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 (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.

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Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

10.21203/rs.3.rs-153369/v1 ◽

2021 ◽

Author(s):

Judith Herrmann ◽

Sebastian Gassenmaier ◽

Thomas Kuestner ◽

Matthias Kuendel ◽

Dominik Nickel ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Acquisition Time ◽

Clinical Implementation ◽

Clinical Workflow ◽

Diagnostic Confidence ◽

Clinical Routine ◽

Time Saving ◽

Reconstruction Process

Abstract Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainable image quality compared to conventional TSE.Methods: DL image reconstruction using a variational network was clinically implemented to enable acquisition of accelerated TSE sequences. After internal review board’s approval and informed consent, 30 examinations for knee, shoulder, and lumbar spine in 15 volunteers at 3 T were included in this prospective study. Conventional TSE sequences (TSE) and TSE with deep learning reconstruction (TSEDL) were compared regarding overall image quality, noise, sharpness, and subjective signal-to-noise-ratio (SNR), as well diagnostic confidence and image impression. Comparative analyses were conducted to assess the differences between the sequences. A survey on technologists’ acceptance was performed for DL image reconstruction. Results: DL image reconstruction was successfully implemented in a clinical workflow and TSEDL allowed a remarkable time saving of more than 50%. Overall image quality, diagnostic confidence and image impression for TSEDL were rated as excellent (median 4, IQR 4-4) and comparable to TSE (image quality: p=0.059, diagnostic confidence: p=0.157, image impression: p=0.102). Noise, sharpness, artifacts, and subjective SNR for TSEDL reached significantly superior levels to TSE (noise: p<0.001, sharpness: p=0.001, artifacts: p=0.014, subjective SNR: p<0.001). Technologists reported high levels of acceptance for DL image reconstruction. Required time for the reconstruction process was rated moderate and longer than standard sequences (median 2, IQR 2-3). Required time and effort for the implementation in daily workflow was rated as low effort (median 4, IQR 3-4). General applicability of DL reconstruction as well as acceptance of DL sequences in clinical routine were rated excellent (median 4, IQR 3-4). Conclusion: DL image reconstruction for TSE sequences can be implemented in clinical workflow and enables a remarkable time saving (>50%) in image acquisition while maintaining excellent image quality.Trial registration: Your clinical trial is officially registered at the German DRKS with the registration number: DRKS00023278.

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Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Journal of X-Ray Science and Technology ◽

10.3233/xst-211033 ◽

2021 ◽

pp. 1-8

Author(s):

Jihang Sun ◽

Haoyan Li ◽

Haiyun Li ◽

Michelle Li ◽

Yingzi Gao ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Ct Angiography ◽

Takayasu Arteritis ◽

Image Noise ◽

Diagnostic Confidence ◽

Ct Number ◽

Laboratory Test Results ◽

Significant Difference

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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Prospective Image Quality and Lesion Assessment in the Setting of MR-Guided Radiation Therapy of Prostate Cancer on an MR-Linac at 1.5 T: A Comparison to a Standard 3 T MRI

Cancers ◽

10.3390/cancers13071533 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1533

Author(s):

Haidara Almansour ◽

Saif Afat ◽

Victor Fritz ◽

Fritz Schick ◽

Marcel Nachbar ◽

...

Keyword(s):

Prostate Cancer ◽

Radiation Therapy ◽

Image Quality ◽

Outcome Measures ◽

Quality Criteria ◽

Lesion Size ◽

Geometric Distortion ◽

Diagnostic Confidence ◽

Prostate Imaging ◽

Lesion Conspicuity

The objective of this study is to conduct a qualitative and a quantitative image quality and lesion evaluation in patients undergoing MR-guided radiation therapy (MRgRT) for prostate cancer on a hybrid magnetic resonance imaging and linear accelerator system (MR-Linac or MRL) at 1.5 Tesla. This prospective study was approved by the institutional review board. A total of 13 consecutive patients with biopsy-confirmed prostate cancer and an indication for MRgRT were included. Prior to radiation therapy, each patient underwent an MR-examination on an MRL and on a standard MRI scanner at 3 Tesla (MRI3T). Three readers (two radiologists and a radiation oncologist) conducted an independent qualitative and quantitative analysis of T2-weighted (T2w) and diffusion-weighted images (DWI). Qualitative outcome measures were as follows: zonal anatomy, capsule demarcation, resolution, visibility of the seminal vesicles, geometric distortion, artifacts, overall image quality, lesion conspicuity, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. Lesion conspicuity and diagnostic confidence were firstly analyzed only on MRL. Afterwards, these outcome parameters were analyzed in consensus with the MRI3T. Quantitative outcome measures were as follows: anteroposterior and right left diameter of the prostate, lesion size, PI-RADS score (Prostate Imaging—Reporting and Data System) and apparent diffusion coefficient (ADC) of the lesions. Intergroup comparisons were computed using the Wilcoxon-sign rank test and t tests. A post-hoc regression analysis was computed for lesion evaluation. Finally, inter-/intra-reader agreement was analyzed using the Fleiss kappa and intraclass correlation coefficient. For T2w images, the MRL showed good results across all quality criteria (median 3 and 4). Furthermore, there were no significant differences between MRL and MRI3T regarding capsule demarcation or geometric distortion. For the DWI, the MRL performed significantly less than MRI3T across most image quality criteria with a median ranging between 2 and 3. However, there were no significant differences between MRL and MRI3T regarding geometric distortion. In terms of lesion conspicuity and diagnostic confidence, inter-reader agreement was fair for MRL alone (Kappa = 0.42) and good for MRL in consensus with MRI3T (Kappa = 0.708). Thus, lesion conspicuity and diagnostic confidence could be significantly improved when reading MRL images in consensus with MRI3T (Odds ratio: 9- to 11-fold for the T2w images and 5- to 8–fold for the DWI) (p < 0.001). For measures of lesion size, anterior-posterior and right-left prostate diameter, inter-reader and intersequence agreement were excellent (ICC > 0.90) and there were no significant differences between MRL and MRI3T among all three readers. In terms of Prostate Imaging Reporting and Data System (PIRADS) scoring, no significant differences were observed between MRL and MRI3T. Finally, there was a significant positive linear relationship between lesion ADC measurements (r = 0.76, p < 0.01) between the ADC values measured on both systems. In conclusion, image quality for T2w was comparable and diagnostic even without administration of spasmolytic- or contrast agents, while DWI images did not reach diagnostic level and need to be optimized for further exploitation in the setting of MRgRT. Diagnostic confidence and lesion conspicuity were significantly improved by reading MRL in consensus with MRI3T which would be advisable for a safe planning and treatment workflow. Finally, ADC measurements of lesions on both systems were comparable indicating that, lesion ADC as measured on the MRL could be used as a biomarker for evaluation of treatment response, similar to examinations using MRI3T.

