Efficiency and Accuracy Evaluation of Multiple Diffusion-Weighted MRI Techniques Across Different Scanners.

accuracy apparent diffusion coefficient diffusion weighted imaging efficiency quantitative imaging biomarker signal-to-noise ratio

Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
Jan 2024
Historique:
revised: 23 05 2023
received: 25 09 2022
accepted: 23 05 2023
pubmed: 19 6 2023
medline: 19 6 2023
entrez: 19 6 2023
Statut: ppublish

Résumé

The choice between different diffusion-weighted imaging (DWI) techniques is difficult as each comes with tradeoffs for efficient clinical routine imaging and apparent diffusion coefficient (ADC) accuracy. To quantify signal-to-noise-ratio (SNR) efficiency, ADC accuracy, artifacts, and distortions for different DWI acquisition techniques, coils, and scanners. Phantom, in vivo intraindividual biomarker accuracy between DWI techniques and independent ratings. NIST diffusion phantom. 51 Patients: 40 with prostate cancer and 11 with head-and-neck cancer at 1.5 T FIELD STRENGTH/SEQUENCE: Echo planar imaging (EPI): 1.5 T and 3 T Siemens; 3 T Philips. Distortion-reducing: RESOLVE (1.5 and 3 T Siemens); Turbo Spin Echo (TSE)-SPLICE (3 T Philips). Small field-of-view (FOV): ZoomitPro (1.5 T Siemens); IRIS (3 T Philips). Head-and-neck and flexible coils. SNR Efficiency, geometrical distortions, and susceptibility artifacts were quantified for different b-values in a phantom. ADC accuracy/agreement was quantified in phantom and for 51 patients. In vivo image quality was independently rated by four experts. QIBA methodology for accuracy: trueness, repeatability, reproducibility, Bland-Altman 95% Limits-of-Agreement (LOA) for ADC. Wilcoxon Signed-Rank and student tests on P < 0.05 level. The ZoomitPro small FOV sequence improved b-image efficiency by 8%-14%, reduced artifacts and observer scoring for most raters at the cost of smaller FOV compared to EPI. The TSE-SPLICE technique reduced artifacts almost completely at a 24% efficiency cost compared to EPI for b-values ≤500 sec/mm ZoomitPro for Siemens and TSE SPLICE for Philips resulted in a trade-off between efficiency and artifacts. Phantom ADC quality control largely underestimated in vivo accuracy: significant ADC bias and variability was found between techniques in vivo. 3 TECHNICAL EFFICACY STAGE: 2.

Sections du résumé

BACKGROUND BACKGROUND
The choice between different diffusion-weighted imaging (DWI) techniques is difficult as each comes with tradeoffs for efficient clinical routine imaging and apparent diffusion coefficient (ADC) accuracy.
PURPOSE OBJECTIVE
To quantify signal-to-noise-ratio (SNR) efficiency, ADC accuracy, artifacts, and distortions for different DWI acquisition techniques, coils, and scanners.
STUDY TYPE METHODS
Phantom, in vivo intraindividual biomarker accuracy between DWI techniques and independent ratings.
POPULATION/PHANTOMS UNASSIGNED
NIST diffusion phantom. 51 Patients: 40 with prostate cancer and 11 with head-and-neck cancer at 1.5 T FIELD STRENGTH/SEQUENCE: Echo planar imaging (EPI): 1.5 T and 3 T Siemens; 3 T Philips. Distortion-reducing: RESOLVE (1.5 and 3 T Siemens); Turbo Spin Echo (TSE)-SPLICE (3 T Philips). Small field-of-view (FOV): ZoomitPro (1.5 T Siemens); IRIS (3 T Philips). Head-and-neck and flexible coils.
ASSESSMENT RESULTS
SNR Efficiency, geometrical distortions, and susceptibility artifacts were quantified for different b-values in a phantom. ADC accuracy/agreement was quantified in phantom and for 51 patients. In vivo image quality was independently rated by four experts.
STATISTICAL TESTS METHODS
QIBA methodology for accuracy: trueness, repeatability, reproducibility, Bland-Altman 95% Limits-of-Agreement (LOA) for ADC. Wilcoxon Signed-Rank and student tests on P < 0.05 level.
RESULTS RESULTS
The ZoomitPro small FOV sequence improved b-image efficiency by 8%-14%, reduced artifacts and observer scoring for most raters at the cost of smaller FOV compared to EPI. The TSE-SPLICE technique reduced artifacts almost completely at a 24% efficiency cost compared to EPI for b-values ≤500 sec/mm
DATA CONCLUSION CONCLUSIONS
ZoomitPro for Siemens and TSE SPLICE for Philips resulted in a trade-off between efficiency and artifacts. Phantom ADC quality control largely underestimated in vivo accuracy: significant ADC bias and variability was found between techniques in vivo.
LEVEL OF EVIDENCE METHODS
3 TECHNICAL EFFICACY STAGE: 2.

