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Bolan PJ, Saunders SL, Kay K, Gross M, Akcakaya M, Metzger GJ. Improved quantitative parameter estimation for prostate T 2 relaxometry using convolutional neural networks. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01186-3. [PMID: 39042205 DOI: 10.1007/s10334-024-01186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/01/2024] [Accepted: 07/02/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVE Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate. MATERIALS AND METHODS Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. RESULTS In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. DISCUSSION This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA.
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
| | - Sara L Saunders
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Mitchell Gross
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN, 55455, USA
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA
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Pierobon Mays G, Hett K, Eisma J, McKnight CD, Elenberger J, Song AK, Considine C, Richerson WT, Han C, Stark A, Claassen DO, Donahue MJ. Reduced cerebrospinal fluid motion in patients with Parkinson's disease revealed by magnetic resonance imaging with low b-value diffusion weighted imaging. Fluids Barriers CNS 2024; 21:40. [PMID: 38725029 PMCID: PMC11080257 DOI: 10.1186/s12987-024-00542-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Parkinson's disease is characterized by dopamine-responsive symptoms as well as aggregation of α-synuclein protofibrils. New diagnostic methods assess α-synuclein aggregation characteristics from cerebrospinal fluid (CSF) and recent pathophysiologic mechanisms suggest that CSF circulation disruptions may precipitate α-synuclein retention. Here, diffusion-weighted MRI with low-to-intermediate diffusion-weightings was applied to test the hypothesis that CSF motion is reduced in Parkinson's disease relative to healthy participants. METHODS Multi-shell diffusion weighted MRI (spatial resolution = 1.8 × 1.8 × 4.0 mm) with low-to-intermediate diffusion weightings (b-values = 0, 50, 100, 200, 300, 700, and 1000 s/mm2) was applied over the approximate kinetic range of suprasellar cistern fluid motion at 3 Tesla in Parkinson's disease (n = 27; age = 66 ± 6.7 years) and non-Parkinson's control (n = 32; age = 68 ± 8.9 years) participants. Wilcoxon rank-sum tests were applied to test the primary hypothesis that the noise floor-corrected decay rate of CSF signal as a function of b-value, which reflects increasing fluid motion, is reduced within the suprasellar cistern of persons with versus without Parkinson's disease and inversely relates to choroid plexus activity assessed from perfusion-weighted MRI (significance-criteria: p < 0.05). RESULTS Consistent with the primary hypothesis, CSF decay rates were higher in healthy (D = 0.00673 ± 0.00213 mm2/s) relative to Parkinson's disease (D = 0.00517 ± 0.00110 mm2/s) participants. This finding was preserved after controlling for age and sex and was observed in the posterior region of the suprasellar cistern (p < 0.001). An inverse correlation between choroid plexus perfusion and decay rate in the voxels within the suprasellar cistern (Spearman's-r=-0.312; p = 0.019) was observed. CONCLUSIONS Multi-shell diffusion MRI was applied to identify reduced CSF motion at the level of the suprasellar cistern in adults with versus without Parkinson's disease; the strengths and limitations of this methodology are discussed in the context of the growing literature on CSF flow.
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Affiliation(s)
| | - Kilian Hett
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jarrod Eisma
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason Elenberger
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander K Song
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ciaran Considine
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wesley T Richerson
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Caleb Han
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam Stark
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel O Claassen
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Manus J Donahue
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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Hayashi T, Kojima S, Ito T, Hayashi N, Kondo H, Yamamoto A, Oba H. Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom. Radiol Phys Technol 2024; 17:186-194. [PMID: 38153622 DOI: 10.1007/s12194-023-00765-8] [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: 09/19/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/29/2023]
Abstract
This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10-3 mm2/s at 0 °C) was imaged at various b-values (0, 1000, 2000, and 4000 s/mm2) using a 3 T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0 mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000 s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000 s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5 mm and combined b-values of 0 and 4000 s/mm2, the ADC values were 0.97 × 10-3and 0.98 × 10-3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.
