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Lee PK, Zhou X, Wang N, Syed AB, Brunsing RL, Vasanawala SS, Hargreaves BA. Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen. Magn Reson Med 2024; 92:586-604. [PMID: 38688875 DOI: 10.1002/mrm.30067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.
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Affiliation(s)
- Philip K Lee
- Radiology, Stanford University, Stanford, California, USA
| | - Xuetong Zhou
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
| | - Nan Wang
- Radiology, Stanford University, Stanford, California, USA
| | - Ali B Syed
- Radiology, Stanford University, Stanford, California, USA
| | | | | | - Brian A Hargreaves
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
- Electrical Engineering, Stanford University, Stanford, California, USA
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Lee PK, Zhou X, Hargreaves BA. Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection. Magn Reson Med 2024; 92:519-531. [PMID: 38623901 DOI: 10.1002/mrm.30102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/18/2024] [Accepted: 03/18/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated. METHODS Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively. RESULTS The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs. CONCLUSION Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.
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Affiliation(s)
- Philip K Lee
- Radiology, Stanford University, Stanford, California, USA
| | - Xuetong Zhou
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
- Electrical Engineering, Stanford University, Stanford, California, USA
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Jalnefjord O, Björkman-Burtscher IM. Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data. Magn Reson Med 2024; 92:303-318. [PMID: 38321596 DOI: 10.1002/mrm.30042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
PURPOSE Joint analysis of flow-compensated (FC) and non-flow-compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain. METHODS Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non-linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b-values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning-based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b-values 0-200 s/mm2 and corresponding flow weighting factors 0-2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis. RESULTS Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning-based algorithm for IVIM parametersD $$ D $$ andf $$ f $$ , and for the Bayesian algorithm only forv d $$ {v}_d $$ , relative to the other methods. CONCLUSION A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning-based algorithms appear promising.
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Affiliation(s)
- Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Section of Neuroradiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
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Führes T, Saake M, Lorenz J, Seuss H, Bickelhaupt S, Uder M, Laun FB. Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver. Z Med Phys 2024; 34:258-269. [PMID: 37543450 PMCID: PMC11156785 DOI: 10.1016/j.zemedi.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe. METHODS Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images. RESULTS The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers. CONCLUSION Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jennifer Lorenz
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hannes Seuss
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Department of Radiology, Klinikum Forchheim - Fränkische Schweiz, Forchheim, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Jiang T, Zeng Q, He J. Do alkaline phosphatases have great potential in the diagnosis, prognosis, and treatment of tumors? Transl Cancer Res 2023; 12:2932-2945. [PMID: 37969388 PMCID: PMC10643954 DOI: 10.21037/tcr-23-1190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/22/2023] [Indexed: 11/17/2023]
Abstract
Alkaline phosphatase (ALP) is a group of enzymes that catalyze hydrolysis of phosphate esters at an alkaline pH, resulting in the generation of inorganic phosphate. These enzymes are widely distributed, and their activity is found in various tissues including bone, liver, intestine, and placenta. However, abnormalities in ALP expression and activity have been observed in certain types of cancer. In some cases, elevated serum levels of ALP are observed in patients with liver and bone metastasis. In other cases, increased levels of ALP have been observed in patients with pancreatic and lung cancer. On the other hand, low expression of ALP has also been associated with poor prognosis in patients with certain types of tumors, including colorectal cancer (CRC), breast cancer, and non-small cell lung cancer (NSCLC). In these cases, low ALP activity may be associated with decreased differentiation of cancer cells and increased cancer cell proliferation. Overall, the role of ALP in cancer is complex and context-dependent. This article reviews application progress of ALP in cancer, and we hypothesize that ALP might be a potential tumor biomarker, combined detection of aspartate aminotransferase (AST)/alanine aminotransferase (ALT), bone-specific alkaline phosphatase (BSAP), carbohydrate antigen 19-9 (CA 19-9), lactate dehydrogenase (LDH) and ALP isozymes levels can be used for more accurate diagnosis of a particular tumor. Further research is needed to better understand the mechanisms underlying ALP dysregulation in cancer and to identify potential therapeutic targets.
