Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3 Tesla

Purpose

The objective of this study was to implement a 5-minute MRI protocol for the shoulder in routine clinical practice consisting of accelerated 2D turbo spin echo (TSE) sequences with deep learning (DL) reconstruction at 1.5 and 3 Tesla, and to compare the image quality and diagnostic performance to that of a standard 2D TSE protocol.

Methods

Patients undergoing shoulder MRI between October 2020 and June 2021 were prospectively enrolled. Each patient underwent two MRI examinations: first a standard, fully sampled TSE (TSES) protocol reconstructed with a standard reconstruction followed by a second fast, prospectively undersampled TSE protocol with a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSEDL). Image quality and visualization of anatomic structures as well as diagnostic performance with respect to shoulder lesions were assessed using a 5-point Likert-scale (5 = best). Interchangeability analysis, Wilcoxon signed-rank test and kappa statistics were performed to compare the two protocols.

Results

A total of 30 participants was included (mean age 50±15 years; 15 men). Overall image quality was evaluated to be superior in TSEDL versus TSES (p<0.001). Noise and edge sharpness were evaluated to be significantly superior in TSEDL versus TSES (noise: p<0.001, edge sharpness: p<0.05). No difference was found concerning qualitative diagnostic confidence, assessability of anatomical structures (p>0.05), and quantitative diagnostic performance for shoulder lesions when comparing the two sequences.

Conclusions

A fast 5-minute TSEDL MRI protocol of the shoulder is feasible in routine clinical practice at 1.5 and 3 T, with interchangeable results concerning the diagnostic performance, allowing a reduction in scan time of more than 50% compared to the standard TSES protocol.

Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T

RATIONALE AND OBJECTIVES

To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI.

MATERIALS AND METHODS

A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWIS) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWIDL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm2) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWIS and DWIDL were compared with the Wilcoxon signed-rank test.

RESULTS

The overall image quality was evaluated to be significantly superior in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWIS was 2:06 minutes, and simulated acquisition time for DWIDL was 1:12 minutes.

CONCLUSION

DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.

Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

Deep Learning MRI Reconstruction for Accelerating Turbo Spin Echo Hand and Wrist Imaging: A Comparison of Image Quality, Visualization of Anatomy, and Detection of Common Pathologies with Standard Imaging

RATIONALE AND OBJECTIVES

Magnetic resonance imaging (MRI) of the hand and wrist is a routine MRI examination and takes about 15-20 minutes, which can lead to problems resulting from the relatively long scan time, such as decreased image quality due to motion artifacts and lower patient throughput. The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the hand and wrist regarding image quality, visualization of anatomy, and diagnostic performance concerning common pathologies.

MATERIALS AND METHODS

Twenty-one patients (mean age: 43 ± 19 [19-85] years, 10 men, 11 female) were prospectively enrolled in this study between October 2020 and June 2021. Each participant underwent two MRI protocols: first, standard fully sampled TSE sequences reconstructed with a standard GRAPPA reconstruction (TSES) and second, prospectively undersampled TSE sequences using a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSEDL). Both protocols were acquired consecutively in one examination. Two experienced MSK-imaging radiologists qualitatively evaluated the images concerning image quality, noise, edge sharpness, artifacts, and diagnostic confidence, as well as the delineation of anatomical structures (triangular fibrocartilage complex, tendon of the extensor carpi ulnaris muscle, extrinsic and intrinsic ligaments, median nerve, cartilage) using a five-point Likert scale and assessed common pathologies. Wilcoxon signed-rank test and kappa statistics were performed to compare the sequences.

RESULTS

Overall image quality, artifacts, delineation of anatomical structures, and diagnostic confidence of TSEDL were rated to be comparable to TSES (p > 0.05). Additionally, TSEDL showed decreased image noise (4.90, median 5, IQR 5-5) compared to TSES (4.52, median 5, IQR 4-5, p < 0.05) and improved edge sharpness (TSEDL: 4.10, median 4, IQR 3.5-5; TSES: 3.57, median 4, IQR 3-4; p < 0.05). Inter- and intrareader agreement was substantial to almost perfect (κ = 0.632-1.000) for the detection of common pathologies. Time of acquisition could be reduced by more than 60% with the protocol using TSEDL.

CONCLUSION

Compared to TSES, TSEDL provided decreased noise and increased edge sharpness, equal image quality, delineation of anatomical structures, detection of pathologies, and diagnostic confidence. Therefore, TSEDL may be clinically relevant for hand and wrist imaging, as it reduces examination time by more than 60%, thus increasing patient comfort and patient throughput.

Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network

OBJECTIVES

Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSEDL) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance.

MATERIAL AND METHODS

In this prospective, monocentric study, TSEDL was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSES). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSEDL). Two radiologists assessed the TSEDL and TSES regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSEDL and TSES.

RESULTS

Compared with TSES, TSEDL was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSES and TSEDL concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved.

CONCLUSION

TSEDL of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSES, reducing the acquisition time significantly.

Combined Metabolic and Functional Tumor Volumes on [18F]FDG-PET/MRI in Neuroblastoma Using Voxel-Wise Analysis

PURPOSE

The purpose of our study was to evaluate the association between the [18F]FDG standard uptake value (SUV) and the apparent diffusion coefficient (ADC) in neuroblastoma (NB) by voxel-wise analysis.

METHODS

From our prospective observational PET/MRI study, a subcohort of patients diagnosed with NB with both baseline imaging and post-chemotherapy imaging was further investigated. After registration and tumor segmentation, metabolic and functional tumor volumes were calculated from the ADC and SUV values using dedicated software allowing for voxel-wise analysis. Under the mean of thresholds, each voxel was assigned to one of three virtual tissue groups: highly vital (v) (low ADC and high SUV), possibly low vital (lv) (high ADC and low SUV), and equivocal (e) with high ADC and high SUV or low ADC and low SUV. Moreover, three clusters were generated from the total tumor volumes using the method of multiple Gaussian distributions. The Pearson's correlation coefficient between the ADC and the SUV was calculated for each group.

RESULTS

Out of 43 PET/MRIs in 21 patients with NB, 16 MRIs in 8 patients met the inclusion criteria (PET/MRIs before and after chemotherapy). The proportion of tumor volumes were 26%, 36%, and 38% (v, lv, e) at baseline, 0.03%, 66%, and 34% after treatment in patients with response, and 42%, 25%, and 33% with progressive disease, respectively. In all clusters, the ADC and the SUV correlated negatively. In the cluster that corresponded to highly vital tissue, the ADC and the SUV showed a moderate negative correlation before treatment (R = -0.18; p < 0.0001) and the strongest negative correlation after treatment (R = -0.45; p < 0.0001). Interestingly, only patients with progression (n = 2) under therapy had a relevant part in this cluster post-treatment.

CONCLUSION

Our results indicate that voxel-wise analysis of the ADC and the SUV is feasible and can quantify the different quality of tissue in neuroblastic tumors. Monitoring ADCs as well as SUV levels can quantify tumor dynamics during therapy.

