Detection of prostate cancer local recurrence following radical prostatectomy: assessment using a continuously acquired radial golden-angle compressed sensing acquisition

"The Pituitary within GRASP" - Golden-Angle Radial Sparse Parallel Dynamic MRI Technique and Applications to the Pituitary Gland

MRI is the preferred radiologic modality for evaluating the pituitary gland. An important component of pituitary MRI examinations is dynamic contrast-enhanced MRI. Compared to conventional dynamic techniques, golden-angle radial sparse parallel (GRASP) imaging offers multiple advantages, including the ability to achieve higher spatial and temporal resolution. In this narrative review, we discuss dynamic imaging of the pituitary gland, the technical fundamentals of GRASP, and applications of GRASP to the pituitary gland.

Respiratory Motion Mitigation and Repeatability of Two Diffusion-Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer

Respiratory motion and increased susceptibility effects at high magnetic fields pose challenges for quantitative diffusion-weighted MRI (DWI) of a mouse abdomen on preclinical MRI systems. We demonstrate the first application of radial k-space-sampled (RAD) DWI of a mouse abdomen using a genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC) on a 4.7 T preclinical scanner equipped with moderate gradient capability. RAD DWI was compared with the echo-planar imaging (EPI)-based DWI method with similar voxel volumes and acquisition times over a wide range of b-values (0.64, 535, 1071, 1478, and 2141 mm²/s). The repeatability metrics are assessed in a rigorous test-retest study (n = 10 for each DWI protocol). The four-shot EPI DWI protocol leads to higher signal-to-noise ratio (SNR) in diffusion-weighted images with persisting ghosting artifacts, whereas the RAD DWI protocol produces relatively artifact-free images over all b-values examined. Despite different degrees of motion mitigation, both RAD DWI and EPI DWI allow parametric maps of apparent diffusion coefficients (ADC) to be produced, and the ADC of the PDAC tumor estimated by the two methods are 1.3 ± 0.24 and 1.5 ± 0.28 × 10^-3 mm²/s, respectively (p = 0.075, n = 10), and those of a water phantom are 3.2 ± 0.29 and 2.8 ± 0.15 × 10^-3 mm²/s, respectively (p = 0.001, n = 10). Bland-Altman plots and probability density function reveal good repeatability for both protocols, whose repeatability metrics do not differ significantly. In conclusion, RAD DWI enables a more effective respiratory motion mitigation but lower SNR, while the performance of EPI DWI is expected to improve with more advanced gradient hardware.

Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores

Background

To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores.

Methods

We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ≤3+3 were considered as clinically insignificant (inPC) and Gleason scores ≥3+4 as sPC and were encoded in a binary fashion, serving as ground-truth. MRI was performed at 3T with high spatiotemporal resolution DCE using Golden-angle RAdial SParse (GRASP) MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2-signal intensities (SI) were determined in all lesions and served as input parameters for four supervised ML models: Gradient Boosting Machines (GBM), Neural Networks (NNet), Random Forest (RF) and Support Vector Machines (SVM). ML results and PI-RADS scores were compared with the ground-truth. Next ROC-curves and AUC values were calculated.

Results

All ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC (RF, GBM, NNet and SVM vs. PI-RADS: AUC 0.899, 0.864, 0.884 and 0.874 vs. 0.595, all P<0.001).

Conclusions

Using quantitative imaging parameters as input, supervised ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC. These results indicate that quantitative imagining parameters contain relevant information for the prediction of sPC from image features.

Dynamic contrast-enhanced MR imaging of rectal cancer using a golden-angle radial stack-of-stars VIBE sequence: comparison with conventional contrast-enhanced 3D VIBE sequence

PURPOSE

To compare conventional 3D volumetric-interpolated breath-hold examination (C-VIBE) sequence image quality to that of golden-angle radial stack-of stars acquisition scheme (R-VIBE) in rectal cancer patients.

METHODS

Seventy-eight patients had undergone pre-contrast C-VIBE, followed by DCE-MRI with R-VIBE and post-contrast C-VIBE in the visualization of rectal cancer. The first phase and the last phase of R-VIBE sequence were compared with pre-contrast and post-contrast C-VIBE sequences, respectively. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of rectal neoplasms, gluteus maximus, and subcutaneous fat were compared between the two different sequences. A further qualitative score system (graded 1-5) was used to evaluate the overall image. Quantitative and qualitative parameters from the two sequences were compared.

RESULTS

In all patients, R-VIBE achieved the same SNR and CNR ratings in pre- and post-contrast (all P > 0.05), with the exception of a higher SNR of fat in pre-contrast images (P = 0.037). In addition, there were no significant differences in scores of overall image quality, lesion conspicuity, and rectal wall boundary (all P > 0.05). There was an improved score in artifacts of post-contrast R-VIBE sequence (P = 0.005).

CONCLUSION

R-VIBE sequence can provide comparable image quality and less motion artifacts to that of C-VIBE sequence and is feasible for imaging of rectal cancer.

View-Sharing Artifact Reduction With Retrospective Compressed Sensing Reconstruction in the Context of Contrast-Enhanced Liver MRI for Hepatocellular Carcinoma (HCC) Screening

BACKGROUND

View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution.

PURPOSE

To evaluate a CS reconstruction algorithm on undersampled DCE-MRI data for image quality and hepatocellular carcinoma (HCC) detection.

STUDY TYPE

Retrospective.

SUBJECTS

Fifty consecutive patients undergoing MRI for HCC screening (29 males, 21 females, 52-72 years).

FIELD STRENGTH/SEQUENCE

3.0T MRI. Multiphase 3D-SPGR T₁ -weighted sequence undersampled in arterial phases with a complementary Poisson disc sampling pattern reconstructed with VS and CS algorithms.

ASSESSMENT

VS and CS reconstructions evaluated by blinded assessments of image quality and anatomic delineation on Likert scales (1-4 and 1-5, respectively), and HCC detection by OPTN/UNOS criteria including a diagnostic confidence score (1-5). Blinded side-by-side reconstruction comparisons for lesion depiction and overall series preference (-3-3).

STATISTICAL ANALYSIS

Two-tailed Wilcoxon signed rank tests for paired nonparametric analyses with Bonferroni-Holm multiple-comparison corrections. McNemar's test for differences in lesion detection frequency and transplantation eligibility.

RESULTS

CS compared with VS demonstrated significantly improved contrast (mean 3.6 vs. 2.9, P < 0.0001) and less motion artifact (mean 3.6 vs. 3.2, P = 0.006). CS compared with VS demonstrated significantly improved delineations of liver margin (mean 4.5 vs. 3.8, P = 0.0002), portal veins (mean 4.5 vs. 3.7, P < 0.0001), and hepatic veins (mean 4.6 vs. 3.5, P < 0.0001), but significantly decreased delineation of hepatic arteries (mean 3.2 vs. 3.7, P = 0.004). No significant differences were seen in the other assessments.

DATA CONCLUSION

Applying a CS reconstruction to data acquired for a VS reconstruction significantly reduces motion artifacts in a clinical DCE protocol for HCC screening.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:984-993.