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Affiliation(s)
- Tatsuya Hayashi
- Graduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan.
| | - Shinya Kojima
- Graduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Toshimune Ito
- Graduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Norio Hayashi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamiokimachi, Maebashi, Gunma, 371-0052, Japan
| | - Hiroshi Kondo
- Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Asako Yamamoto
- Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Hiroshi Oba
- Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan
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Bisgaard ALH, Keesman R, van Lier ALHMW, Coolens C, van Houdt PJ, Tree A, Wetscherek A, Romesser PB, Tyagi N, Lo Russo M, Habrich J, Vesprini D, Lau AZ, Mook S, Chung P, Kerkmeijer LGW, Gouw ZAR, Lorenzen EL, van der Heide UA, Schytte T, Brink C, Mahmood F. Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group. Radiother Oncol 2023; 186:109803. [PMID: 37437609 PMCID: PMC11197850 DOI: 10.1016/j.radonc.2023.109803] [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: 01/19/2023] [Revised: 06/30/2023] [Accepted: 07/06/2023] [Indexed: 07/14/2023]
Abstract
BACKGROUND AND PURPOSE The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it. MATERIALS AND METHODS Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group. RESULTS The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types. CONCLUSION Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.
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Affiliation(s)
- Anne L H Bisgaard
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Kløvervænget 19, 5000 Odense, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19.3, 5000 Odense Denmark.
| | - Rick Keesman
- Department of Radiation Oncology, Radboud University Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Astrid L H M W van Lier
- Department of Radiotherapy, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX,Utrecht, The Netherlands.
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, M5G 2M9 Toronto, ON, Canada.
| | - Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Postbus 90203, 1006 BE Amsterdam, The Netherlands.
| | - Alison Tree
- Department of Urology, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT London, UK.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, SM2 5NG London, UK.
| | - Paul B Romesser
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box 22, NY 10065, New York, USA.
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 545 E. 73rd street, NY 10021, New York, USA.
| | - Monica Lo Russo
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany.
| | - Jonas Habrich
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany.
| | - Danny Vesprini
- Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, 2075 Bayview Avenue, M4N 3M5 Toronto, ON, Canada.
| | - Angus Z Lau
- Physical Sciences Platform, Sunnybrook Research Institute. Department of Medical Biophysics, University of Toronto, 2075 Bayview Avenue, M4N 3M5 Toronto, ON, Canada.
| | - Stella Mook
- Department of Radiotherapy, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX,Utrecht, The Netherlands.
| | - Peter Chung
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network. Department of Radiation Oncology, University of Toronto, 610 University Avenue, M5G 2M9 Toronto, ON, Canada.
| | - Linda G W Kerkmeijer
- Department of Radiation Oncology, Radboud University Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Zeno A R Gouw
- Department of Radiation Oncology, the Netherlands Cancer Institute, Postbus 90203, 1006 BE Amsterdam, The Netherlands.
| | - Ebbe L Lorenzen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Kløvervænget 19, 5000 Odense, Denmark.
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Postbus 90203, 1006 BE Amsterdam, The Netherlands.
| | - Tine Schytte
- Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19.3, 5000 Odense Denmark; Department of Oncology, Odense University Hospital, Kløvervænget 19, 5000 Odense, Denmark.
| | - Carsten Brink
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Kløvervænget 19, 5000 Odense, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19.3, 5000 Odense Denmark.
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Kløvervænget 19, 5000 Odense, Denmark; Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19.3, 5000 Odense Denmark.
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Kauppinen RA, Thothard J, Leskinen HPP, Pisharady PK, Manninen E, Kettunen M, Lenglet C, Gröhn OHJ, Garwood M, Nissi MJ. Axon fiber orientation as the source of T 1 relaxation anisotropy in white matter: A study on corpus callosum in vivo and ex vivo. Magn Reson Med 2023; 90:708-721. [PMID: 37145027 DOI: 10.1002/mrm.29667] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/22/2023] [Accepted: 03/24/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE Recent studies indicate that T1 in white matter (WM) is influenced by fiber orientation in B0 . The purpose of the study was to investigate the interrelationships between axon fiber orientation in corpus callosum (CC) and T1 relaxation time in humans in vivo as well as in rat brain ex vivo. METHODS Volunteers were scanned for relaxometric and diffusion MRI at 3 T and 7 T. Angular T1 plots from WM were computed using fractional anisotropy and fiber-to-field-angle maps. T1 and fiber-to-field angle were measured in five sections of CC to estimate the effects of inherently varying fiber orientations on T1 within the same tracts in vivo. Ex vivo rat-brain preparation encompassing posterior CC was rotated in B0 and T1 , and diffusion MRI images acquired at 9.4 T. T1 angular plots were determined at several rotation angles in B0 . RESULTS Angular T1 plots from global WM provided reference for estimated fiber orientation-linked T1 changes within CC. In anterior midbody of CC in vivo, where small axons are dominantly present, a shift in axon orientation is accompanied by a change in T1 , matching that estimated from WM T1 data. In CC, where large and giant axons are numerous, the measured T1 change is about 2-fold greater than the estimated one. Ex vivo rotation of the same midsagittal CC region of interest produced angular T1 plots at 9.4 T, matching those observed at 7 T in vivo. CONCLUSION These data causally link axon fiber orientation in B0 to the T1 relaxation anisotropy in WM.