Novel deep-learning-based diffusion weighted imaging sequence in 1.5 T breast MRI

PURPOSE

This study aimed to assess the technical feasibility, the impact on image quality, and the acquisition time (TA) of a new deep-learning-based reconstruction algorithm in diffusion weighted imaging (DWI) of breast magnetic resonance imaging (MRI).

METHODS

Retrospective analysis of 55 female patients who underwent breast DWI at 1.5 T. Raw data were reconstructed using a deep-learning (DL) reconstruction algorithm on a subset of the acquired averages, therefore a reduction of TA. Clinically used standard DWI sequence (DWIStd) and the DL-reconstructed images (DWIDL) were compared. Two radiologists rated the image quality of b800 and ADC images, using a Likert-scale from 1 to 5 with 5 being considered perfect image quality. Signal intensities were measured by placing a region of interest (ROI) at the same position in both sequences.

RESULTS

TA was reduced by 40 % in DWIDL, compared to DWIStd, DWIDL improved noise and sharpness while maintaining contrast, the level of artifacts, and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC), (p = 0.955), b50-values (p = 0.070) and b800-values (p = 0.415) comparing standard and DL-imaging. Lesion assessment showed no differences regarding the number of lesions in ADC and DWI (both p = 1.000) and regarding the lesion diameter in DWI (p = 0.961;0.972) and ADC (p = 0.961;0.972).

CONCLUSIONS

The novel deep-learning-based reconstruction algorithm significantly reduces TA in breast DWI, while improving sharpness, reducing noise, and maintaining a comparable level of image quality, artifacts, contrast, and diagnostic confidence. DWIDL does not influence the quantifiable parameters.

Faster Elbow MRI with Deep Learning Reconstruction-Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging

OBJECTIVE

The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy.

MATERIALS AND METHODS

Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20-70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSESTD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSEDL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies.

RESULTS

Image quality was significantly improved in TSEDL (mean 4.35, IQR 4-5) compared to TSESTD (mean 3.76, IQR 3-4, p = 0.008). Moreover, TSEDL showed decreased noise (mean 4.29, IQR 3.5-5) compared to TSESTD (mean 3.35, IQR 3-4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSEDL and TSESTD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628-0.904). No difference was found concerning the detection of pathologies between the readers and between TSEDL and TSESTD. Using DL, the acquisition time could be reduced by more than 35% compared to TSESTD.

CONCLUSION

TSEDL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSESTD. Providing more than a 35% reduction of acquisition time, TSEDL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging

PURPOSE

Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality.

METHOD

In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics.

RESULTS

After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction.

CONCLUSIONS

An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.

Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.

Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability

Background Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed. Purpose To investigate the diagnostic interchangeability of an unrolled DL-reconstructed TSE (hereafter, TSE_DL) T1- and T2-weighted acquisition method with standard TSE and to test their impact on acquisition time, image quality, and diagnostic confidence. Materials and Methods This prospective single-center study included participants with various spinal abnormalities who gave written consent from November 2020 to July 2021. Each participant underwent two MRI examinations: standard fully sampled T1- and T2-weighted TSE acquisitions (reference standard) and prospectively undersampled TSE_DL acquisitions with threefold and fourfold acceleration. Image evaluation was performed by five readers. Interchangeability analysis and an image quality-based analysis were used to compare the TSE and TSE_DL images. Acquisition time and diagnostic confidence were also compared. Interchangeability was tested using the individual equivalence index regarding various degenerative and nondegenerative entities, which were analyzed on each vertebra and defined as discordant clinical judgments of less than 5%. Interreader and intrareader agreement and concordance (κ and Kendall τ and W statistics) were computed and Wilcoxon and McNemar tests were used. Results Overall, 50 participants were evaluated (mean age, 46 years ± 18 [SD]; 26 men). The TSE_DL method enabled up to a 70% reduction in total acquisition time (100 seconds for TSE_DL vs 328 seconds for TSE, P < .001). All individual equivalence indexes were less than 4%. TSE_DL acquisition was rated as having superior image noise by all readers (P < .001). No evidence of a difference was found between standard TSE and TSE_DL regarding frequency of major findings, overall image quality, or diagnostic confidence. Conclusion The deep learning (DL)-reconstructed turbo spin-echo (TSE) method was found to be interchangeable with standard TSE for detecting various abnormalities of the spine at MRI. DL-reconstructed TSE acquisition provided excellent image quality, with a 70% reduction in examination time. German Clinical Trials Register no. DRKS00023278 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hallinan in this issue.

Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time

PURPOSE

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).

MATERIALS AND METHODS

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.

RESULTS

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).

CONCLUSION

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.

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

OBJECTIVES

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_DLR) of the prostate as compared to conventional T2w TSE imaging (T2_S).

MATERIALS AND METHODS

Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2_S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2_DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2_S was 4:12 min compared to 4:37 min for T2_DLR. 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_S versus T2_DLR was evaluated.

RESULTS

The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2_DLR with a median of 4 versus a median of 3 in T2_S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2_DLR as compared to T2_S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2_DLR versus T2_S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2_DLR in 29 cases as their preference.

CONCLUSIONS

Thin-slice T2_DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE_DL)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTE_DL and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTE_DL showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTE_DL provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.

Accelerated Three-dimensional T2-Weighted Turbo-Spin-Echo Sequences with Inner-Volume Excitation and Iterative Denoising in the Setting of Pelvis MRI at 1.5T: Impact on Image Quality and Lesion Detection

RATIONALE AND OBJECTIVES

To investigate image quality and rate of lesion detection in a novel three-dimensional T2-weighted turbo-spin-echo sequence with inner-volume excitation (zoomed imaging) and iterative denoising processing in pelvic MRI at 1.5T. Two-dimensional T2-weighted turbo-spin-echo sequences were used as the clinical reference standard (2D-T2-TSE).

MATERIALS AND METHODS

This is a prospective study of patients with various pelvic pathologies. Each patient underwent standard 2D-T2-TSE in three planes with two-fold acceleration as well as a single three-dimensional T2-TSE in the sagittal plane with four-fold acceleration known as Sampling-Perfection-with-Application-optimized-Contrast-using-different-flip-angle-Evolutions (3D-T2-SPACE). The 3D-T2-SPACE images were reconstructed in three orthogonal planes at a slice thickness of 2 mm (vs. 2D-T2-TSE at 4 mm). Two radiologists conducted a qualitative image analysis on standard 2D-T2-TSE and multiplanar reconstructed 3D-T2-SPACE images. These parameters were compared and inter-reader agreement was computed. Furthermore, each reader documented the observed lesions of various pelvic organs. The rate of lesion detection was compared between readers and sequences. Inter-reader and inter-sequence agreement were computed.

RESULTS

Forty patients (25 females) were included. Mean patient age was 58 ± 13 years. 3D-T2-SPACE enabled an approximate 22% reduction of acquisition time and 50% of reconstructed slice thickness. 3D-T2-SPACE showed fewer artifacts than 2D-T2-TSE (p < 0.001). However, 2D-T2-TSE was rated to have significantly higher signal intensity than 3D-T2-SPACE (p < 0.001). There were no significant differences between the two sequences regarding all other parameters. Inter-reader agreement regarding image quality parameters was substantial (Kappa = 0.772). For all analyzed pelvic anatomic structures, inter-reader and inter-sequence agreement for lesion detection was excellent (Kappa > 0.80).

