Introduction

Diffusion weighted imaging (DWI) has emerged as a valuable tool for the assessment of prostate cancer, providing high sensitivity in lesion detection¹. However, prostate DWI inherently suffers from low signal-to-noise ratio (SNR). When using EPI sequences, partial Fourier (PF) sampling along the phase-encoding direction shortens the echo train and thereby the effective TE, resulting in increased signal.

Zero-filling (ZF) the missing k-space lines inevitably leads to blurring and Gibbs ringing in the reconstructed image. Commonly used techniques for PF reconstruction, such as Projection onto Convex Sets² (POCS) and homodyne reconstruction³ produce unsatisfactory results in regions with rapid phase variations and tend to introduce additional noise when estimating high-frequency components. Alternatively, Convolutional Neural Networks (CNNs) have been proposed for PF reconstruction, showing promising results for Dixon imaging⁴ and brain DWI⁵. However, it remains unclear how these approaches generalize to the task of PF reconstruction in prostate DWI which is particularly difficult due to low SNR.

The goal of this work is to develop a CNN-based method for PF reconstruction of DW images of the prostate. In contrast to previous work, correlations across different b-values are exploited.

Methods

Data: A prototype EPI sequence was used to acquire fully-sampled DW images of the prostate at two b-values (50 and 800$$$\,$$$s/mm²) in 18 male volunteers on 1.5$$$\,$$$T and 3$$$\,$$$T MR scanners (MAGNETOM, Siemens Healthcare GmbH, Erlangen, Germany). In addition, one patient with suspected prostate cancer was scanned to showcase the clinical value. While 15 volunteer data sets were used for training, the data of one volunteer served as a validation set. The remaining two volunteers as well as the patient data set were used for testing. Instead of operating on the combined, trace-weighted magnitude images, our model was applied to the complex-valued images of the single shots. In order to augment the data set, images were acquired with two different phase-encoding directions (RL and AP) and with both single-shot EPI and reduced FOV EPI (ZOOMit). In total, the training, validation and test splits contained 10944, 768 and 1536 images, respectively.

CNN training: For preprocessing, images were retrospectively downsampled by multiplication with a PF mask in k-space, yielding the zero-filled images. For training, the images were interpolated to a fixed resolution of 100x100 pixels. The complex-valued images were presented as a two-channel input to the network. A modified version of a state-of-the-art CNN in the related field of super-resolution was used as network architecture⁶ (see Figure 1 for details). Training was performed by minimizing the L1-loss between the network output and the fully-sampled ground-truth images using the Adam optimizer⁷ with a learning rate of 10^-4. Based on the observation that the deblurring ability of the network on low-signal b800 images was poor, we tried to utilize the correlations between different b-values by concatenating b50 and b800 images along the channel dimension. Although b50 and b800 images show different contrasts, they share similar structural information, hence, allowing the less noisy b50 image to serve as a "guidance". As a post-processing step which further enforced data fidelity, the k-space of the output images was substituted by measured data where it was available.

Evaluation: The results on the test set were evaluated quantitatively and qualitatively and compared to standard PF reconstruction methods. In addition to retrospectively and prospectively undersampled trace-weighted images, the performance was analyzed on derived ADC maps as well.

Results & Discussion

Table 1 gives an overview of the quantitative results of our CNN and the reference methods zero-filling (ZF), POCS and homodyne reconstruction. On the entire test set, our CNN outperforms the next-best method (POCS) by +$$$\,$$$0.0175 (SSIM) and +$$$\,$$$1.45$$$\,$$$dB (PSNR). For b800 images, the improvements are lower than for b50 images, underlining the difficulty of training the network for inputs with very low SNR.

The qualitative comparison on retrospectively undersampled images presented in Figure 2 confirms the quantitative results. While the ZF reconstruction suffers from strong blurring and ringing, POCS as well as the homodyne reconstruction are capable of restoring resolution. However, this comes at the cost of a structured noise pattern across the image. In contrast, our method performs deringing and deblurring without visual noise enhancement. The above-mentioned observations equivalently hold true for prospectively undersampled images (see Figure 3).

Figure 4 shows the ADC map of a patient with suspected prostate cancer. While an area of restricted diffusion is clearly delineated in the fully-sampled ground-truth image, blurring in the ZF reconstruction makes it more difficult to differentiate it from normal tissue. Our CNN-based method restores image quality and sharpness nearly to the level of the ground-truth.

Conclusion

We showed that a CNN-based reconstruction enables robust PF imaging. Unlike traditional reconstruction techniques, our method recovers image quality without introducing noticeable artifacts. Furthermore, correlations across b-values are exploited to improve performance for high b-values. The proposed technique provided promising results for prostate DWI in healthy volunteers and one patient. Although it was only applied to an extreme case of a PF factor of 5/8 in this work, the concept is similarly applicable to other commonly used PF factors. A clinical validation is the subject of future work.

Acknowledgements

No acknowledgement found.