Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep conventional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully-sampled image. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm²

<div><p>Objectives</p><p>To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection.</p><p>Methods</p><p>Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0–4500 s/mm²) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models. The b-values were divided into four ranges: 0–1000, 0–2000, 0–3200, and 0–4500 s/mm², grouped as A, B, C, and D, respectively. ADC, , D*, f, DDC, α, D_app, and K_app were estimated for each group. The adjusted coefficient of determination (R²) was calculated to measure goodness-of-fit. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the parameters.</p><p>Results</p><p>All parameters except D* showed significant differences between cancerous tissues and benign PZs in each group. The area under the curve values (AUCs) of ADC were comparable in groups C and D (<i>p</i> = 0.980) and were significantly higher than those in groups A and B (<i>p</i>< 0.05 for all). The AUCs of ADC and K_app in groups B and C were similar (<i>p</i> = 0.07 and <i>p</i> = 0.954), and were significantly higher than the other parameters (<i>p</i>< 0.001 for all). The AUCs of ADC in group D was slightly higher than K_app (<i>p</i> = 0.002), and both were significantly higher than the other parameters (<i>p</i>< 0.001 for all).</p><p>Conclusions</p><p>ADC derived from conventional mono-exponential high b-value (3200 s/mm²) models is an optimal parameter for PZ PCa detection.</p></div

Diagnostic performance of parameters calculated with different b-value ranges.

<p>Diagnostic performance of parameters calculated with different b-value ranges.</p

ROC curve analyses show the diagnostic accuracy of the diffusion parameters in distinguishing between cancerous tissues and benign PZs.

<p>In group A, K_app had the largest AUC (0.940), but the AUCs of ADC and K_app were not significantly different (<i>p</i> = 0.070). In groups B and C, the AUCs of ADC and K_app were comparable and significantly higher than those of the other parameters. In group D, the AUCs of ADC was slightly higher than that of K_app (0.957 vs 0.953, <i>p</i> = 0.002), and both were significantly higher than those of other parameters (<i>p</i>< 0.001 for all).</p

Prostate cancer in a 61-year-old patient with high serum PSA level of 19.43 ng/ml.

<p>(a)Transverse T2-weighted anatomical image shows a hypointense lesion (white arrow) in the right middle peripheral zone of the prostate; (b) A series of b-value images are shown with the corresponding location to the transverse T2-weighted (unites, s/mm²); (c) Measured signal and fitted curves of cancerous tissue and the opposite side benign tissue using maximum b-value of 4500 s/mm² (group D).</p

Diffusion parameters in benign PZs and cancerous tissues.

<p>Diffusion parameters in benign PZs and cancerous tissues.</p

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm² - Fig 3

<p><b>Boxplot of diffusion parameters calculated using different b-value ranges (groups A, B, C and D)</b>. (a-b, d-g), the mean values of ADC, , f, DDC, α, and D_app were significantly lower in cancerous tissues than in benign PZs in each group (h). The K_app in cancerous tissues was significantly higher than in benign PZs in each group. The value of D*, which had a large standard deviation, showed no significant difference between cancerous tissues and benign PZs in groups A and D but was significantly different in groups B and C(c).</p

Graph showing variations in the adjusted R² values of the mono-exponential, bi-exponential, stretched-exponential and kurtosis models with an increase in b-value.

<p>The mean value of the adjusted R² of the mono-exponential model decreased with the increase of b-value, while the mean value of the adjusted R² of the bi-exponential, stretched-exponential and kurtosis models were stable and excellent with the increase of b-value (It should be noted that, because the adjusted R² values of the bi-exponential and stretched-exponential models were close, their curves have been superimposed).</p

Mean values and standard deviation of the adjusted R².

<p>Mean values and standard deviation of the adjusted R².</p