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Affiliation(s)
- Risto A Kauppinen
- Department of Electric and Electronic Engineering, University of Bristol, Bristol, UK
| | - Jeromy Thothard
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Henri P P Leskinen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Pramod K Pisharady
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Eppu Manninen
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Mikko Kettunen
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Olli H J Gröhn
- A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Michael Garwood
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mikko J Nissi
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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Bolan PJ, Saunders SL, Kay K, Gross M, Akcakaya M, Metzger GJ. Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284194. [PMID: 36711813 PMCID: PMC9882442 DOI: 10.1101/2023.01.11.23284194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Sara L Saunders
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Kendrick Kay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
| | - Mitchell Gross
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis MN
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN
- Department of Radiology, University of Minnesota, Minneapolis MN
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Kauppinen RA, Thotland J, Pisharady PK, Lenglet C, Garwood M. White matter microstructure and longitudinal relaxation time anisotropy in human brain at 3 and 7 T. NMR IN BIOMEDICINE 2023; 36:e4815. [PMID: 35994269 PMCID: PMC9742158 DOI: 10.1002/nbm.4815] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 05/22/2023]
Abstract
A high degree of structural order by white matter (WM) fibre tracts creates a physicochemical environment where water relaxations are rendered anisotropic. Recently, angularly dependent longitudinal relaxation has been reported in human WM. We have characterised interrelationships between T1 relaxation and diffusion MRI microstructural indices at 3 and 7 T. Eleven volunteers consented to participate in the study. Multishell diffusion MR images were acquired with b-values of 0/1500/3000 and 0/1000/2000 s/mm2 at 1.5 and 1.05 mm3 isotropic resolutions at 3 and 7 T, respectively. DTIFIT was used to compute DTI indices; the fibre-to-field angle (θFB ) maps were obtained using the principal eigenvector images. The orientations and volume fractions of multiple fibre populations were estimated using BedpostX in FSL, and the orientation dispersion index (ODI) was estimated using the NODDI protocol. MP2RAGE was used to acquire images for T1 maps at 1.0 and 0.9 mm3 isotropic resolutions at 3 and 7 T, respectively. At 3 T, T1 as a function of θFB in WM with high fractional anisotropy and one-fibre orientation volume fraction or low ODI shows a broad peak centred at 50o , but a flat baseline at 0o and 90o . The broad peak amounted up to 7% of the mean T1. At 7 T, the broad peak appeared at 40o and T1 in fibres running parallel to B0 was longer by up to 75 ms (8.3% of the mean T1) than in those perpendicular to the field. The peak at 40o was approximately 5% of mean T1 (i.e., proportionally smaller than that at 54o at 3 T). The data demonstrate T1 anisotropy in WM with high microstructural order at both fields. The angular patterns are indicative of the B0-dependency of T1 anisotropy. Thus myelinated WM fibres influence T1 contrast both by acting as a T1 contrast agent and rendering T1 dependent on fibre orientation with B0.
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Affiliation(s)
- Risto A. Kauppinen
- Department of Electric and Electronic EngineeringUniversity of BristolBristolUK
| | - Jeromy Thotland
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Pramod K. Pisharady
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Christophe Lenglet
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Michael Garwood
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
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8
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Liu F, Yang J, Feng M, Cui Z, He X, Zhou L, Feng J, Shen D. Does perfect filtering really guarantee perfect phase correction for diffusion MRI data? Comput Med Imaging Graph 2023; 103:102160. [PMID: 36528017 DOI: 10.1016/j.compmedimag.2022.102160] [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: 08/25/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022]
Abstract
Owing to its merit of avoiding noise-floor, phase correction is recently used to reconstruct real-valued diffusion MRI data by employing an image filter to estimate the noise-free background phase. However, several studies report an unexpected signal-loss issue for their reconstruction results, with its causing reason still remaining unclear. Although phase correction has achieved promising results in mitigating the signal-loss issue via improving the employed image filter, we have observed counterintuitive results that an advanced filter generates severe artifacts in our previous work. Considering the potential issues with phase correction procedures, in this paper, we argue that even a perfect image filter is insufficient to produce perfect phase correction. To point out the reason why phase correction introduces signal-loss and address this issue, we first propose a complex polar coordinate system (CPCS) to analyze its procedures in detail; second, based on CPCS, we find that phase correction has not sufficiently utilized the background phase, and thus propose a quantitative criterion to fully exploit the background phase; eventually, we propose a phase calibration procedure to remedy current phase correction. Extensive experimental results, including those on synthetic and real diffusion MRI data, demonstrate that our proposed method significantly reduces signal-loss and also eliminates artifacts in FA maps, particularly with improved accuracy on FA.
