Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Quality Comparison of 3 Tesla multiparametric MRI of the prostate using a flexible surface receiver coil versus conventional surface coil plus endorectal coil setup

PURPOSE

To subjectively and quantitatively compare the quality of 3 Tesla magnetic resonance imaging of the prostate acquired with a novel flexible surface coil (FSC) and with a conventional endorectal coil (ERC).

METHODS

Six radiologists independently reviewed 200 pairs of axial, high-resolution T2-weighted and diffusion-weighted image data sets, each containing one examination acquired with the FSC and one with the ERC, respectively. Readers selected their preferred examination from each pair and assessed every single examination using six quality criteria on 4-point scales. Signal-to-noise ratios were measured and compared.

RESULTS

Two readers preferred FSC acquisition (36.5-45%) over ERC acquisition (13.5-15%) for both sequences combined, and four readers preferred ERC acquisition (41-46%). Analysis of pooled responses for both sequences from all readers shows no significant preference for FSC or ERC. Analysis of the individual sequences revealed a pooled preference for the FSC in T2WI (38.7% vs 17.8%) and for the ERC in DWI (50.9% vs 19.6%). Patients' weight was the only weak predictor of a preference for the ERC acquisition (p = 0.04). SNR and CNR were significantly higher in the ERC acquisitions (p<0.001) except CNR differentiating tumor lesions from benign prostate (p=0.1).

CONCLUSION

Although readers have strong individual preferences, comparable subjective image quality can be obtained for prostate MRI with an ERC and the novel FSC. ERC imaging might be particularly valuable for sequences with inherently lower SNR as DWI and larger patients whereas the FSC is generally preferred in T2WI. FSC imaging generates a lower SNR than with an ERC.

Two-centre comparative experimental study of biparametric MRI at 3.0 T with and without endorectal coil using kiwifruit (Actinidia deliciosa) as a phantom for human prostate

BACKGROUND

Application of an endorectal coil (ERC) for 3.0-T prostate magnetic resonance imaging (MRI) is contentious. We hypothesised that a multicoil phased-array protocol provides T2-weighted images (T2WI) and diffusion-weighted images (DWI) with reduced field-of-view (DWI_reduced) and monoexponential apparent diffusion coefficient (ADC) maps that are technically equivalent with ERC or without ERC (noERC).

METHODS

Axial T2WI (repetition time [TR] 7500 ms, echo time [TE] 98-101 ms) and DWI_reduced (field-of-view 149-179 × 71-73 mm², TR/TE 4500-5500/61-74 ms, b values, 50/800 s/mm²) ERC and noERC images were obtained on identical clinical 3.0-T scanners at two centres and compared for signal-to-noise ratio (SNR) in anterior and posterior outer pericarp (OP) and peripheral placenta (PP) in five green Hayward kiwifruit (Actinidia deliciosa, European Union regulation 543/2011 class 2). Corroboration in 21 patients with benign prostate hyperplasia (negative biopsy, prostate imaging reporting and data system version 2 ≤ 2) involved identical MRI protocols: 10 at site 1, noERC, and 11 at site 2, with ERC. Two-tailed Student's t test was used.

RESULTS

With few exceptions, signal-to-noise ratio (SNR) was similar in kiwifruits and prostates for ERC and noERC. In T2WI, SNR was higher posteriorly in noERC MRI for peripheral zone (PZ) (p < 0.001). In DWI_reduced, SNR was higher posteriorly in ERC-OP (p = 0.013) and ERC-PZ (p = 0.026) for b = 50 s/mm²; noERC-OP (p = 0.044) and ERC-PZ (p = 0.001) for b = 800 s/mm²; and ERC-OP (p = 0.001), noERC-OP (p = 0.001), and ERC-PZ (p = 0.001) for ADC, respectively. Volumes of kiwifruits and prostates were similar (89.2 ± 11.2 versus 90.8 ± 48.5 cm³, p = 0.638-0.920).

CONCLUSIONS

Findings imply that multicoil phased-array 3.0-T prostate MRI with T2WI and DWI_reduced with ADC maps provides equivalent results with and without ERC.

A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.