CONCLUSION

3D-T2-SPACE with the inner-volume excitation and iterative denoising is clinically feasible at 1.5 T, enabling faster imaging, thinner slices, and significant reduction of artifacts. Despite that signal intensity was inferior in the SPACE images, overall image quality, diagnostic confidence and lesion detection were not compromised. This prospective study sets the stage for further clinical implementation and future investigations tailored to specific indications in pelvis MRI.

Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30−86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52−59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52−59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.

Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity

RATIONALE AND OBJECTIVES

To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBE_SR) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBE_SD).

MATERIALS AND METHODS

Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBE_SD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBE_SR and VIBE_SD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test.

RESULTS

A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBE_SR, breath-hold time was significantly reduced by approximately 13.6% (VIBE_SR 11.9 ± 1.2 seconds vs. VIBE_SD: 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBE_SR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBE_SR and VIBE_SD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBE_SR than in VIBE_SD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBE_SD and VIBE_SR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBE_SD and VIBE_SR.

CONCLUSION

The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique.

Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage

PURPOSE

 To investigate reduction of radiation exposure in unenhanced CT in suspicion of renal calculi using a tin-filtered high tube voltage protocol compared to a standard low-dose protocol without spectral shaping.

MATERIALS AND METHODS

 A phantom study using 7 human renal calculi was performed to test both protocols. 120 consecutive unenhanced CT examinations performed due to suspicion of renal calculi were included in this retrospective, monocentric study. 60 examinations were included with the standard-dose protocol (SP) (100 kV/130 mAs), whereas another 60 studies were included using a low-dose protocol (LD) applying spectral shaping with tin filtration of high tube voltages (Sn150 kV/80 mAs). Image quality was assessed by two radiologists in consensus blinded to technical parameters using an equidistant Likert scale ranging from 1-5 with 5 being the highest score. Quantitative image quality was assessed using regions of interest in abdominal organs, muscles, and adipose tissue to analyze image noise and signal-to-noise ratios (SNR). Commercially available dosimetry software was used to determine and compare effective dose (ED) and size-specific dose estimates (SSDEmean).

RESULTS

 All seven renal calculi of the phantom could be detected with both protocols. There was no difference regarding calcluli size between the two protocols except for the smallest one. The smallest concretion measured 1.5 mm in LD and 1.0 mm in SP (ground truth 1.5 mm). CTDIvol was 3.36 mGy in LD (DLP: 119.3 mGycm) and 8.27 mGy in SP (DLP: 293.6 mGycm). The mean patient age in SP was 47 ± 17 years and in LD 49 ± 13 years. Ureterolithiasis was found in 33 cases in SP and 32 cases in LD. The median concretion size was 3 mm in SP and 4 mm in LD. The median ED in LD was 1.3 mSv (interquartile range (IQR) 0.3 mSv) compared to 2.3 mSv (IQR 0.9 mSv) in SP (p < 0.001). The SSDEmean of LD was also significantly lower compared to SP with 2.4 mGy (IQR 0.4 mGy) vs. 4.8 mGy (IQR 2.3 mGy) (p < 0.001). The SNR was significantly lower in LD compared to SP (p < 0.001). However, there was no significant difference between SP and LD regarding the qualitative assessment of image quality with a median of 4 (IQR 1) for both groups (p = 0.648).

CONCLUSION

Tin-filtered unenhanced abdominal CT for the detection of renal calculi using high tube voltages leads to a significant reduction of radiation exposure and yields high diagnostic image quality without a significant difference compared to the institution's standard of care low-dose protocol without tin filtration.

KEY POINTS

  · Tin-filtered CT for the detection of renal calculi significantly reduces radiation dose.. · The application of tin filtration provides comparable diagnostic image quality to that of SP protocols.. · An increase in image noise does not hamper diagnostic image quality..

CITATION FORMAT

· Gassenmaier S, Winkelmann MT, Magnus J et al. Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1752-0472.

Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality

OBJECTIVES

The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradient echo imaging of the abdomen.

MATERIALS AND METHODS

Fifty consecutive patients who underwent a 1.5 T contrast-enhanced magnetic resonance imaging examination of the abdomen between April and May 2021 were included in this retrospective study. After acquisition of a conventional T1-weighted volumetric interpolated breath-hold examination using Dixon for water-fat separation (VIBEStd), the acquired data were reprocessed including a superresolution algorithm that was optimized for partial Fourier acquisitions (VIBESR). To accelerate theoretically the acquisition process, a more aggressive partial Fourier setting was applied in VIBESR reconstructions practically corresponding to a shorter acquisition for the data included in the retrospective reconstruction. Precontrast, dynamic contrast-enhanced, and postcontrast data sets were processed. Image analysis was performed by 2 radiologists independently in a blinded random order without access to clinical data regarding the following criteria using a Likert scale ranging from 1 to 4 with 4 being the best: noise levels, sharpness and contrast of vessels, sharpness and contrast of organs and lymph nodes, overall image quality, diagnostic confidence, and lesion conspicuity.Wilcoxon signed rank test for paired data was applied to test for significance.

RESULTS

Mean patient age was 61 ± 14 years. Mean acquisition time for the conventional VIBEStd sequence was 15 ± 1 seconds versus theoretical 13 ± 1 seconds of acquired data used for the VIBESR reconstruction. Noise levels were evaluated to be better in VIBESR with a median of 4 (4-4) versus a median of 3 (3-3) in VIBEStd by both readers (P < 0.001). Sharpness and contrast of vessels as well as organs and lymph nodes were also evaluated to be superior in VIBESR compared with VIBEStd with a median of 4 (4-4) versus a median of 3 (3-3) (P < 0.001). Diagnostic confidence was also rated superior in VIBESR with a median of 4 (4-4) versus a median of 3.5 (3-4) in VIBEStd by reader 1 and with a median of 4 (4-4) for VIBESR and a median of 4 (4-4) for VIBEStd by reader 2 (both P < 0.001).

CONCLUSIONS

Image enhancement using deep learning-based superresolution tailored to partial Fourier acquisitions of T1-weighted gradient echo imaging of the abdomen provides improved image quality and diagnostic confidence in combination with more aggressive partial Fourier settings leading to shorter scan time.