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Affiliation(s)
- Feihong Liu
- School of Information Science and Technology, Northwest University, Xi'an, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Junwei Yang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Mingyue Feng
- Department of Informatics, Technische Universität München, Garching, Germany
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Xiaowei He
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China
| | - Luping Zhou
- School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Chakwizira A, Westin C, Brabec J, Lasič S, Knutsson L, Szczepankiewicz F, Nilsson M. Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange. NMR IN BIOMEDICINE 2023; 36:e4827. [PMID: 36075110 PMCID: PMC10078514 DOI: 10.1002/nbm.4827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 05/06/2023]
Abstract
Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Carl‐Fredrik Westin
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Brabec
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreCopenhagenDenmark
- Random Walk Imaging ABLundSweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
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10
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Reduced field-of-view and multi-shot DWI acquisition techniques: Prospective evaluation of image quality and distortion reduction in prostate cancer imaging. Magn Reson Imaging 2022; 93:108-114. [DOI: 10.1016/j.mri.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/20/2022]
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11
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Vis G, Nilsson M, Westin CF, Szczepankiewicz F. Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution. Neuroimage 2021; 245:118673. [PMID: 34688898 PMCID: PMC9272945 DOI: 10.1016/j.neuroimage.2021.118673] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/13/2021] [Accepted: 10/20/2021] [Indexed: 12/31/2022] Open
Abstract
Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution—a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.
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Affiliation(s)
- Geraline Vis
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden.
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden.
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
| | - Filip Szczepankiewicz
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
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12
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Hernando D, Zhang Y, Pirasteh A. Quantitative diffusion MRI of the abdomen and pelvis. Med Phys 2021; 49:2774-2793. [PMID: 34554579 DOI: 10.1002/mp.15246] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 09/15/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.
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Affiliation(s)
- Diego Hernando
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yuxin Zhang
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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13
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Baur O, Den Harder J, Hemke R, Farid FM, Smithuis F, De Weerdt E, Nederveen A, Maas M. The road to optimal acceleration of Dixon imaging and quantitative T2-mapping in the ankle using compressed sensing and parallel imaging. Eur J Radiol 2020; 132:109295. [DOI: 10.1016/j.ejrad.2020.109295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 11/26/2022]
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14
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Liu F, Feng J, Chen G, Shen D, Yap PT. Gaussianization of Diffusion MRI Data Using Spatially Adaptive Filtering. Med Image Anal 2020; 68:101828. [PMID: 33338870 DOI: 10.1016/j.media.2020.101828] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 06/28/2020] [Accepted: 08/07/2020] [Indexed: 11/27/2022]
Abstract
Diffusion MRI magnitude data, typically Rician or noncentral χ distributed, is affected by the noise floor, which falsely elevates signal, reduces image contrast, and biases estimation of diffusion parameters. Noise floor can be avoided by extracting real-valued Gaussian-distributed data from complex diffusion-weighted images via phase correction, which is performed by rotating each complex diffusion-weighted image based on its phase so that the actual image content resides in the real part. The imaginary part can then be discarded, leaving only the real part to form a Gaussian-noise image that is not confounded by the noise floor. The effectiveness of phase correction depends on the estimation of the background phase associated with factors such as brain motion, cardiac pulsation, perfusion, and respiration. Most existing smoothing techniques, applied to the real and imaginary images for phase estimation, assume spatially-stationary noise. This assumption does not necessarily hold in real data. In this paper, we introduce an adaptive filtering approach, called multi-kernel filter (MKF), for image smoothing catering to spatially-varying noise. Inspired by the mechanisms of human vision, MKF employs a bilateral filter with spatially-varying kernels. Extensive experiments demonstrate that MKF significantly improves spatial adaptivity and outperforms various state-of-the-art filters in signal Gaussianization.
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Affiliation(s)
- Feihong Liu
- School of Information Science and Technology, Northwest University, Xi'an, China; Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China.
| | - Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A..
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A..
