scholarly journals Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based On MR Priors

2021 ◽  
Author(s):  
Fuquan Deng ◽  
Xiaoyuan Li ◽  
Fengjiao Yang ◽  
Hongwei Sun ◽  
Jianmin Yuan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Nuclear Medicine ◽  
Deep Learning ◽  
Low Dose ◽  
Imaging Method ◽  
Learning Methods ◽  
Full Dose ◽  
Multiple Imaging ◽  
Psma Pet ◽  
Noise Ratio

Abstract Purpose: 68 Ga-prostate-specific membrane antigen (PSMA) PET/MRI has become an effective imaging method for prostate cancer. The purpose of this study was to use deep learning methods to perform low-dose image restoration on PSMA PET/MRI and to evaluate the effect of synthesis on the images and the medical diagnosis of patients at risk of prostate cancer.Methods: We reviewed the 68 Ga-PSMA PET/MRI data of 41 patients. The low-dose PET images of these patients were restored to full-dose PET images through a deep learning method based on MR priors. The synthesized images were evaluated according to quantitative scores from nuclear medicine doctors and multiple imaging indicators, such as peak-signal noise ratio (PSNR), structural similarity (SSIM), normalization mean square error (NMSE), and relative contrast-to-noise ratio (RCNR).Results: The scores of the full images synthesized from 25%- and 50%-dose images based on MR priors were 3.84±0.36 and 4.03±0.17, respectively, which were higher than the scores of the target images. Correspondingly, the PSNR, SSIM, NMSE, and RCNR values of the full-dose images synthesized from 25%-dose PET images based on MR priors were 37.86±4.16, 0.916±0.063, 0.015±0.012, and 1.004±0.126, respectively.Conclusion: According to a combination of quantitative scores from nuclear medicine doctors and evaluations with multiple image indicators, the synthesis of full-dose images based on MR priors using 25%- and 50%-dose PET images did not affect the clinical diagnosis of prostate cancer. Prostate cancer patients can undergo 68 Ga-PSMA prostate PET/MRI scans with radiation doses reduced by up to 75% through the use of deep learning methods to synthesize full-dose images.

scholarly journals Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

2020 ◽  
Vol 189 ◽  
pp. 105316 ◽  
Author(s):  
Rogier R. Wildeboer ◽  
Ruud J.G. van Sloun ◽  
Hessel Wijkstra ◽  
Massimo Mischi
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Cancer Imaging ◽  
Learning Methods

scholarly journals Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200677
Author(s):  
Andrea Steuwe ◽  
Marie Weber ◽  
Oliver Thomas Bethge ◽  
Christin Rademacher ◽  
Matthias Boschheidgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Dose Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Image Noise ◽  
The Novel ◽  
Subjective Image Quality ◽  
Image Information ◽  
Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

Comparison of hybrid 68Ga-PSMA-PET/CT and 99mTc-DPD-SPECT/CT for the detection of bone metastases in prostate cancer patients: Additional value of morphologic information from low dose CT

2017 ◽  
Vol 28 (2) ◽  
pp. 610-619 ◽  
Author(s):  
Jan-Carlo Janssen ◽  
Sebastian Meißner ◽  
Nadine Woythal ◽  
Vikas Prasad ◽  
Winfried Brenner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Low Dose ◽  
Low Dose Ct ◽  
Psma Pet ◽  
Pet Ct ◽  
Additional Value

HIGH-DOSE-RATE AND LOW-DOSE-RATE BRACHYTHERAPY AS MONOTHERAPY FOR CLINICALLY LOCALIZED PROSTATE CANCER

2019 ◽  
Vol 65 (3) ◽  
pp. 434-440
Author(s):  
Vladimir Solodkiy ◽  
Andrey Pavlov ◽  
Aleksey Tsybulskiy ◽  
Aleksandr Pchelintsev ◽  
Ivan Moshurov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dose Rate ◽  
Low Dose ◽  
Risk Groups ◽  
High Dose Rate ◽  
Localized Prostate Cancer ◽  
High Dose ◽  
Intermediate Risk ◽  
Psma Pet ◽  
Gu Toxicity