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Affiliation(s)
- Tingting Jiang
- Department of Clinical Laboratory, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Qun Zeng
- Department of Biochemistry and Molecular Biology, Hengyang Medical School, University of South China, Hengyang, China
| | - Jun He
- Department of Clinical Laboratory, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China
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Führes T, Saake M, Szczepankiewicz F, Bickelhaupt S, Uder M, Laun FB. Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs. PLoS One 2023; 18:e0291273. [PMID: 37796773 PMCID: PMC10553293 DOI: 10.1371/journal.pone.0291273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/24/2023] [Indexed: 10/07/2023] Open
Abstract
PURPOSE The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression. METHODS Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales. RESULTS Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings). CONCLUSION All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | | | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Geng R, Zhang Y, Rice J, Muehler MR, Starekova J, Rutkowski DR, Uboha NV, Pirasteh A, Roldán-Alzate A, Guidon A, Hernando D. Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver. Magn Reson Med 2023; 89:908-921. [PMID: 36404637 PMCID: PMC9792444 DOI: 10.1002/mrm.29531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/30/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques. METHODS DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference. RESULTS In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( p = 0 . 0078 $$ p=0.0078 $$ , reproducibility coefficients (RPC) = 470 × 1 0 - 6 $$ 470\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( p = 0 . 29 $$ p=0.29 $$ , RPC = 220 × 1 0 - 6 $$ \mathrm{RPC}=220\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( p < 0 . 001 $$ p<0.001 $$ ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI. CONCLUSION Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.
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Affiliation(s)
- Ruiqi Geng
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Medical Physics, University of Wisconsin-Madison, WI, USA
| | - Yuxin Zhang
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Medical Physics, University of Wisconsin-Madison, WI, USA
| | - James Rice
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Mechanical Engineering, University of Wisconsin-Madison, WI, USA
| | | | - Jitka Starekova
- Department of Radiology, University of Wisconsin-Madison, WI, USA
| | - David R. Rutkowski
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Mechanical Engineering, University of Wisconsin-Madison, WI, USA
| | - Nataliya V. Uboha
- Division of Hematology and Oncology, Department of Medicine, University of Wisconsin-Madison, WI, USA,UW Carbone Cancer Center, WI, USA
| | - Ali Pirasteh
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Medical Physics, University of Wisconsin-Madison, WI, USA
| | - Alejandro Roldán-Alzate
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Mechanical Engineering, University of Wisconsin-Madison, WI, USA
| | | | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, WI, USA,Department of Medical Physics, University of Wisconsin-Madison, WI, USA,Department of Electrical and Computer Engineering, University of Wisconsin-Madison, WI, USA,Department of Biomedical Engineering, University of Wisconsin-Madison, WI, USA
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Führes T, Saake M, Lorenz J, Seuss H, Stemmer A, Benkert T, Uder M, Laun FB. Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow‐compensated diffusion images in the liver—A quantitative evaluation of postprocessing algorithms. Magn Reson Med 2022; 89:423-439. [PMID: 36089798 DOI: 10.1002/mrm.29427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/05/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. METHODS Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5 T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( Q total , $$ {Q}_{\mathrm{total}}, $$ set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively. RESULTS The weighted averaging algorithm performed best according to the total quality score ( Q total = 1.111 ± 0.067 $$ {Q}_{\mathrm{total}}=1.111\pm 0.067 $$ ). The further ranking was outlier exclusion algorithm ( Q total = 1.086 ± 0.061 $$ {Q}_{\mathrm{total}}=1.086\pm 0.061 $$ ), p-mean algorithm ( Q total = 1.045 ± 0.049 $$ {Q}_{\mathrm{total}}=1.045\pm 0.049 $$ ), percentile algorithm ( Q total = 1.012 ± 0.049 $$ {Q}_{\mathrm{total}}=1.012\pm 0.049 $$ ), and exception set algorithm ( Q total = 0.957 ± 0.027 $$ {Q}_{\mathrm{total}}=0.957\pm 0.027 $$ ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in Q total $$ {Q}_{\mathrm{total}} $$ were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe. CONCLUSION Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
| | - Jennifer Lorenz
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
| | - Hannes Seuss
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
- Abteilung für Radiologie Klinikum Forchheim – Fränkische Schweiz Forchheim Germany
| | - Alto Stemmer
- MR Application Predevelopment Siemens Healthcare GmbH Erlangen Germany
| | - Thomas Benkert
- MR Application Predevelopment Siemens Healthcare GmbH Erlangen Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU) Erlangen Germany
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