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15
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Balasubramanian M, Mulkern RV, Neil JJ, Maier SE, Polimeni JR. Probing in vivo cortical myeloarchitecture in humans via line-scan diffusion acquisitions at 7 T with 250-500 micron radial resolution. Magn Reson Med 2020; 85:390-403. [PMID: 32738088 DOI: 10.1002/mrm.28419] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 06/15/2020] [Accepted: 06/18/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE The goal of this study was to measure diffusion signals within the cerebral cortex using the line-scan technique to achieve extremely high resolution in the radial direction (ie, perpendicular to the cortical surface) and to demonstrate the utility of these measurements for investigating laminar architecture in the living human brain. METHODS Line-scan diffusion data with 250-500 micron radial resolution were acquired at 7 T on 8 healthy volunteers, with each line prescribed perpendicularly to primary somatosensory cortex (S1) and primary motor cortex (M1). Apparent diffusion coefficients, fractional anisotropy values, and radiality indices were measured as a function of cortical depth. RESULTS In the deep layers of S1, we found evidence for high anisotropy and predominantly tangential diffusion, with low anisotropy observed in superficial S1. In M1, moderate anisotropy and predominantly radial diffusion was seen at almost all cortical depths. These patterns were consistent across subjects and were conspicuous without averaging data across different locations on the cortical sheet. CONCLUSION Our results are in accord with the myeloarchitecture of S1 and M1, known from prior histology studies: in S1, dense bands of tangential myelinated fibers run through the deep layers but not the superficial ones, and in M1, radial myelinated fibers are prominent at most cortical depths. This work therefore provides support for the idea that high-resolution diffusion signals, measured with the line-scan technique and receiving a boost in SNR at 7 T, may serve as a sensitive probe of in vivo laminar architecture.
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Affiliation(s)
- Mukund Balasubramanian
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Robert V Mulkern
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Stephan E Maier
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Institute of Clinical Sciences, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jonathan R Polimeni
- Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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16
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Goodburn RJ, Barrett T, Patterson I, Gallagher FA, Lawrence EM, Gnanapragasam VJ, Kastner C, Priest AN. Removing rician bias in diffusional kurtosis of the prostate using real-data reconstruction. Magn Reson Med 2020; 83:2243-2252. [PMID: 31737935 PMCID: PMC7065237 DOI: 10.1002/mrm.28080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 10/22/2019] [Accepted: 10/23/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase-corrected real data with those generated using magnitude data with and without noise compensation (NC). METHODS Diffusion-weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b-values (0-1500 s/mm2 ), each acquired with 6 signal averages along 3 diffusion directions, with noise-only images acquired to allow NC. In addition to conventional magnitude averaging, phase-corrected real data were averaged in an attempt to reduce rician noise-bias, with a range of phase-correction low-pass filter (LPF) sizes (8-128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase-corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ). RESULTS Simulations indicated LPF size can strongly affect K metrics, where 64-pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64-LPF real-data K were lower (P < 0.0001) by 4/10/7%, respectively. CONCLUSION Compared with magnitude data with NC, phase-corrected real data can produce similar K, although the choice of phase-correction LPF should be chosen carefully.
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Affiliation(s)
- Rosie J. Goodburn
- Department of Medical PhysicsCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
- Division of Radiotherapy and ImagingThe Institute of Cancer ResearchLondon
| | - Tristan Barrett
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Ilse Patterson
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Ferdia A. Gallagher
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Edward M. Lawrence
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Christof Kastner
- Department of UrologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Andrew N. Priest
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
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17
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Pizzolato M, Gilbert G, Thiran JP, Descoteaux M, Deriche R. Adaptive phase correction of diffusion-weighted images. Neuroimage 2020; 206:116274. [PMID: 31629826 PMCID: PMC7355239 DOI: 10.1016/j.neuroimage.2019.116274] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 10/08/2019] [Accepted: 10/10/2019] [Indexed: 12/22/2022] Open
Abstract
Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.
