A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer.
A DNN was trained using co-registered ARFI, SWEI, MF, and B-mode data obtained in men with biopsy-confirmed prostate cancer prior to radical prostatectomy (15 subjects, comprising 980,620 voxels). Data were obtained using a commercial scanner that was modified to allow user control of the acoustic beam sequences and provide access to the raw image data. For each subject, the index lesion and a non-cancerous region were manually segmented using visual confirmation based on whole-mount histopathology data.
In a prostate phantom, the DNN increased lesion contrast-to-noise ratio (CNR) compared to a previous approach that used a linear support vector machine (SVM). In the in vivo test datasets (n = 15), the DNN-based mpUS volumes clearly portrayed histopathology-confirmed prostate cancer and significantly improved CNR compared to the linear SVM (2.79 ± 0.88 vs. 1.98 ± 0.73, paired-sample t-test p < 0.001). In a sub-analysis in which the input modalities to the DNN were selectively omitted, the CNR decreased with fewer inputs; both stiffness- and echogenicity-based modalities were important contributors to the multiparametric model.
The findings from this study indicate that a DNN can be optimized to generate mpUS prostate volumes with high CNR from ARFI, SWEI, MF, and B-mode and that this approach outperforms a linear SVM approach.
Ultrasound in medicine & biology. 2024 Aug 21 [Epub ahead of print]
Derek Y Chan, D Cody Morris, Spencer R Moavenzadeh, Theresa H Lye, Thomas J Polascik, Mark L Palmeri, Jonathan Mamou, Kathryn R Nightingale
Department of Biomedical Engineering, Duke University, Durham, NC, USA. Electronic address: ., Department of Biomedical Engineering, Duke University, Durham, NC, USA., Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Topcon Advanced Biomedical Imaging Laboratory, Topcon Healthcare, Oakland, NJ, USA., Departments of Urology and Radiology, Duke University Medical Center, Durham, NC, USA., Department of Radiology, Weill Cornell Medicine, New York, NY, USA.