AI Lung Segmentation and Perfusion Analysis of Dual-Energy CT Can Help to Distinguish COVID-19 Infiltrates from Visually Similar Immunotherapy-Related Pneumonitis Findings and Can Optimize Radiological Workflows

(1) To explore the potential impact of an AI dual-energy CT (DECT) prototype on decision making and workflows by investigating its capabilities to differentiate COVID-19 from immunotherapy-related pneumonitis. (2) Methods: From 3 April 2020 to 12 February 2021, DECT from biometrically matching patients with COVID-19, pneumonitis, and inconspicuous findings were selected from our clinical routine. Three blinded readers independently scored each pulmonary lobe analogous to CO-RADS. Inter-rater agreement was determined with an intraclass correlation coefficient (ICC). Averaged perfusion metrics per lobe (iodine uptake in mg, volume without vessels in ml, iodine concentration in mg/mL) were extracted using manual segmentation and an AI DECT prototype. A generalized linear mixed model was used to investigate metric validity and potential distinctions at equal CO-RADS scores. Multinomial regression measured the contribution “Reader”, “CO-RADS score”, and “perfusion metrics” to diagnosis. The time to diagnosis was measured for manual vs. AI segmentation. (3) Results: We included 105 patients (62 ± 13 years, mean BMI 27 ± 2). There were no significant differences between manually and AI-extracted perfusion metrics (p = 0.999). Regardless of the CO-RADS score, iodine uptake and concentration per lobe were significantly higher in COVID-19 than in pneumonitis (p < 0.001). In regression, iodine uptake had a greater contribution to diagnosis than CO-RADS scoring (Odds Ratio (OR) = 1.82 [95%CI 1.10–2.99] vs. OR = 0.20 [95%CI 0.14–0.29]). The AI prototype extracted the relevant perfusion metrics significantly faster than radiologists (10 ± 1 vs. 15 ± 2 min, p < 0.001). (4) Conclusions: The investigated AI prototype positively impacts decision making and workflows by extracting perfusion metrics that differentiate COVID-19 from visually similar pneumonitis significantly faster than radiologists.

Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

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.

1.5 vs 3 Tesla Magnetic Resonance Imaging: A Review of Favorite Clinical Applications for Both Field Strengths-Part 2

ABSTRACT

The second part of this review deals with experiences in neuroradiological and pediatric examinations using modern magnetic resonance imaging systems with 1.5 T and 3 T, with special attention paid to experiences in pediatric cardiac imaging. In addition, whole-body examinations, which are widely used for diagnostic purposes in systemic diseases, are compared with respect to the image quality obtained in different body parts at both field strengths. A systematic overview of the technical differences at 1.5 T and 3 T has been presented in part 1 of this review, as well as several organ-based magnetic resonance imaging applications including musculoskeletal imaging, abdominal imaging, and prostate diagnostics.

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles

OBJECTIVE

Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE).

MATERIALS AND METHODS

First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants. Image quality and diagnostic confidence with 5 different FA evolutions of a HASTEDL were assessed to identify the most suitable FA evolution. Second, the identified FA evolution was implemented clinically and evaluated in 51 patients undergoing a clinically indicated liver magnetic resonance imaging at 3 T. Two radiologists assessed the HASTEDL and standard sequences regarding overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Comparative analyses were conducted to assess the differences between HASTEDL (acquisition time, 21 seconds; single breath-hold) and the routinely used T2-weighted BLADE sequence (acquisition time, 4 minutes; respiratory triggering).

RESULTS

From the volunteer study, the FA evolution characterized by the control points 130-90-110-130 degrees (HASTEDL) was identified as optimal among the 5 evolutions evaluated and was implemented in our clinical protocol. In all 51 patients, HASTEDL was successfully acquired at 3 T and showed excellent image quality (median, 4; interquartile range, 3-4). Although BLADE was rated significantly higher for overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence than HASTEDL, no differences were found concerning the number (n = 102) and measured diameter of the detected hepatic lesions between the 2 sequences BLADE and HASTEDL.

CONCLUSIONS

The proposed single-breath-hold abdominal HASTEDL with variable refocusing FAs is feasible at 3 T within SAR limits and yields high image quality and diagnostic confidence as compared with a standard T2-weighted acquisition technique, at a 10th of the acquisition time.

Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging

Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSE_S) and DL-based TSE sequences (TSE_DL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSE_S (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSE_S and TSE_DL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity

OBJECTIVES

The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE images (VIBESD).

METHODS

Between May and August 2020, a total of 46 patients with various abdominal pathologies underwent contrast-enhanced upper abdominal VIBE magnetic resonance imaging (MRI) at 1.5 T. After data acquisition, the precontrast and postcontrast T1-weighted VIBE raw data were processed by a deep learning-based prototype algorithm for deblurring and denoising the images as well as for enhancing their sharpness (VIBESR). In a randomized and blinded manner, 2 radiologists independently analyzed the image data sets using the unprocessed images VIBESD as a standard reference. Outcome measures were as follows: overall image quality, anatomic clarity of organ borders, sharpness of vessels, artifacts, noise, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. If the MRI examination encompassed a hepatic lesion, the maximum diameter of the largest hepatic lesion was quantified, and lesion sharpness and conspicuity were evaluated on an ordinal 4-point Likert scale. In addition, a post hoc regression analysis for lesion evaluation was computed. Finally, interrater/intrarater agreement was analyzed.

RESULTS

The overall image quality, anatomic clarity of organ borders, and sharpness of vessels in both precontrast and postcontrast images were rated significantly higher in VIBESR than in VIBESD (P < 0.001). Similarly, diagnostic confidence was higher in VIBESR than in VIBESD (P < 0.001). Furthermore, VIBESR images were rated to have significantly less noise and fewer artifacts in comparison with VIBESD (P < 0.001). The interreader agreement was substantial with a Cohen κ of 0.72 for the precontrast analysis and a κ of 0.74 for the postcontrast analysis. A total of 28 hepatic lesions were analyzed. For both readers, lesion sharpness and conspicuity were rated significantly better in VIBESR than in VIBESD in both the precontrast and postcontrast data sets (P < 0.01), which was consistent with the post hoc regression analysis (for every 1-point increase in sharpness/conspicuity, the odds ratio revealed a positive relation with VIBESR of 13-fold to 17-fold in comparison with VIBESD; P < 0.001). In terms of lesion size, there was no significant difference between the precontrast VIBESD and VIBESR or between the postcontrast VIBESD and VIBESR for both readers. Similarly, there was an excellent interreader agreement regarding lesion size (intraclass correlation coefficient, >0.9).

CONCLUSIONS

The data-driven superresolution reconstruction (VIBESR) is clinically feasible for precontrast and postcontrast upper abdominal VIBE MRI, providing improved image quality, diagnostic confidence, and lesion conspicuity compared with standard VIBESD images.

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

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 (T2_S), the novel T2 TSE sequence with DLR (T2_DLR) was applied in three planes. Overall, the acquisition time for T2_S resulted in 10:21 min versus 3:50 min for T2_DLR. 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 T2_DLR versus T2_S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2_DLR versus T2_S in all three acquisition planes (p = 0.005-<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2_DLR with a median of 4 versus a median of 3 in T2_S (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.

Quantification of liver and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence

PURPOSE

To investigate the feasibility of liver fat quantification in contrast-enhanced dual source dual energy computed tomography (DECT) using multi-echo Dixon magnetic resonance imaging (MRI) as reference standard.