PURPOSE:To compare the outcome of high-dose-rate interstitial brachytherapy (HDR-BT) and low-dose-ratebrachytherapy (LDR-BT) as monotherapy for localized prostate cancer of low and intermediate risks progression. METHODS AND MATERIALS: The study included 165 patients with localized prostate cancer in low and intermediate progression risk groups. We compared 65 patients treated with HDR-BT and 100 patients with LDR-BT as monotherapy. LDR-BT treated advanced disease with more hormonal therapy than HDR-BT. All patients were in low and intermediate risk groups for prostate cancer progression. HDR-BT as monotherapy was delivered in 2 fractions of 15 Gy, two weeks apart. LDR-BT was performed in a standard mode of 145 Gy. The median observation was 32 months. All patients gave written informed consent. RESULTS: Overall biochemical free survival rate (BFSR) is 95,8%. There are 7 people having a growing prostatic specific antigen (PSA) while the case follow-up (in the group HDR-BT - 2 patients, LDR - 5 patients). Two recurrence cases with metastases in lymph nodes and bones were brought out as a result of 68Ga-PSMA PET examination in the group of HDR-BT. In 4 cases out of 5 LDR-BT, a local recurrence was detected (p=0,085). All cases of relapse were found in patients at intermediate risk (p = 0,041). LDR-BT showed a higher incidence of genitourinary (GU) toxicity grade >2 than that of HDR-BT in the acute phase and grade 1 toxicity in late phase. Accumulated incidence of late grade >2 GU and GU toxicity was equivalent between HDR-BT and LDR-BT. CONCLUSION: HDR-BT monotherapy showed an equivalent outcome to that of LDR-BT for low and intermediate risk patients. LDR-BT showed equivalent incidence of grade >2 late GU toxicities and higher grade >2 acute GU toxicity as that of HDR-BT as a monotherapy.

DDV: A Taxonomy for Deep Learning Methods in Detecting Prostate Cancer

2021 ◽  
Author(s):  
Abeer Alsadoon ◽  
Ghazi Al-Naymat ◽  
Omar Hisham Alsadoon ◽  
P. W. C. Prasad
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Learning Methods

scholarly journals 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 ◽  
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.

scholarly journals Prospective analysis of clinically significant prostate cancer detection with [18F]DCFPyL PET/MRI compared to multiparametric MRI: a comparison with the histopathology in the radical prostatectomy specimen, the ProStaPET study

2021 ◽  
Author(s):  
Yves J. L. Bodar ◽  
Ben G. J. C. Zwezerijnen ◽  
Patrick J. van der Voorn ◽  
Bernard H. E. Jansen ◽  
Ruth S. Smit ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Imaging Method ◽  
Operating Characteristics ◽  
Tumour Localization ◽  
Prostate Biopsies ◽  
Psma Pet ◽  
Clinically Significant ◽  
Total Agreement

Abstract Purpose Multiparametric magnetic resonance imaging (mpMRI) is a well-established imaging method for localizing primary prostate cancer (PCa) and for guiding targeted prostate biopsies. [18F]DCFPyL positron emission tomography combined with MRI (PSMA-PET/MRI) might be of additional value to localize primary PCa. The aim of this study was to assess the diagnostic performance of [18F]DCFPyL-PET/MRI vs. mpMRI in tumour localization based on histopathology after robot-assisted radical-prostatectomy (RARP), also assessing biopsy advice for potential image-guided prostate biopsies. Methods Thirty prospectively included patients with intermediate to high-risk PCa underwent [18F]DCFPyL-PET/MRI and mpMRI prior to RARP. Two nuclear medicine physicians and two radiologists assessed tumour localization on [18F]DCFPyL-PET/MRI and on mpMRI respectively, and gave a prostate biopsy advice (2 segments) using a 14-segment model of the prostate. The uro-pathologist evaluated the RARP specimen for clinically significant PCa (csPCa) using the same model. csPCa was defined as any PCa with Grade Group (GG) ≥ 2. The biopsy advice based on imaging was correlated with the final histology in the RARP specimen for a total-agreement analysis. An additional near-agreement correlation was performed to approximate clinical reality. Results Overall, 142 of 420 (33.8%) segments contained csPCa after pathologic examination. The segments recommended for targeted biopsy contained the highest GG PCa segment in 27/30 patients (90.0%) both for [18F]DCFPyL-PET/MRI and mpMRI. Areas under the receiver operating characteristics curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the total-agreement detection of csPCa per segment using [18F]DCFPyL-PET/MRI were 0.70, 50.0%, 89.9%, 71.7%, and 77.9%, respectively. These results were 0.75, 54.2%, 94.2%, 82.8%, and 80.1%, respectively, for mpMRI only. Conclusion Both [18F]DCFPyL-PET/MRI and mpMRI were only partly able to detect csPCa on a per-segment basis. An accurate detection (90.0%) of the highest GG lesion at patient-level was observed when comparing both [18F]DCFPyL-PET/MRI and mpMRI biopsy advice with the histopathology in the RARP specimen. So, despite the finding that [18F]DCFPyL-PET/MRI adequately detects csPCa, it does not outperform mpMRI.

scholarly journals DeepTOFSino: A Deep Learning Model for Synthesizing Full-Dose time-of-flight bin Sinograms from their Corresponding Low-Dose Sinograms