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Affiliation(s)
- Marco Pizzolato
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | | | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Rachid Deriche
- Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, France
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18
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Accelerated Segmented Diffusion-Weighted Prostate Imaging for Higher Resolution, Higher Geometric Fidelity, and Multi-b Perfusion Estimation. Invest Radiol 2019; 54:238-246. [PMID: 30601292 DOI: 10.1097/rli.0000000000000536] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
PURPOSE The aim of this study was to improve the geometric fidelity and spatial resolution of multi-b diffusion-weighted magnetic resonance imaging of the prostate. MATERIALS AND METHODS An accelerated segmented diffusion imaging sequence was developed and evaluated in 25 patients undergoing multiparametric magnetic resonance imaging examinations of the prostate. A reduced field of view was acquired using an endorectal coil. The number of sampled diffusion weightings, or b-factors, was increased to allow estimation of tissue perfusion based on the intravoxel incoherent motion (IVIM) model. Apparent diffusion coefficients measured with the proposed segmented method were compared with those obtained with conventional single-shot echo-planar imaging (EPI). RESULTS Compared with single-shot EPI, the segmented method resulted in faster acquisition with 2-fold improvement in spatial resolution and a greater than 3-fold improvement in geometric fidelity. Apparent diffusion coefficient values measured with the novel sequence demonstrated excellent agreement with those obtained from the conventional scan (R = 0.91 for bmax = 500 s/mm and R = 0.89 for bmax = 1400 s/mm). The IVIM perfusion fraction was 4.0% ± 2.7% for normal peripheral zone, 6.6% ± 3.6% for normal transition zone, and 4.4% ± 2.9% for suspected tumor lesions. CONCLUSIONS The proposed accelerated segmented prostate diffusion imaging sequence achieved improvements in both spatial resolution and geometric fidelity, along with concurrent quantification of IVIM perfusion.
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19
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Cordero-Grande L, Christiaens D, Hutter J, Price AN, Hajnal JV. Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage 2019; 200:391-404. [PMID: 31226495 PMCID: PMC6711461 DOI: 10.1016/j.neuroimage.2019.06.039] [Citation(s) in RCA: 158] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/31/2019] [Accepted: 06/17/2019] [Indexed: 11/28/2022] Open
Abstract
We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
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Affiliation(s)
- Lucilio Cordero-Grande
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Jo V Hajnal
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
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20
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Richardson ML, Amini B, Richards TL. Some new angles on the magic angle: what MSK radiologists know and don't know about this phenomenon. Skeletal Radiol 2018; 47:1673-1681. [PMID: 29995211 DOI: 10.1007/s00256-018-3011-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 06/11/2018] [Accepted: 06/17/2018] [Indexed: 02/02/2023]
Abstract
PURPOSE Magic angle effects (MAE) are well-recognized in musculoskeletal (MSK) MRI. With short TE acquisitions, the signal intensity of tendons, ligaments, and menisci depend on their orientation relative to the main magnetic field (B0). An interactive resident physics teaching module simulating MR imaging of a tendon forced us to identify and correct several misconceptions we had about MAE. We suspected these misconceptions were shared by other MSK radiologists. MATERIALS AND METHODS We surveyed members of the Society of Academic Bone Radiologists (SABR) regarding which pulse sequences, acquisition parameters, tissues and angles relative to B0 were most likely to produce MAE. RESULTS Survey respondents knew that MAE strongly depend on TE and commonly appear on T1W, FSE and PD sequences, but were less aware that MAE may also appear on T2W, STIR and DWI sequences. They knew of MAE effects in tendons, ligaments and cartilage, but were less aware of those in entheses, peripheral nerves and intervertebral discs. Respondents underestimated the wide angular range (full-width at half-maximum ≈ 40∘) over which significant MAE can be seen with short TE. CONCLUSIONS Collagen-containing tissues with parallel molecular alignment exhibit increased signal intensity when oriented at 55∘ relative to B0. Experienced MSK radiologists were found to underestimate the combinations of image parameters, pulse sequences, tissues and collagen orientations in which significant MAE may be seen. Our survey results highlight the need for ongoing MR physics education for practicing radiologists.
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Affiliation(s)
| | - Behrang Amini
- Department of Radiology, M. D. Anderson Cancer Center, Houston, TX, USA
| | - Todd L Richards
- Department of Radiology, University of Washington, Seattle, WA, USA
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21
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Rabanillo-Viloria I, Zhu A, Aja-Fernández S, Alberola-López C, Hernando D. Computation of exact g-factor maps in 3D GRAPPA reconstructions. Magn Reson Med 2018; 81:1353-1367. [PMID: 30229566 DOI: 10.1002/mrm.27469] [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: 03/07/2018] [Revised: 07/05/2018] [Accepted: 07/06/2018] [Indexed: 11/12/2022]
Abstract
PURPOSE To characterize the noise distributions in 3D-MRI accelerated acquisitions reconstructed with GRAPPA using an exact noise propagation analysis that operates directly in k-space. THEORY AND METHODS We exploit the extensive symmetries and separability in the reconstruction steps to account for the correlation between all the acquired k-space samples. Monte Carlo simulations and multi-repetition phantom experiments were conducted to test both the accuracy and feasibility of the proposed method; a high-resolution in-vivo experiment was performed to assess the applicability of our method to clinical scenarios. RESULTS Our theoretical derivation shows that the direct k-space analysis renders an exact noise characterization under the assumptions of stationarity and uncorrelation in the original k-space. Simulations and phantom experiments provide empirical support to the theoretical proof. Finally, the high-resolution in-vivo experiment demonstrates the ability of the proposed method to assess the impact of the sub-sampling pattern on the overall noise behavior. CONCLUSIONS By operating directly in the k-space, the proposed method is able to provide an exact characterization of noise for any Cartesian pattern sub-sampled along the two phase-encoding directions. Exploitation of the symmetries and separability into independent blocks through the image reconstruction procedure allows us to overcome the computational challenges related to the very large size of the covariance matrices involved.