METHOD

Patients who underwent MRI of the liver including a multi-echo Dixon sequence for estimation of proton density fat fraction in 2017 as well as contrast-enhanced DECT imaging of the abdomen were included in this retrospective, monocentric IRB approved study. Furthermore, patients with a hepatic fat amount >5% who were examined in 2018 with MRI and DECT were included. The final study group consisted of 81 patients with 90 pairs of examinations. Analysis of parameter maps was performed manually using congruent regions of interest which were placed in the liver parenchyma, in the erector spinae muscles, and psoas major muscles.

RESULTS

Mean patient age was 61 ± 13 years. Median time between MRI and DECT was 48 days. MRI liver fat quantification resulted in a median of 3.8% (IQR: 2.2-8.2%) compared to 1.8% (IQR: 0-6.3%) in DECT (p < 0.001), with a Spearman correlation of 0.73. Bland-Altman analysis resulted in a systematic underestimation of liver fat in DECT, with a mean difference of -1.7%. Fat quantification in the erector spinae muscles (p = 0.257) and the psoas major muscles (p = 0.208) was not significantly different in DECT compared to MRI.

CONCLUSIONS

Liver and muscular fat quantification in portal-venous phase DECT is feasible with good to excellent correlation compared to a multi-echo Dixon MRI sequence analysis. While there is an underestimation of the liver fat content in DECT, there are no significant differences between DECT and MRI fat quantification of the erector spinae and psoas major muscles.

Image Quality Improvement of Dynamic Contrast-Enhanced Gradient Echo Magnetic Resonance Imaging by Iterative Denoising and Edge Enhancement

OBJECTIVES

The aim of this study was to investigate the impact of a novel edge enhancement and iterative denoising algorithm in 1.5-T T1-weighted dynamic contrast-enhanced (DCE) gradient echo (GRE) magnetic resonance imaging of the abdomen on image quality, noise levels, diagnostic confidence, and lesion detectability.

MATERIALS AND METHODS

Fifty patients who underwent a clinically indicated magnetic resonance imaging with DCE imaging of the abdomen between June and August 2020 were included in this retrospective, monocentric, institutional review board-approved study. For DCE imaging, a series of 3 volume interpolated breath-hold examinations (VIBEs) was performed. The raw data of all DCE imaging studies were processed twice, once using standard reconstruction (DCES) and again using an edge enhancement and iterative denoising approach (DCEDE). All imaging studies were randomly reviewed by 2 radiologists independently regarding noise levels, arterial contrast, sharpness of vessels, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Furthermore, lesion detectability was evaluated using the same ranking system.

RESULTS

All 50 imaging studies were successfully reconstructed with both methods. Interreader agreement (Cohen κ) was substantial to perfect for both readers. Arterial contrast and sharpness of vessels were rated superior by both readers with a median of 4 in DCEDE versus a median of 3 in DCES (P < 0.001). Furthermore, noise levels as well as overall image quality were rated higher with a median of 4 in DCEDE compared with a median of 3 in DCES (P < 0.001). Lesion detectability was evaluated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001). Consequently, diagnostic confidence was also rated to be superior in DCEDE with a median of 4 versus DCES with a median of 3 (P < 0.001).

CONCLUSIONS

Iterative denoising and edge enhancement are feasible in DCE imaging of the abdomen providing superior arterial contrast, noise levels, and overall image quality. Furthermore, lesion detectability and diagnostic confidence were significantly improved using this novel reconstruction method. Further reduction of acquisition time might be possible via reduction of increased noise levels using this presented method.

123Iodine-metaiodobenzylguanidine scintigraphy versus whole-body magnetic resonance imaging with diffusion-weighted imaging in children with high-risk neuroblastoma - pilot study

BACKGROUND

The prognostic value of the International Society of Paediatric Oncology European Neuroblastoma Research Network (SIOPEN) skeletal score using ¹²³iodine-metaiodobenzylguanidine (MIBG) has been confirmed for people with high-risk neuroblastoma. Whole-body MRI with diffusion-weighted imaging is used increasingly.

OBJECTIVE

To compare the original SIOPEN score and its adaption by diffusion-weighted imaging in high-risk stage 4 neuroblastoma and to evaluate any consequences of score differences on overall survival.

MATERIALS AND METHODS

This retrospective observational study included pediatric patients who underwent MIBG scintigraphy and whole-body MRI, including diffusion-weighted imaging, between 2010 and 2015. Semi-quantitative skeletal scores for each exam were calculated independently. A difference of two or more points was defined as clinically relevant and counted as M+ (more in diffusion-weighted imaging) or S+ (more in MIBG). In cases of a negative result in one of the studies, residual disease of 1 point was also rated as relevant. We tested correlation and differences on an exam basis and also grouped by different therapeutic conditions. Overall survival was used to evaluate prognostic relevance.

RESULTS

Seventeen children with 25 paired examinations were evaluated. Median MIBG scintigraphy score was 0 (interquartile range [IQR] 0-4, range 0-25) vs. a median whole-body MRI score of 1 (IQR 0-5.5, range 0-35) (P=0.018). A relevant difference between whole-body MRI and MIBG scintigraphy was noted in 14 of the 25 paired examinations (M+: n=9; S+: n=5). After treatment, the median survival of cases with M+ was 14 months (IQR 4-59, range 1-74 months), while S+ cases showed a median survival of 49 months (IQR 36-52, range 36-52 months) (P=0.413).

CONCLUSION

The SIOPEN scoring system is feasible for whole-body MRI but might result in slightly higher scores, probably because of MRI's superior spatial resolution. Further studies are necessary to validate any impact on prognosis.

Prevalence of pathological FFRCT values without coronary artery stenosis in an asymptomatic marathon runner cohort

OBJECTIVES

To evaluate computed tomography fractional flow reserve (FFR_CT) values in distal parts of the coronaries in an asymptomatic cohort of marathon runners without any coronary stenosis for potentially false-positive values.

METHODS

Ninety-eight asymptomatic male marathon runners (age 53 ± 7 years) were enrolled in a prospective monocentric study and underwent coronary computed tomography angiography (CCTA). CCTA data were analyzed for visual coronary artery stenosis. FFR_CT was evaluated in 59 participants without coronary artery stenosis in proximal, mid, and distal coronary sections using an on-site software prototype.

RESULTS

In participants without coronary artery stenosis, abnormal FFR_CT values ≤ 0.8 in distal segments were found in 22 participants (37%); in 19 participants in the LAD; in 5 participants in the LCX; and in 4 participants in the RCA. Vessel diameters in participants with FFR_CT values > 0.80 compared to ≤ 0.80 were 1.6 ± 0.3 mm versus 1.5 ± 0.3 mm for distal LAD (p = 0.025), 1.8 ± 0.3 mm versus 1.6 ± 0.5 mm for distal LCX (p = 0.183), and 2.0 ± 0.4 mm versus 1.5 ± 0.2 mm for distal RCA (p < 0.001).

CONCLUSIONS

Abnormal FFR_CT values of ≤ 0.8 frequently occurred in distal coronary segments in subjects without any anatomical coronary artery stenosis. This effect is only to some degree explainable by small distal vessel diameters. Therefore, the validity of hemodynamic relevance evaluation using FFR_CT in distal coronary artery segment stenosis is reduced.