NeuroImage ◽  
2021 ◽  
pp. 118697
Author(s):  
Amirhossein Sanaat ◽  
Hossein Shooli ◽  
Sohrab Ferdowsi ◽  
Isaac Shiri ◽  
Hossein Arabi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Dose ◽  
Time Of Flight ◽  
Learning Model ◽  
Full Dose ◽  
Dose Time ◽  
Deep Learning Model

scholarly journals Iterative reconstruction improves image quality and reduces radiation dose in trauma protocols; A human cadaver study

2021 ◽  
Vol 10 (10) ◽  
pp. 205846012110553
Author(s):  
Johannes Clemens Godt ◽  
Cathrine K Johansen ◽  
Anne Catrine T Martinsen ◽  
Anselm Schulz ◽  
Helga M Brøgger ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Human Cadaver ◽  
Full Dose ◽  
Significant Difference ◽  
Noise Ratio ◽  
Dose Levels

Background Radiation-related cancer risk is an object of concern in CT of trauma patients, as these represent a young population. Different radiation reducing methods, including iterative reconstruction (IR), and spilt bolus techniques have been introduced in the recent years in different large scale trauma centers. Purpose To compare image quality in human cadaver exposed to thoracoabdominal computed tomography using IR and standard filtered back-projection (FBP) at different dose levels. Material and methods Ten cadavers were scanned at full dose and a dose reduction in CTDIvol of 5 mGy (low dose 1) and 7.5 mGy (low dose 2) on a Siemens Definition Flash 128-slice computed tomography scanner. Low dose images were reconstructed with FBP and Sinogram affirmed iterative reconstruction (SAFIRE) level 2 and 4. Quantitative image quality was analyzed by comparison of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Qualitative image quality was evaluated by use of visual grading regression (VGR) by four radiologists. Results Readers preferred SAFIRE reconstructed images over FBP at a dose reduction of 40% (low dose 1) and 56% (low dose 2), with significant difference in overall impression of image quality. CNR and SNR showed significant improvement for images reconstructed with SAFIRE 2 and 4 compared to FBP at both low dose levels. Conclusions Iterative image reconstruction, SAFIRE 2 and 4, resulted in equal or improved image quality at a dose reduction of up to 56% compared to full dose FBP and may be used a strong radiation reduction tool in the young trauma population.

A randomized trial of personalized peptide vaccine (PPV) plus low-dose estramustine (EMP) versus full-dose EMP in patients with hormone-refractory prostate cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 3007-3007
Author(s):  
M. Noguchi ◽  
H. Uemura ◽  
H. Kumon ◽  
Y. Nasu ◽  
Y. Hirao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Low Dose ◽  
Performance Status ◽  
Randomized Study ◽  
Peptide Vaccine ◽  
Subcutaneous Administration ◽  
Rank Test ◽  
Hormone Refractory Prostate Cancer ◽  
Full Dose ◽  
Hormone Refractory

3007 Background: Personalized selection of the right peptides for each patient could be a novel peptide-based immunotherapy for boosting anti-cancer immunity in many patients (pts). This randomized study evaluated the anti-tumor effect and safety of PPV plus a low-dose EMP compared with full dose EMP for patients with hormone-refractory prostate cancer (HRPC). Methods: This was a randomized (1:1), open labeled, cross-over study in pts with HRPC. Pts were randomized to arm A; PPV plus low-dose EMP (280 mg/day) or arm B; full dose EMP (560 mg/day) according to age and PSA levels. In arm A, prevaccination plasma were measured for their IgG levels for each of the 14 or 12 candidate peptides which can induce HLA-A2 or A24-restricted CTL activity against cancer cells followed by biweekly subcutaneous administration of the top four peptides (3mg each) showing the strongest IgG responses. Disease progression (PD) was defined as three consecutive and 125% increase from baseline PSA levels at least two weeks apart or objective PD by RECIST criteria. After PD, pts were treated with the opposite regime. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival and toxicity. The planned sample size was 80. Results: A total of 54 pts from 4 institutions were enrolled between June 2006 and December 2008. The accural into arms A and B was 27 and 27 pts, respectively. The main pts characteristics are (arm A/B): median age 71/69 years, EOCG performance status 0/1 96%/4% and 100%/0%, HLA A2/A24/A2A24 40%/32%/28% and 54%/27%/19%, median PSA 27/25 ng/ml, and metastatic HRPC 96%/85%. All pts were evaluable for their response at the time of interim analysis. The personalized peptide vaccination was well tolerated with no major adverse effects. Increased levels of IgG responses to the vaccinated peptides were observed in 20 of 23 (87%) patients tested. The median PFS time was 246 days in the arm A group and 85 days in the arm B, respectively. The PFS time in the arm A was statistically longer than that in the arm B (log-rank test: p = 0.0007, hazard ratio: 0.27, 95%CI: 0.12 to 0.615). Conclusions: PPV plus low-dose EMP was associated with improvement in PSA-based PFS compared to full-dose EMP alone. No significant financial relationships to disclose.

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