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Affiliation(s)
| | - Ante Zhu
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | | | | | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
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22
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Yu CH, Prado R, Ombao H, Rowe D. A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2018.1476244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Cheng-Han Yu
- Department of Applied Mathematics & Statistics, University of California at Santa Cruz, Santa Cruz, CA
| | - Raquel Prado
- Department of Applied Mathematics & Statistics, University of California at Santa Cruz, Santa Cruz, CA
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
| | - Daniel Rowe
- Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI
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23
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Jansen JFA, Parra C, Lu Y, Shukla-Dave A. Evaluation of Head and Neck Tumors with Functional MR Imaging. Magn Reson Imaging Clin N Am 2016; 24:123-133. [PMID: 26613878 DOI: 10.1016/j.mric.2015.08.011] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Head and neck cancer is one of the most common cancers worldwide. MR imaging-based diffusion and perfusion techniques enable the noninvasive assessment of tumor biology and physiology, which supplement information obtained from standard structural scans. Diffusion and perfusion MR imaging techniques provide novel biomarkers that can aid monitoring in pretreatment, during treatment, and posttreatment stages to improve patient selection for therapeutic strategies; provide evidence for change of therapy regime; and evaluate treatment response. This review discusses pertinent aspects of the role of diffusion and perfusion MR imaging and computational analysis methods in studying head and neck cancer.
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Affiliation(s)
- Jacobus F A Jansen
- Department of Radiology, Maastricht University Medical Center, PO Box 5800, Maastricht 6202 AZ, The Netherlands.
| | - Carlos Parra
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Yonggang Lu
- Department of Radiation Oncology, University of Washington, 4921 Parkview Pl, St Louis, MO 63110, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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24
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Brown AM, Nagala S, McLean MA, Lu Y, Scoffings D, Apte A, Gonen M, Stambuk HE, Shaha AR, Tuttle RM, Deasy JO, Priest AN, Jani P, Shukla‐Dave A, Griffiths J. Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI. Magn Reson Med 2016; 75:1708-16. [PMID: 25995019 PMCID: PMC4654719 DOI: 10.1002/mrm.25743] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 03/05/2015] [Accepted: 04/02/2015] [Indexed: 12/20/2022]
Abstract
PURPOSE Ultrasound-guided fine needle aspirate cytology fails to diagnose many malignant thyroid nodules; consequently, patients may undergo diagnostic lobectomy. This study assessed whether textural analysis (TA) could noninvasively stratify thyroid nodules accurately using diffusion-weighted MRI (DW-MRI). METHODS This multi-institutional study examined 3T DW-MRI images obtained with spin echo echo planar imaging sequences. The training data set included 26 patients from Cambridge, United Kingdom, and the test data set included 18 thyroid cancer patients from Memorial Sloan Kettering Cancer Center (New York, New York, USA). Apparent diffusion coefficients (ADCs) were compared over regions of interest (ROIs) defined on thyroid nodules. TA, linear discriminant analysis (LDA), and feature reduction were performed using the 21 MaZda-generated texture parameters that best distinguished benign and malignant ROIs. RESULTS Training data set mean ADC values were significantly different for benign and malignant nodules (P = 0.02) with a sensitivity and specificity of 70% and 63%, respectively, and a receiver operator characteristic (ROC) area under the curve (AUC) of 0.73. The LDA model of the top 21 textural features correctly classified 89/94 DW-MRI ROIs with 92% sensitivity, 96% specificity, and an AUC of 0.97. This algorithm correctly classified 16/18 (89%) patients in the independently obtained test set of thyroid DW-MRI scans. CONCLUSION TA classifies thyroid nodules with high sensitivity and specificity on multi-institutional DW-MRI data sets. This method requires further validation in a larger prospective study. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.
