), that can affect diagnosis. Materials and Methods: Patients (98 CAs, 47 AAs) with known or suspected prostate cancer (PCa) underwent 3T MRI (T2W, DWI, and DCE-MRI) prior to biopsy or prostatectomy. Quantitative mpMRI metrics: ADC, T2, and DCE empirical mathematical model parameters were calculated. Results: AAs had a greater percentage of higher Gleason-grade lesions compared to CAs. There were no significant differences in the quantitative ADC and T2 values between AAs and CAs. The cancer signal enhancement rate (α) on DCE-MRI was significantly higher for AAs compared to CAs (AAs: 13.3 ± 9.3 vs. CAs: 6.1 ± 4.7 s⁻¹, p < 0.001). The DCE signal washout rate (β) was significantly lower in benign tissue of AAs (AAs: 0.01 ± 0.09 s⁻¹ vs. CAs: 0.07 ± 0.07 s⁻¹, p < 0.001) and significantly elevated in cancer tissue in AAs (AAs: 0.12 ± 0.07 s⁻¹ vs. CAs: 0.07 ± 0.08 s⁻¹, p = 0.02). DCE significantly improves the differentiation of PCa from benign in AAs (α: 52%, β: 62% more effective in AAs compared to CAs). Histologic analysis showed cancers have a greater proportion (p = 0.04) of epithelium (50.9 ± 12.3 vs. 44.7 ± 12.8%) and lower lumen (10.5 ± 6.9 vs. 16.2 ± 6.8%) in CAs compared to AAs. Conclusions: This study shows that AAs have different quantitative DCE-MRI values for benign prostate and prostate cancer and different histologic makeup in PCa compared to CAs. Quantitative DCE-MRI can significantly improve the performance of MRI for PCa diagnosis in African Americans but is much less effective for Caucasian Americans.

This study investigated whether quantitative MRI and histology of the prostate reveals differences between races that can affect diagnosis. Cancer signal enhancement rate (α) on DCE-MRI was significantly higher for African Americans (AA) compared to Caucasian Americans (CA). Signal washout rate (β) was significantly lower in benign tissue in AAs and significantly elevated in cancers in AAs. However, no significant differences were found for ADC and T2. ROC analysis showed that ADC and T2 are slightly less effective in AA compared to CA. DCE significantly improves the differentiation of PCa from benign in AAs (α: 52%, β: 62% more effective in AA compared to CA). Histologic analysis showed cancers have a greater proportion of epithelium and lower lumen in AA compared to CA. This study shows that the different races have different quantitative MRI values and histologic makeup. Quantitative DCE-MRI is highly effective and improves PCa diagnosis in African Americans.

PurposeValidation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).ApproachA digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 μm/60 s) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 μm/1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.ResultsThe observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.ConclusionAn in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

Purpose: This study addresses the challenge of low resolution and signal‐to‐noise ratio (SNR) in diffusion‐weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b‐values through multiple acquisitions, but this results in diminished image resolution due to motion‐induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.Methods: We introduce a novel approach employing a “Perturbation Network” to learn subvoxel‐size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low‐resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher‐resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion‐correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.Results: Quantitative analyses revealed significantly higher structural similarity measures of super‐resolution images to ground truth high‐resolution images compared to high‐order interpolation (p 0.005). In blind qualitative experiments, of super‐resolution images were assessed to have superior diagnostic quality compared to interpolated images.Conclusion: High‐resolution details in DWI can be obtained without the need for high‐resolution training data. One notable advantage of the proposed method is that it does not require a super‐resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

We investigated why some prostate cancers (PCas) are not identified on multiparametric MRI (mpMRI) by using ground truth reference from whole-mount prostatectomy specimens. A total of 61 patients with biopsy-confirmed PCa underwent 3T mpMRI followed by prostatectomy. Lesions visible on MRI prospectively or retrospectively identified after correlating with histology were considered “identified cancers” (ICs). Lesions that could not be identified on mpMRI were considered “unidentified cancers” (UCs). Pathologists marked the Gleason score, stage, size, and density of the cancer glands and performed quantitative histology to calculate the tissue composition. Out of 115 cancers, 19 were unidentified on MRI. The UCs were significantly smaller and had lower Gleason scores and clinical stage lesions compared with the ICs. The UCs had significantly (p < 0.05) higher ADC (1.34 ± 0.38 vs. 1.02 ± 0.30 μm²/ms) and T2 (117.0 ± 31.1 vs. 97.1 ± 25.1 ms) compared with the ICs. The density of the cancer glands was significantly (p = 0.04) lower in the UCs. The percentage of the Gleason 4 component in Gleason 3 + 4 lesions was nominally (p = 0.15) higher in the ICs (20 ± 12%) compared with the UCs (15 ± 8%). The UCs had a significantly lower epithelium (32.9 ± 21.5 vs. 47.6 ± 13.1%, p = 0.034) and higher lumen volume (20.4 ± 10.0 vs. 13.3 ± 4.1%, p = 0.021) compared with the ICs. Independent from size and Gleason score, the tissue composition differences, specifically, the higher lumen and lower epithelium in UCs, can explain why some of the prostate cancers cannot be identified on mpMRI.