KEY POINTS

• Abnormal FFR_CT values (≤ 0.8) occurred in over a third of the subjects in the distal LAD despite the absence of coronary artery stenosis.. • Therefore, the validity of hemodynamic relevance evaluation in distal coronary artery segment stenosis is reduced. • Decision-making based on abnormal FFR_CT values in distal vessel sections should be performed with caution and only in combination with visual assessment of the grade of stenosis..

Cardiac MRI findings to differentiate athlete's heart from hypertrophic (HCM), arrhythmogenic right ventricular (ARVC) and dilated (DCM) cardiomyopathy

To provide clinically relevant criteria for differentiation between the athlete’s heart and similar appearing hypertrophic (HCM), dilated (DCM), and arrhythmogenic right-ventricular cardiomyopathy (ARVC) in MRI. 40 top-level athletes were prospectively examined with cardiac MR (CMR) in two university centres and compared to retrospectively recruited patients diagnosed with HCM (n = 14), ARVC (n = 18), and DCM (n = 48). Analysed MR imaging parameters in the whole study cohort included morphology, functional parameters and late gadolinium enhancement (LGE). Mean left-ventricular enddiastolic volume index (LVEDVI) was high in athletes (105 ml/m²) but significantly lower compared to DCM (132 ml/m²; p = 0.001). Mean LV ejection fraction (EF) was 61% in athletes, below normal in 7 (18%) athletes vs. EF 29% in DCM, below normal in 46 (96%) patients (p < 0.0001). Mean RV-EF was 54% in athletes vs. 60% in HCM, 46% in ARVC, and 41% in DCM (p < 0.0001). Mean interventricular myocardial thickness was 10 mm in athletes vs. 12 mm in HCM (p = 0.0005), 9 mm in ARVC, and 9 mm in DCM. LGE was present in 1 (5%) athlete, 8 (57%) HCM, 10 (56%) ARVC, and 21 (44%) DCM patients (p < 0.0001). Healthy athletes’ hearts are characterized by both hypertrophy and dilation, low EF of both ventricles at rest, and increased interventricular septal thickness with a low prevalence of LGE. Differentiation of athlete’s heart from other non-ischemic cardiomyopathies in MRI can be challenging due to a significant overlap of characteristics also seen in HCM, ARVC, and DCM.

Application of a Novel Iterative Denoising and Image Enhancement Technique in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of the Abdomen: Improvement of Image Quality and Diagnostic Confidence

OBJECTIVES

The aim of this study was to investigate the impact of a novel iterative denoising and image enhancement technique in T1-weighted precontrast and postcontrast volume-interpolated breath-hold examination (VIBE) of the abdomen on image quality, noise levels, and diagnostic confidence without change of acquisition parameters.

MATERIALS AND METHODS

Fifty patients were included in this retrospective, monocentric, institutional review board-approved study after clinically indicated magnetic resonance imaging of the abdomen including T1-weighted precontrast and postcontrast imaging. After acquisition of the standard VIBE (VIBES), images were processed with a novel reconstruction algorithm using the same raw data as for VIBES, resulting in a denoised and enhanced dataset (VIBEDE). Two different radiologists evaluated both datasets in a randomized order regarding sharpness of organs as well as vessels, noise levels, artifacts, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4 with 4 being the best. Furthermore, in the presence of focal liver lesions, the largest lesion was measured in the postcontrast dataset, and lesion detectability was analyzed using a Likert scale (1-4).

RESULTS

Precontrast and postcontrast sharpness of organs and sharpness of vessels were rated significantly superior by both readers in VIBEDE with a median of 4 (interquartile range, 0) compared with VIBES with a median of 3 (1) (all P's < 0.0001). Precontrast and postcontrast noise levels were also rated superior by both readers in VIBEDE with a median of 4 (0) compared with VIBES with a median of 3 (1) for precontrast and a median of 3 (0) (median of 3 [1] for reader 2) for postcontrast imaging (all P's < 0.0001).Overall image quality was also rated higher with a median of 4 (0) in VIBEDE versus 3 (1) in VIBES (P < 0.0001). Twenty-seven imaging studies contained liver lesions. There was no difference regarding the number and localization between the readers and between VIBES and VIBEDE. Lesion detectability was rated by both readers significantly better in VIBEDE with a median of 4 (0) compared with a median of 4 (1) for reader 1 and a median of 3 (1) for reader 2 (P = 0.001 for reader 1; P < 0.001 for reader 2). Consequently, diagnostic confidence was also significantly superior in VIBEDE versus VIBES with a median of 4 (0) for both (P = 0.001). Interreader agreement resulted in a Cohen κ of 0.76 for precontrast analysis as well as of 0.76 for postcontrast analysis.

CONCLUSIONS

Application of a novel iterative denoising and image enhancement technique in T1-weighted VIBE precontrast and postcontrast imaging of the abdomen is feasible, providing superior image quality, noise levels, and diagnostic confidence.

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold

OBJECTIVE

The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE).

MATERIALS AND METHODS

Sixty-six patients who underwent 1.5-T liver magnetic resonance imaging were included in this monocentric, retrospective study. The following T2-weighted sequences in axial orientation and using spectral fat suppression were compared: a conventional respiratory-triggered BLADE sequence (time of acquisition [TA] = 4:00 minutes), a conventional multiple breath-hold HASTE sequence (HASTES) (TA = 1:30 minutes), as well as a single breath-hold HASTE with deep learning reconstruction (HASTEDL) (TA = 0:16 minutes). Two radiologists assessed the 3 sequences regarding overall image quality, noise, sharpness, diagnostic confidence, and lesion detectability as well as lesion characterization using a Likert scale ranging from 1 to 4 with 4 being the best. Comparative analyses were conducted to assess the differences between the 3 sequences.

RESULTS

HASTEDL was successfully acquired in all patients. Overall image quality for HASTEDL was rated as good (median, 3; interquartile range, 3-4) and was significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.001). Noise, sharpness, and artifacts for HASTEDL reached similar levels to BLADE (P ≤ 0.176) and were significantly superior to HASTEs (P < 0.001). Diagnostic confidence for HASTEDL was rated excellent by both readers and significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.044). Lesion detectability and lesion characterization for HASTEDL reached similar levels to those of BLADE (P ≤ 0.523) and were significantly superior to HASTEs (P < 0.001). Concerning the number of detected lesions and the measured diameter of the largest lesion, no significant differences were found comparing BLADE, HASTES, and HASTEDL (P ≤ 0.912).

CONCLUSIONS

The single breath-hold HASTEDL is feasible and yields comparable image quality and diagnostic confidence to standard T2-weighted TSE BLADE and may therefore allow for a remarkable time saving in abdominal imaging.

Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality

PURPOSE

To introduce a novel deep learning (DL) T2-weighted TSE imaging (T2_DL) sequence in prostate MRI and investigate its impact on examination time, image quality, diagnostic confidence, and PI-RADS classification compared to standard T2-weighted TSE imaging (T2_S).