Identifiants

pubmed: 37335079
doi: 10.1002/jmri.28869
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

311-322

Subventions

Organisme : Cancéropôle Nord-Ouest

Informations de copyright

© 2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Références

Luzurier A, Jouve De Guibert PH, Allera A, et al. Dynamic contrast-enhanced imaging in localizing local recurrence of prostate cancer after radiotherapy: Limited added value for readers of varying level of experience. J Magn Reson Imaging 2018;48:1012-1023.
Vargas HA, Hötker AM, Goldman DA, et al. Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: Critical evaluation using whole-mount pathology as standard of reference. Eur Radiol 2016;26:1606-1612.
Lotte R, Lafourcade A, Mozer P, et al. Multiparametric MRI for suspected recurrent prostate cancer after HIFU:Is DCE still needed? Eur Radiol 2018;28:3760-3769.
Onal C, Erbay G, Guler OC, Oymak E. The prognostic value of mean apparent diffusion coefficient measured with diffusion-weighted magnetic resonance image in patients with prostate cancer treated with definitive radiotherapy. Radiother Oncol 2022;173:285-291.
Alexander EJ, Murray JR, Morgan VA, et al. Validation of T2- and diffusion-weighted magnetic resonance imaging for mapping intra-prostatic tumour prior to focal boost dose-escalation using intensity-modulated radiotherapy (IMRT). Radiother Oncol 2019;141:181-187.
Foltz WD, Porter DA, Simeonov A, et al. Readout-segmented echo-planar diffusion-weighted imaging improves geometric performance for image-guided radiation therapy of pelvic tumors. Radiother Oncol 2015;117:525-531.
Martens RM, Noij DP, Ali M, et al. Functional imaging early during (chemo)radiotherapy for response prediction in head and neck squamous cell carcinoma; a systematic review. Oral Oncol 2019;88:75-83.
Gurney-Champion OJ, Mahmood F, van Schie M, et al. Quantitative imaging for radiotherapy purposes. Radiother Oncol 2020;146:66-75.
Shukla-Dave A, Obuchowski NA, Chenevert TL, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101-e121.
Keenan KE, Gimbutas Z, Dienstfrey A, et al. Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom. PLoS One 2021;16:1-19.
Glide-Hurst CK, Paulson ES, McGee K, et al. Task group 284 report: Magnetic resonance imaging simulation in radiotherapy: Considerations for clinical implementation, optimization, and quality assurance. Med Phys 2021;48:e636-e670.
McGee KP, Hwang KP, Sullivan DC, et al. Magnetic resonance biomarkers in radiation oncology: The report of AAPM task group 294. Med Phys 2021;48:e697-e732.
Peña-Nogales Ó, Hernando D, Aja-Fernández S, de Luis-Garcia R. Determination of optimized set of b-values for apparent diffusion coefficient mapping in liver diffusion-weighted MRI. J Magn Reson 2020;310:1-14.
Sullivan DC, Obuchowski NA, Kessler LG, et al. For the RSNA-QIBA Metrology Working Group: Metrology standards for quantitative imaging biomarkers. Radiology 2015;277:813-825.
Porter DA, Heidemann RM. High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magn Reson Med 2009;62:468-475.
Schick F. SPLICE: Sub-second diffusion-sensitive MR imaging using a modified fast spin-echo acquisition mode. Magn Reson Med 1997;38:638-644.
Finsterbusch J. Improving the performance of diffusion-weighted inner field-of-view echo-planar imaging based on 2D-selective radiofrequency excitations by tilting the excitation plane. J Magn Reson Imaging 2012;35:984-992.
Liney GP, Holloway L, Al Harthi TM, et al. Quantitative evaluation of diffusion-weighted imaging techniques for the purposes of radiotherapy planning in the prostate. Br J Radiol 2015;88:20150034.
Lu Y, Hatzoglou V, Banerjee S, et al. Repeatability investigation of reduced field-of-view diffusion weighted magnetic resonance imaging on thyroid glands. J Comput Assist Tomogr 2015;39:334.
Korn N, Kurhanewicz J, Banerjee S, Starobinets O, Saritas E, Noworolski S. Reduced-FOV excitation decreases susceptibility artifact in diffusion-weighted MRI with Endorectal coil for prostate cancer detection. Magn Reson Imaging 2015;33:56-62.
Larkman DJ, Nunes RG. Parallel magnetic resonance imaging. Phys Med Biol 2007;52:R15-R55.
Keenan KE, Carnicka S, Gottlieb SC, Stupic KF. Assessing changes in MRI measurands incurred in a scanner upgrade: Is my study comprised. CA, USA: ISMRM; 2017.
Ripley BD. The R project in statistical computing. MSOR Connect 2001;1:23-25.
Kessler LG, Barnhart HX, Buckler AJ, et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 2015;24:9-26.
Raunig DL, McShane LM, Pennello G, et al. Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment. Stat Methods Med Res 2015;24:27-67.
American College of Radiology. Magnetic resonance imaging quality control manual. Virginia, USA: American College of Radiology; 2015.
Griffanti L, Baglio F, Preti MG, et al. Signal-to-noise ratio of diffusion weighted magnetic resonance imaging: Estimation methods and in vivo application to spinal cord. Biomed Signal Process Control 2012;7:285-294.
Reeder SB, Wintersperger BJ, Dietrich O, et al. Practical approaches to the evaluation of signal-to-noise ratio performance with parallel imaging: Application with cardiac imaging and a 32-channel cardiac coil. Magn Reson Med 2005;54:748-754.
Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010;23:803-820.
Martin Bland J, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;327:307-310.
Dietrich O, Raya JG, Reeder SB, Reiser MF, Schoenberg SO. Measurement of signal-to-noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters. J Magn Reson Imaging 2007;26:375-385.
Newitt DC, Zhang Z, Gibbs JE, et al. Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial. J Magn Reson Imaging 2019;49:1617-1628.
Gibbs P, Pickles MD, Turnbull LW. Repeatability of echo-planar-based diffusion measurements of the human prostate at 3 T. Magn Reson Imaging 2007;25:1423-1429.
Barrett T, Lawrence EM, Priest AN, et al. Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis. Eur J Radiol 2019;110:22-29.
Habrich J, Boeke S, Nachbar M, et al. Repeatability of diffusion-weighted magnetic resonance imaging in head and neck cancer at a 1.5 T MR-Linac. Radiother Oncol 2022;174:141-148.
Paudyal R, Konar AS, Obuchowski NA, et al. Repeatability of quantitative diffusion-weighted imaging metrics in phantoms, head-and-neck and thyroid cancers: Preliminary findings. Tomography 2019;5:15-25.
Baltzer P, Mann RM, Iima M, et al. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI international breast diffusion-weighted imaging working group. Eur Radiol 2020;30:1436-1450.

