Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study

Abstract

BackgroundApparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value.ObjectivesWe aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks.MethodsThis prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient.ResultsThe s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC.ConclusionThe deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

“…Peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), and feature similarity (FSIM) were selected as evaluation indexes [ 21 ].…”

Recent Developments in Speeding up Prostate MRI

“…For DWI, AI-based applications for synthesizing high b-value DWI or generating ADC maps demonstrate comparable performance to conventional methods of high b-value acquisition and ADC generation. [31][32][33][34][35] To ensure successful implementation in clinical practice, the proposed methods must exhibit diagnostic quality comparable to conventional techniques. Radiological evaluation plays a critical role in this assessment, as quantitative metrics may not fully reflect diagnostic quality.…”

“…In another prospective study, ADCs with a reduced FOV and increased resolution were synthesized using only one full FOV b-value (b-50, b-1000, or b-1500) using a GAN. 34 Two radiologists evaluated the synthesized ADC maps for 50 patients and 10 volunteers. Compared to conventional ADCs, synthesized ADCs demonstrated better image quality, higher accuracy, and reproducibility.…”

A preliminary study on the diagnostic value of PSADR, DPC and TSRP in the distinction of prostatitis and prostate cancer

“…The imaging changes of prostatitis and prostate cancer on T2W1 and DWI are similar, expressed as abnormal signal changes in the peripheral zone, but the signal intensity is different [ 16 , 17 ]. We found that after the signal value of tumor nodules on T2W1 is lower than that in the inflammatory lesions, and the signal value of DWI in tumor nodules can is higher than inflammatory lesions, by measuring the lesions in the signal value of the two groups and the comparison with the surrounding tissue signal values.…”