Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included. All patients had undergone abdominal CT between August 2019 and October 2019. CT-images were reconstructed using the following three methods: filtered back-projection, iterative reconstruction, and PixelShine (DL-software) with both sharp and soft kernels. Stone size, CT attenuation, and objective image quality (signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) were evaluated and compared using Bonferroni-corrected Friedman tests. Objective image quality was measured in six regions-of-interest. Stone size ranged between 4.4 × 3.1–4.4 × 3.2 mm (sharp kernel) and 5.1 × 3.8–5.6 × 4.2 mm (soft kernel). Mean attenuation ranged between 704–717 Hounsfield Units (HU) (soft kernel) and 915–1047 HU (sharp kernel). Differences in measured stone sizes were ≤1.3 mm. DL-processed images resulted in significantly higher CNR and SNR values (p 0.001) by decreasing image noise significantly (p 0.001). DL-software significantly improved objective image quality while maintaining both correct stone size and CT-attenuation values. Therefore, the clinical impact of stone assessment in denoised image data sets remains unchanged. Through the relevant noise suppression, the software additionally offers the potential to further reduce radiation exposure

Toward Optimal Fitting Parameters for Multi-Exponential DWI Image Analysis of the Human Kidney: A Simulation Study Comparing Different Fitting Algorithms

In DWI, multi-exponential signal analysis can be used to determine signal underlying diffusion components. However, the approach is very complex due to the inherent low SNR, the limited number of signal decay data points, and the absence of appropriate acquisition parameters and standardized analysis methods. Within the scope of this work, different methods for multi-exponential analysis of the diffusion signal in the kidney were compared. To assess the impact of fitting parameters, a simulation was conducted comparing the free non-negative (NNLS) and rigid non-linear least square (NLLS) fitting methods. The simulation demonstrated improved accuracy for NNLS in combination with area-under-curve estimation. Furthermore, the accuracy and stability of the results were further enhanced utilizing optimized parameters, namely 350 logarithmically spaced diffusion coefficients within [0.7, 300] × 10−3 mm2/s and a minimal SNR of 100. The NNLS approach shows an improvement over the rigid NLLS method. This becomes apparent not only in terms of accuracy and omitting prior knowledge, but also in better representation of renal tissue physiology. By employing the determined fitting parameters, it is expected that more stable and reliable results for diffusion imaging in the kidney can be achieved. This might enable more accurate DWI results for clinical utilization

Feasibility of quantitative susceptibility mapping (QSM) of the human kidney

Objective!#!To evaluate the feasibility of in-vivo quantitative susceptibility mapping (QSM) of the human kidney.!##!Methods!#!An axial single-breath-hold 3D multi-echo sequence (acquisition time 33 s) was completed on a 3 T-MRI-scanner (Magnetom Prisma, Siemens Healthineers, Erlangen, Germany) in 19 healthy volunteers. Graph-cut-based unwrapping combined with the T!##!Results!#!QSM was successful in 17 volunteers and the patient with renal fibrosis. Anatomical structures in the abdomen were clearly distinguishable by QSM and the susceptibility values obtained in the liver were comparable to those found in the literature. The results showed a good reproducibility. Besides, the mean renal QSM values obtained in healthy volunteers (0.04 ± 0.07 ppm for the right and - 0.06 ± 0.19 ppm for the left kidney) were substantially higher than that measured in the investigated fibrotic kidney (- 0.43 ± - 0.02 ppm).!##!Conclusion!#!QSM of the human kidney could be a promising approach for the assessment of information about microscopic renal tissue structure. Therefore, it might further improve functional renal MR imaging

Magnetic Resonance Imaging-guided Active Surveillance Without Annual Rebiopsy in Patients with Grade Group 1 or 2 Prostate Cancer: The Prospective PROMM-AS Study

Background: Multiparametric magnetic resonance imaging (mpMRI) may allow patients with prostate cancer (PC) on active surveillance (AS) to avoid repeat prostate biopsies during monitoring. Objective: To assess the ability of mpMRI to reduce guideline-mandated biopsy and to predict grade group upgrading in patients with International Society of Urological Pathology grade group (GG) 1 or GG 2 PC using Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scores. The hypothesis was that the AS disqualification rate (ASDQ) rate could be reduced to 15%. Design, setting and participants: PROMM-AS was a prospective study assessing 2-yr outcomes for an mpMRI-guided AS protocol. A 12 mo after AS inclusion on the basis of MRI/transrectal ultrasound fusion-guided biopsy (FBx), all patients underwent mpMRI. For patients with stable mpMRI (PRECISE 1–3), repeat biopsy was deferred and follow-up mpMRI was scheduled for 12 mo later. Patients with mpMRI progression (PRECISE 4–5) underwent FBx. At the end of the study, follow-up FBx was indicated for all patients. Outcome measurements and statistical analysis: We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for upgrading to GG 2 in the GG 1 group, and to GG 3 in the GG 2 group on MRI. We performed regression analyses that included clinical variables. Results and limitations: The study included 101 patients with PC (60 GG 1 and 41 GG 2). Histopathological progression occurred in 31 patients, 18 in the GG 1 group and 13 in the GG 2 group. Thus, the aim of reducing the ASDQ rate to 15% was not achieved. The sensitivity, specificity, PPV, and NPV for PRECISE scoring of MRI were 94%, 64%, 81%, and 88% in the GG 1 group, and 92%, 50%, 92%, and 50%, respectively, in the GG 2 group. On regression analysis, initial prostate-specific antigen (p < 0.001) and higher PRECISE score (4–5; p = 0.005) were significant predictors of histological progression of GG 1 PC. Higher PRECISE score (p = 0.009), initial Prostate Imaging-Reporting and Data System score (p = 0.009), previous negative biopsy (p = 0.02), and percentage Gleason pattern 4 (p = 0.04) were significant predictors of histological progression of GG 2 PC. Limitations include extensive MRI reading experience, the small sample size, and limited follow-up. Conclusions: MRI-guided monitoring of patients on AS using PRECISE scores avoided unnecessary follow-up biopsies in 88% of patients with GG 1 PC and predicted upgrading during 2-yr follow-up in both GG 1 and GG 2 PC. Patient summary: We investigated whether MRI (magnetic resonance imaging) scores can be used to guide whether patients with lower-risk prostate cancer who are on active surveillance (AS) need to undergo repeat biopsies. Follow-up biopsy was deferred for 1 year for patients with a stable score and performed for patients whose score progressed. After 24 months on AS, all men underwent MRI and biopsy. Among patients with grade group 1 cancer and a stable MRI score, 88% avoided biopsy. For patients with MRI score progression, AS termination was correctly recommended in 81% of grade group 1 and 92% of grade group 2 cases

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous