T2-PROPELLER Compared to T2-FRFSE for Image Quality and Lesion Detection at Prostate MRI

[Evaluation of the Latest Motion Correction Techniques in Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) Imaging across Different Vendors]

PURPOSE

To evaluate the robustness of the latest periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technology from each vendor against head movements and to investigate their characteristics for effective clinical use.

METHODS

Using a phantom simulating the T2-weighted image of the human brain, images were acquired with devices from CANON MEDICAL SYSTEMS (Tochigi, Japan; hereinafter "Canon"), GE HealthCare (Chicago, IL, USA; hereinafter "GE"), Philips (Amsterdam, Netherlands), and Siemens Healthineers (Forchheim, Germany; hereinafter "SIEMENS"). The head motion patterns were divided into rotation angle dependency (single rotation and multiple rotations) and rotation frequency dependency and evaluated using structural similarity (SSIM).

RESULTS

For rotation angle dependency, Canon was robust against small rotation angles and fine movements. Despite the rotation angle, GE was robust against movements, with deep learning reconstruction (DLR) improving correction functionality. Philips could be used with compressed sensitivity encoding (CS), and robustness varied with blade width. SIEMENS was robust against large movements. For rotation frequency dependency, results were similar across the 4 vendors.

CONCLUSION

The rotation angle and rotation frequency dependencies of the PROPELLER technology from the 4 vendors were quantitatively evaluated. Understanding the characteristics of PROPELLER allows for the possibility of providing diagnostic-quality images even for patients who move during head MRI exams by appropriately using PROPELLER.

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Improving image quality using the pause function combination to PROPELLER sequence in brain MRI: a phantom study

While some MRI systems offer a "pause" function, combining it with the PROPELLER method for image quality improvement remains underexplored. This study investigated whether repositioning the head after pausing during PROPELLER imaging enhances image quality. All brain phantom images in this study were obtained using a 3.0 T MRI and acquired using the fast spin-echo T2WI-based PROPELLER with motion correction. By combining the angle of rotational motion of the head phantom and the number of repositioning after a pause, two studies including seven trials were performed. Increasing the rotation angle decreased the image quality; however, pausing the image and repositioning the head phantom to the original angle improved the image quality. A similar result was obtained by repositioning the angle closer to its original angle. Experiments with multiple head movements showed that pausing the scan and repositioning the phantom with each movement improved image quality.

Update on Optimization of Prostate MR Imaging Technique and Image Quality

Prostate MR imaging quality has improved dramatically over recent times, driven by advances in hardware, software, and improved functional imaging techniques. MRI now plays a key role in prostate cancer diagnostic work-up, but outcomes of the MRI-directed pathway are heavily dependent on image quality and optimization. MR sequences can be affected by patient-related degradations relating to motion and susceptibility artifacts which may enable only partial mitigation. In this Review, we explore issues relating to prostate MRI acquisition and interpretation, mitigation strategies at a patient and scanner level, PI-QUAL reporting, and future directions in image quality, including artificial intelligence solutions.

Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality

PURPOSE

To evaluate the impact of a commercially available deep learning-based reconstruction (DLR) algorithm with varying combinations of DLR noise reduction settings and imaging parameters on quantitative and qualitative image quality, PI-RADS classification and examination time in prostate T2-weighted (T2WI) and diffusion-weighted (DWI) imaging.

METHOD

Forty patients were included. Standard-of-care (SoC) prostate MRI sequences including T2WI and DWI were reconstructed without and with different DLR de-noising levels (low, medium, high). In addition, faster T2WI(Fast) and DWI(Fast) sequences, and a higher resolution T2WI(HR) sequence were evaluated. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values. Two radiologists performed qualitative analysis, independently evaluating imaging datasets using 5-point scoring scales for image quality and artifacts. PI-RADS category assignment was also performed by the more experienced radiologist.

RESULTS

All DLR levels resulted in significantly higher SNR and CNR compared to the DLR(off) acquisitions. DLR allowed the acquisition time to be reduced by 33% for T2WI(Fast) and 49% for DWI(Fast) compared to SoC, without affecting image quality, whilst T2WI(HR) with DLR allowed for a 73% increase in spatial resolution in the phase encode direction compared to SoC. The inter-reader agreement for image quality and artifact scores was substantial for all subjective measurements on T2WI and DWI. The T2WI(Fast) protocol with DLR(medium) and DWI(Fast) with DLR(low) received the highest qualitative quality score.

CONCLUSION

DLR can reduce T2WI and DWI acquisition time and increase SNR and CNR without compromising image quality or altering PI-RADS classification.

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4–1.0. The L1TV-LAD with a regularization strength of λ= 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.