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

Diagnostics ◽

10.3390/diagnostics11122181 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2181

Author(s):

Sebastian Gassenmaier ◽

Thomas Küstner ◽

Dominik Nickel ◽

Judith Herrmann ◽

Rüdiger Hoffmann ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Deep Learning ◽

Clinical Practice ◽

Magnetic Resonance ◽

Image Reconstruction ◽

Learning Technologies ◽

Acquisition Time ◽

Resonance Imaging ◽

Major Player ◽

The Impact

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.

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Image quality assessment of deep learning image reconstruction in computed tomography using tube current modulation: a phantom study

10.21203/rs.3.rs-591163/v1 ◽

2021 ◽

Author(s):

kazuhiro takeuchi ◽

Yasuhiro Ide ◽

Yuichiro Mori ◽

Yusuke Uehara ◽

Hiroshi Sukeishi ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Quality Characteristics ◽

Image Noise ◽

Tube Current Modulation ◽

Current Modulation ◽

Tube Current ◽

The Impact

Abstract The novel deep learning image reconstruction (DLIR) is known to change its image quality characteristics according to object contrast and image noise. In clinical practice, computed tomography (CT) image noise is usually controlled by tube current modulation (TCM) to accommodate changes in object size. This study aimed to evaluate the image quality characteristics of DLIR for different object sizes when in-plane noise is controlled by TCM. We used Mercury 4.0 phantoms with different object sizes. Phantom image acquisition was performed on a GE Revolution CT system to investigate the impact of the DLIR algorithm compared to standard reconstructions: filtered back projection (FBP) and hybrid iterative reconstruction (hybrid-IR). For image quality evaluation, the noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were determined. The NPS of DLIR was very similar to that of FBP, and the information in the high-frequency region was maintained. In terms of TTF, DLIR showed higher resolution than hybrid-IR at low- to medium-contrast (Δ50, Δ90HU), but not necessarily higher than FBP. At the simulated contrast and lesion size, DLIR showed higher detectability than hybrid-IR, regardless of the phantom size. In this study, we evaluated a novel DLIR algorithm by reproducing clinical behaviors. The findings indicate that DLIR produces higher image quality than hybrid-IR regardless of the phantom size, although it depends on the reconstruction strength.

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

Diagnostics ◽

10.3390/diagnostics11081484 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1484

Author(s):

Judith Herrmann ◽

Gregor Koerzdoerfer ◽

Dominik Nickel ◽

Mahmoud Mostapha ◽

Mariappan Nadar ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Spin Echo ◽

Musculoskeletal Imaging ◽

Acquisition Time ◽

Diagnostic Confidence ◽

Clinical Routine ◽

Time Saving ◽

Anatomical Structures ◽

Edge Sharpness

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 (TSES) and DL-based TSE sequences (TSEDL) 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 TSES (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSES and TSEDL (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.

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Application of Deep Learning Image Reconstruction (DLIR) Algorithm to Improve Image Quality in CT Angiography of Children with Takayasu Arteritis

10.21203/rs.3.rs-805973/v1 ◽

2021 ◽

Author(s):

Jihang Sun ◽

Haoyan Li ◽

Jianying Li ◽

Haiyun Li ◽

Michelle Li ◽

...

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Image Quality ◽

Takayasu Arteritis ◽

Image Noise ◽

Slice Thickness ◽

Reconstruction Algorithms ◽

Diagnostic Confidence ◽

Ct Number ◽

Significant Difference

Abstract purpose: To evaluate the image quality improvement in CTA of children with Takayasu arteritis (TAK) using a Deep learning image reconstruction (DLIR) in comparison to other reconstruction algorithms.Methods: 32 patients (9.14±4.51 years old) with TAK underwent neck, chest and abdominal CTA with 100kVp were enrolled. Images were reconstructed at 0.625mm slice thickness using Filtered Back-Projection (FBP), 50% adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). The CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers.Results: There was no significant difference in CT number across all reconstructions. The image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 with FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P>0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P<0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, but only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). Conclusions: DLIR-H improves the CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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