Auteurs

Frederik Crop (F)

Department of Medical Physics, Centre Oscar Lambret, Lille, France.
University of Lille, IEMN, Lille, France.

Clémence Robert (C)

Department of Medical Physics, Centre Oscar Lambret, Lille, France.

Romain Viard (R)

University of Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, PLBS UAR 2014-US 41, Lille, France.
University of Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience & Cognition, Lille, France.

Julien Dumont (J)

University of Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, PLBS UAR 2014-US 41, Lille, France.

Marine Kawalko (M)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Pauline Makala (P)

Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France.

Xavier Liem (X)

Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France.

Imen El Aoud (I)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Aicha Ben Miled (A)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Victor Chaton (V)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Lucas Patin (L)

Department of Radiology, Centre Oscar Lambret, Lille, France.

David Pasquier (D)

Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France.
University of Lille, Centre de recherche en informatique, Signal et automatique de Lille, Lille, France.

Ophélie Guillaud (O)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Benjamin Vandendorpe (B)

Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France.

Xavier Mirabel (X)

Academic Department of Radiotherapy, Centre Oscar Lambret, Lille, France.

Luc Ceugnart (L)

Department of Radiology, Centre Oscar Lambret, Lille, France.

Camille Decoene (C)

Department of Medical Physics, Centre Oscar Lambret, Lille, France.

Thomas Lacornerie (T)

Department of Medical Physics, Centre Oscar Lambret, Lille, France.

Classifications MeSH