METHOD

Thirty patients who underwent multiparametric MRI (mpMRI) of the prostate due to suspicion of prostatic cancer were included in this retrospective study. Standard sequences were acquired consisting of T1- and T2-weighted imaging and diffusion-weighted imaging as well as the novel T2_DL. Axial acquisition time of T2_S was 4:37 min compared to 1:38 min of T2_DL. Two radiologists independently evaluated all imaging datasets in a blinded reading regarding image quality, lesion detectability, and diagnostic confidence using a Likert-scale ranging from 1 to 4 with 4 being the best. T2 score as well as PI-RADS score were obtained for the most malignant lesion.

RESULTS

Mean patient age was 65 ± 11 years. Noise levels and overall image quality were rated significantly superior by both readers with a median of 4 in T2_DL compared to a median of 3 in T2_S (all p < 0.001). Lesion detectability was also rated higher in T2_DL by both readers with a median of 4 versus a median of 3 in T2_S (p = 0.005 and <0.001, respectively). There was no difference regarding PI-RADS scoring between T2_DL and T2_S affecting patient management.

CONCLUSIONS

Deep learning axial T2w TSE imaging of the prostate is feasible with reduction of examination time of 65 % compared to standard imaging and improvement of image quality and lesion detectability.

Feasibility and possible value of quantitative semi-automated diffusion weighted imaging volumetry of neuroblastic tumors

Background

To assess the feasibility and possible value of semi-automated diffusion weighted imaging (DWI) volumetry of whole neuroblastic tumors with apparent diffusion coefficient (ADC) map evaluation after neoadjuvant chemotherapy.

Methods

Pediatric patients who underwent surgical resection of neuroblastic tumors at our institution from 2013 to 2019 and who received a preoperative MRI scan with DWI after chemotherapy were included. Tumor volume was assessed with a semi-automated approach in DWI using a dedicated software prototype. Quantitative ADC values were calculated automatically of the total tumor volume after manual exclusion of necrosis. Manual segmentation in T1 weighted and T2 weighted sequences was used as reference standard for tumor volume comparison. The Student’s t test was used for parametric data while the Wilcoxon rank sum test and the Kruskal-Wallis test were applied for non-parametric data.

Results

Twenty seven patients with 28 lesions (neuroblastoma (NB): n = 19, ganglioneuroblastoma (GNB): n = 7, ganglioneuroma (GN): n = 2) could be evaluated. Mean patient age was 4.5 ± 3.2 years. Median volume of standard volumetry (T1w or T2w) was 50.2 ml (interquartile range (IQR): 91.9 ml) vs. 45.1 ml (IQR: 98.4 ml) of DWI (p = 0.145). Mean ADC values (× 10^− 6 mm²/s) of the total tumor volume (without necrosis) were 1187 ± 301 in NB vs. 1552 ± 114 in GNB/GN (p = 0.037). The 5th percentile of ADC values of NB (614 ± 275) and GNB/GN (1053 ± 362) provided the most significant difference (p = 0.007) with an area under the curve of 0.848 (p < 0.001).

Conclusions

Quantitative semi-automated DWI volumetry is feasible in neuroblastic tumors with integrated analysis of tissue characteristics by providing automatically calculated ADC values of the whole tumor as well as an ADC heatmap. The 5th percentile of the ADC values of the whole tumor volume proved to be the most significant parameter for differentiation of the histopathological subtypes in our patient cohort and further investigation seems to be worthwhile.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-020-00366-3.

Dual-Energy Computed Tomography of the Lung in COVID-19 Patients: Mismatch of Perfusion Defects and Pulmonary Opacities

To evaluate contrast-enhanced dual-energy computed tomography (DECT) chest examinations regarding pulmonary perfusion patterns and pulmonary opacities in patients with confirmed COVID-19 disease. Fourteen patients with 24 DECT examinations performed between April and May 2020 were included in this retrospective study. DECT studies were assessed independently by two radiologists regarding pulmonary perfusion defects, using a Likert scale ranging from 1 to 4. Furthermore, in all imaging studies the extent of pulmonary opacities was quantified using the same rating system as for perfusion defects. The main pulmonary findings were ground glass opacities (GGO) in all 24 examinations and pulmonary consolidations in 22 examinations. The total lung scores after the addition of the scores of the single lobes showed significantly higher values of opacities compared to perfusion defects, with a median of 12 (9-18) for perfusion defects and a median of 17 (15-19) for pulmonary opacities (p = 0.002). Furthermore, mosaic perfusion patterns were found in 19 examinations in areas with and without GGO. Further studies will be necessary to investigate the pathophysiological background of GGO with maintained perfusion compared to GGO with reduced perfusion, especially regarding long-term lung damage and prognosis.

Retrospective accuracy analysis of MRI based lesion size measurement in neuroblastic tumors: which sequence should we choose?

BACKGROUND

MR imaging of neuroblastic tumors is widely used for assessing the effect of chemotherapy on tumor size. However, there are some concerns that MRI might falsely estimate lesion diameters due to calcification and fibrosis. Therefore, the aim of our study was to compare neuroblastic tumor size based on MRI measurements to histopathology measurements of the resected specimens as standard of reference.

METHODS

Inclusion criteria were diagnosis of a neuroblastic tumor, MR imaging within 100 days to surgery and gross total resection without fragmentation of the tumor between 2008 and 2019. Lesion diameters were measured by two radiologists according to RECIST 1.1 in axial plane in T2w turbo spin echo (TSE), diffusion-weighted imaging (DWI), and in T1w pre- and postcontrast sequences. Furthermore, the largest lesion size in three-dimensions was noted. The largest diameter of histopathology measurements of each specimen was used for comparison with MRI.

RESULTS

Thirty-seven patients (mean age: 5 ± 4 years) with 38 lesions (neuroblastoma: n = 17; ganglioneuroblastoma: n = 11; ganglioneuroma: n = 10) were included in this retrospective study. There was excellent intra-class correlation coefficient between both readers for all sequences (> 0.9) Tumor dimensions of reader 1 based on axial MRI measurements were significantly smaller with the following median differences (cm): T1w precontrast - 1.4 (interquartile range (IQR): 1.8), T1w postcontrast - 1.0 (IQR: 1.9), T2w TSE: -1.0 (IQR: 1.6), and DWI -1.3 (IQR: 2.2) (p < 0.001 for all sequences). However, the evaluation revealed no significant differences between the three-dimensional measurements and histopathology measurements of the resected specimens regardless of the applied MRI sequence.

CONCLUSIONS

Axial MRI based lesion size measurements are significantly smaller than histopathological measurements. However, there was no significant difference between three-dimensional measurements and histopathology measurements of the resected specimens. T2w TSE and T1w postcontrast images provided the lowest deviation and might consequently be preferred for measurements.

Forensic age estimation in living adolescents with CT imaging of the clavicula-impact of low-dose scanning on readers' confidence

Objectives

Computed tomography (CT) imaging of the clavicula displays the reference standard for forensic bone age diagnostics in adolescents and young adults. Consequently, highest efforts on radiation reduction are warranted. Therefore, the aim of this study was to investigate the feasibility of low-dose (LD) CT imaging of the clavicula for age estimation in living adolescents.

