Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with ...promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1–4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49–85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005–<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.
Objectives
A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, ...noise, artifacts and diagnostic confidence without change of acquisition parameters.
Materials and methods
Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBE
S
), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBE
DL
). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured.
Results
The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBE
DL
(all
P
< 0.001).
There was no significant difference in the diameter or the localization of the largest lung lesion in VIBE
DL
compared to VIBE
S
. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBE
DL
(both
P
< 0.001).
Conclusion
The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.
•Deep learning image reconstruction of diffusion-weighted liver imaging including acquisition time reduction of more than 40% is feasible without loss of image quality.•Deep learning image ...reconstruction of diffusion-weighed liver imaging provides significant reduction of the noise (P < 0.001).•Deep learning image reconstruction of diffusion-weighted liver imaging provides significantly greater signal intensities on ADC map for the liver, spleen, and erector spinae muscles.
The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence.
One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21–88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWIDL as compared to 2 min 31 s for standard DWI (DWIStd) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1–4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest.
No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWIDL versus DWIStd (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWIDL versus DWIStd for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles.
DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.
Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study ...was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2
) of the prostate as compared to conventional T2w TSE imaging (T2
).
Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2
(3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2
(2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2
was 4:12 min compared to 4:37 min for T2
. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2
versus T2
was evaluated.
The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2
with a median of 4 versus a median of 3 in T2
(
< 0.001 for both readers). Image noise was evaluated to be significantly worse in T2
as compared to T2
(
< 0.001 and
= 0.021, respectively). Overall image quality was also evaluated to be superior in T2
versus T2
with a median of 4 versus 3 (
< 0.001 for both readers). Both readers chose T2
in 29 cases as their preference.
Thin-slice T2
of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.
Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting ...might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.
•Deep learning-based super-resolution gradient echo imaging results in improved image quality and reduction of acquisition time for MRI of the pancreas.•Deep learning-based super-resolution gradient ...echo imaging results in less image artifacts via simulated shortening of the acquisition time.•Deep learning-based super-resolution gradient echo imaging can be easily implemented in post-processing workflow without protocol changes.
The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBESTD).
This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation SD) years (age range: 33–89 years). VIBESTD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBESR). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best.
VIBESR was rated better than VIBESTD by both readers regarding lesion detectability (5 IQR: 5, 5 vs. 5 IQR: 4, 5, for reader 1; 5 IQR: 5, 5 vs. 4 IQR: 4, 5) for reader 2; both P <0.001), noise levels (5 IQR: 5, 5 vs. 5 IQR: 4, 5 for reader 1; 5 IQR: 5, 5 vs. 4 IQR: 4, 5 for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 IQR: 5, 5 vs. 5 IQR: 4, 5 for reader 1; 5 IQR: 5, 5 vs. 4 IQR: 4, 5 for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 IQR: 5, 5 vs. 5 IQR: 4, 5 for reader 1; 5 IQR: 5, 5 vs. 4 IQR: 4, 5 for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBESR and VIBESTD images (P > 0.05). The mean acquisition time for VIBESTD (15 ± 1 SD s; range: 11–16 s) was longer than that for VIBESR (13 ± 1 SD s; range: 11–14 s) (P < 0.001).
Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI.
Instrumentation failure in the context of spine surgery is attributed to cyclic loading leading to formation of fatigue cracks, which later propagate and result in rod fracture. A biomechanical ...analysis of the potential impact of electrocautery on the fatigue life of spinal implants has not been previously performed. The aim of this study was to assess the fatigue life of titanium (Ti) and cobalt-chrome (CoCr) rod-screw constructs after being treated with electrocautery. Twelve spinal constructs with CoCr and Ti rods were examined. Specimens were divided into four groups by rod material (Ti and CoCr) and application of monopolar electrocautery on the rods' surface (control-group and electrocautery-group). Electrocautery was applied on each rod at three locations, then constructs were cyclically tested. Outcome measures were load-to-failure, total number of cycles-to-failure, and location of rod failure. Ti-rods treated with electrocautery demonstrated a significantly decreased fatigue life compared to non-treated Ti-rods. Intergroup comparison of cycles-to-failure revealed a significant mean decrease of almost 9 × 10
cycles (
= 0.03). No CoCr-rods failed in this experiment. Electrocautery application on the surface of Ti-rods significantly reduces their fatigue life. Surgeons should exercise caution when using electrocautery in the vicinity of Ti-rods to mitigate the risk of rod failure.
BackgroundTo assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding ...immunotherapy.Material and methodsA total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance.ResultsWe performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001).ConclusionThe new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.
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