A REVIEW ON THE DIVISION OF MAGNETIC RESONANT PROSTATE IMAGES WITH DEEP LEARNING

Deep learning; it is often used in dividing processes on images in the biomedical field. In recent years, it has been observed that there is an increase in the division procedures performed on prostate images using deep learning compared to other methods of image division. Looking at the literature; It is seen that the process of dividing prostate images, which are carried out with deep learning, is an important step for the diagnosis and treatment of prostate cancer. For this reason, in this study; to be a source for future splitting operations; deep learning splitting procedures on prostate images obtained from magnetic resonance (MRI) imaging devices were examined. Key words: deep learning, image division, prostate cancer

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Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

Abdominal Radiology ◽

10.1007/s00261-021-02964-6 ◽

2021 ◽

Author(s):

Xinzeng Wang ◽

Jingfei Ma ◽

Priya Bhosale ◽

Juan J. Ibarra Rovira ◽

Aliya Qayyum ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Deep Learning ◽

Magnetic Resonance ◽

Image Quality ◽

Reconstruction Method ◽

Anatomical Landmarks ◽

Resonance Imaging ◽

Endorectal Coil ◽

Reconstruction Methods

Abstract Introduction Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, “DLR” in improving image quality and mitigating artifacts, which is now commercially available as AIRTM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts. Methods This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERCDLR, ERCConv, Non-ERCDLR, and Non-ERCConv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor. Results The Non-ERCDLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor. Conclusion Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

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Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

10.1117/12.2254423 ◽

2017 ◽

Cited By ~ 12

Author(s):

Yohannes K. Tsehay ◽

Nathan S. Lay ◽

Holger R. Roth ◽

Xiaosong Wang ◽

Jin Tae Kwak ◽

...

Keyword(s):

Neural Network ◽

Prostate Cancer ◽

Deep Learning ◽

Magnetic Resonance ◽

Convolutional Neural Network ◽

Cancer Detection ◽

Magnetic Resonance Images ◽

Prostate Cancer Detection

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Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Cancers ◽

10.3390/cancers14010040 ◽

2021 ◽

Vol 14 (1) ◽

pp. 40

Author(s):

Gyu Sang Yoo ◽

Huan Minh Luu ◽

Heejung Kim ◽

Won Park ◽

Hongryull Pyo ◽

...

Keyword(s):

Prostate Cancer ◽

Computed Tomography ◽

Deep Learning ◽

Magnetic Resonance ◽

Soft Tissue ◽

Ct Images ◽

Learning Approaches ◽

Learning Methods ◽

Computed Tomography Images ◽

The Mean

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.

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