Methods

A total of 207 non-contrast chest CT of 144 patients born between 1988 and 2012, performed in 2018 due to various clinical indications, were included in this retrospective study. The mean patient age was 16.9 ± 6.6 years. Patients were divided into a LD (n = 146) and standard-dose (SD; n = 61) group. Image quality, confidence levels, and ossification stages (using the 5-stage classification including the subgroups 2a–3c) were assessed by two radiologists independently. Radiation dose was determined via dosimetry software.

Results

Dose simulation with z-axis reduction to depict the clavicula only resulted in a median exposure of 0.1 mSv (IQR: 0.0) in LD compared with 0.9 mSv (IQR: 0.6) in SD (p < 0.001). The median image quality was rated by both readers significantly worse in LD compared with SD on a Likert scale ranging from 1 to 4 with a median of 3 (IQR: 1) versus 4 (IQR: 0; p < 0.001 for both readers). There was an almost perfect agreement for the ossification stages between both readers with a Cohen’s kappa of 0.83 (p < 0.001). Median confidence levels of both readers were not significantly different between LD and SD in the decisive subgroups 2a–3c.

Conclusions

Low-dose CT imaging of the clavicula for age estimation in living adolescents is possible without loss of readers’ confidence.

Key Points

• Radiological bone age diagnostics in young delinquents with unknown exact chronological age is important as the judicial systems differentiate between youths and adults.

• Low-dose computed tomography scanning of the medial clavicular joint for forensic age estimation is feasible in living adolescents without loss of readers’ confidence.

• Sufficient image quality of the medial clavicular joint for forensic bone age diagnostics in living adolescents is achievable using a median dose of 0.1 mSv.

Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques

BACKGROUND

The influence of computed tomography (CT) reconstruction algorithms on the performance of machine-learning-based CT-derived fractional flow reserve (CT-FFR_ML) has not been investigated. CT-FFR_ML values and processing time of two reconstruction algorithms were compared using an on-site workstation.

METHODS

CT-FFR_ML was computed on 40 coronary CT angiography (CCTA) datasets that were reconstructed with both iterative reconstruction in image space (IRIS) and filtered back-projection (FBP) algorithms. CT-FFR_ML was computed on a per-vessel and per-segment basis as well as distal to lesions with ≥50% stenosis on CCTA. Processing times were recorded. Significant flow-limiting stenosis was defined as invasive FFR and CT-FFR_ML values ≤ 0.80. Pearson's correlation, Wilcoxon, and McNemar statistical testing were used for data analysis.

RESULTS

Per-vessel analysis of IRIS and FBP reconstructions demonstrated significantly different CT-FFR_ML values (p ≤ 0.05). Correlation of CT-FFR_ML values between algorithms was high for the left main (r = 0.74), left anterior descending (r = 0.76), and right coronary (r = 0.70) arteries. Proximal and middle segments showed a high correlation of CT-FFR_ML values (r = 0.73 and r = 0.67, p ≤ 0.001, respectively), despite having significantly different averages (p ≤ 0.05). No difference in diagnostic accuracy was observed (both 81.8%, p = 1.000). Of the 40 patients, 10 had invasive FFR results. Per-lesion correlation with invasive FFR values was moderate for IRIS (r = 0.53, p = 0.117) and FBP (r = 0.49, p = 0.142). Processing time was significantly shorter using IRIS (15.9 vs. 19.8 min, p ≤ 0.05).

CONCLUSION

CT reconstruction algorithms influence CT-FFR_ML analysis, potentially affecting patient management. Additionally, iterative reconstruction improves CT-FFR_ML post-processing speed.

Quantitative inversion time prescription for myocardial late gadolinium enhancement using T1-mapping-based synthetic inversion recovery imaging: reducing subjectivity in the estimation of inversion time

To develop a quantitative T1-mapping-based synthetic inversion recovery (IR_synth) approach to calculate the optimal inversion time (TI₀) for late gadolinium enhancement (LGE) imaging. Prospectively enrolled patients (n = 130, 58 ± 16 years) underwent cardiac MRI on a 1.5T system including Look-Locker TI-scout (LL), modified LL IR (MOLLI)-based T1-mapping, and LGE acquisitions. Patients were randomized into two groups: LL group (TI-scout followed T1-mapping) or MOLLI group (T1-mapping followed TI-scout). In both groups, the second acquisition was used to determine the TI₀ for LGE. IR_synth images were generated from T1-maps between TI = 200-400 ms in 5 ms increments. Image quality was rated on a 3-point scale and the remote/background signal intensity ratio (SIR) was calculated. In the LL group (n = 53), the TI-scout-based TI₀ was significantly shorter compared to IR_synth [230 ms (219-242) vs. 280 ms (263-297), P < 0.0001]. The TI₀ used for LGE was set 30-40 ms longer [261 ms (247-276), P < 0.0001] than the TI-scout-based TI₀, resulting in a TI₀ ~ 20 ms shorter than what was obtained by IR_synth (P = 0.0156). In the MOLLI group (n = 63), IR_synth-based TI₀ was significantly longer than the TI-scout-based TI₀ [298 ms (262-334) vs. 242 ms (217-267), P = 0.0313]. The quality of myocardial nulling was rated higher [2.4 (2.2-2.5) vs. 2.0 (1.8-2.1), P = 0.0042] and the remote/background SIR was found to be more optimal (1.6 [1.1-2.1] vs. 2.6 [1.8-3.3], P = 0.0256) in the MOLLI group. T1-based IR_synth selects TI₀ for LGE more accurately than conventional TI-scout imaging. IR_synth improves TI₀ selection by providing excellent visualization of the representative image contrast for LGE images, reducing operator dependence in LGE acquisition.

Structured reporting of MRI of the shoulder - improvement of report quality?

OBJECTIVES

To evaluate the effect of structured reports (SRs) in comparison to non-structured narrative free text (NRs) shoulder MRI reports and potential effects of both types of reporting on completeness, readability, linguistic quality and referring surgeons' satisfaction.

METHODS

Thirty patients after trauma or with suspected degenerative changes of the shoulder were included in this study (2012-2015). All patients underwent shoulder MRI for further assessment and possible surgical planning. NRs were generated during clinical routine. Corresponding SRs were created using a dedicated template. All 60 reports were evaluated by two experienced orthopaedic shoulder surgeons using a questionnaire that included eight questions.

RESULTS

Eighty per cent of the SRs were fully complete without any missing key features whereas only 45% of the NRs were fully complete (p < 0.001). The extraction of information was regarded to be easy in 92% of the SRs and 63% of the NRs. The overall quality of the SRs was rated better than that of the NRs (p < 0.001).

CONCLUSIONS

Structured reporting of shoulder MRI improves the readability as well as the linguistic quality of radiological reports, and potentially leads to a higher satisfaction of referring physicians.

KEY POINTS

• Structured MRI reports of the shoulder improve readability. • Structured reporting facilitates information extraction. • Referring physicians prefer structured reports to narrative free text reports. • Structured MRI reports of the shoulder can reduce radiologist re-consultations.