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Affiliation(s)
- Anna M. Brown
- Cancer Research UK Cambridge Institute, University of CambridgeLi Ka Shing CentreRobinson WayCambridgeUnited Kingdom
- Duke University School of MedicineDurhamNorth CarolinaUSA
| | - Sidhartha Nagala
- Addenbrooke's Hospital Department of OtolaryngologyCambridgeUnited Kingdom
| | - Mary A. McLean
- Cancer Research UK Cambridge Institute, University of CambridgeLi Ka Shing CentreRobinson WayCambridgeUnited Kingdom
| | - Yonggang Lu
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Daniel Scoffings
- Addenbrooke's Hospital Department of RadiologyCambridgeUnited Kingdom
| | - Aditya Apte
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Mithat Gonen
- Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Hilda E. Stambuk
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Ashok R. Shaha
- Department of SurgeryMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - R. Michael Tuttle
- Department of MedicineMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Joseph O. Deasy
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Andrew N. Priest
- Addenbrooke's Hospital Department of RadiologyCambridgeUnited Kingdom
| | - Piyush Jani
- Cambridge Teaching Hospitals ENT DepartmentCambridgeUnited Kingdom
| | - Amita Shukla‐Dave
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - John Griffiths
- Cancer Research UK Cambridge Institute, University of CambridgeLi Ka Shing CentreRobinson WayCambridgeUnited Kingdom
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Sprenger T, Sperl JI, Fernandez B, Haase A, Menzel MI. Real valued diffusion‐weighted imaging using decorrelated phase filtering. Magn Reson Med 2016; 77:559-570. [DOI: 10.1002/mrm.26138] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 11/26/2015] [Accepted: 12/24/2015] [Indexed: 01/26/2023]
Affiliation(s)
- Tim Sprenger
- Technische Universität München, Institute of Medical Engineering, Munich, Germany.,GE Global Research, Munich, Germany
| | | | | | - Axel Haase
- Technische Universität München, Institute of Medical Engineering, Munich, Germany
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26
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Sprenger T, Sperl JI, Fernandez B, Golkov V, Eidner I, Sämann PG, Czisch M, Tan ET, Hardy CJ, Marinelli L, Haase A, Menzel MI. Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes. Magn Reson Med 2016; 76:1684-1696. [DOI: 10.1002/mrm.26008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 08/28/2015] [Accepted: 09/15/2015] [Indexed: 01/12/2023]
Affiliation(s)
- Tim Sprenger
- Technische Universität München; Institute of Medical Engineering; Munich Germany
- GE Global Research; Munich Germany
| | | | | | - Vladimir Golkov
- Technische Universität München; Institute of Medical Engineering; Munich Germany
- Technische Universität München; Computer Vision Group; Munich Germany
| | - Ines Eidner
- Max Planck Institute of Psychiatry; Munich Germany
| | | | | | - Ek T. Tan
- GE Global Research; Niskayuna New York USA
| | | | | | - Axel Haase
- Technische Universität München; Institute of Medical Engineering; Munich Germany
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27
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Lu Y, Moreira AL, Hatzoglou V, Stambuk HE, Gonen M, Mazaheri Y, Deasy JO, Shaha AR, Tuttle RM, Shukla-Dave A. Using diffusion-weighted MRI to predict aggressive histological features in papillary thyroid carcinoma: a novel tool for pre-operative risk stratification in thyroid cancer. Thyroid 2015; 25:672-80. [PMID: 25809949 PMCID: PMC4490628 DOI: 10.1089/thy.2014.0419] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Initial management recommendations of papillary thyroid carcinoma (PTC) are very dependent on preoperative studies designed to evaluate the presence of PTC with aggressive features. The purpose of this study was to evaluate whether diffusion-weighted magnetic resonance imaging (DW-MRI) before surgery can be used as a tool to stratify tumor aggressiveness in patients with PTC. METHODS In this prospective study, 28 patients with PTC underwent DW-MRI studies on a three Tesla MR scanner prior to thyroidectomy. Due to image quality, 21 patients were finally suitable for further analysis. Apparent diffusion coefficients (ADCs) of normal thyroid tissues and PTCs for 21 patients were calculated. Tumor aggressiveness was defined by surgical histopathology. The Mann-Whitney U test was used to compare the difference in ADCs among groups of normal thyroid tissues and PTCs with and without features of tumor aggressiveness. Receiver operating characteristic (ROC) analysis was performed to assess the discriminative specificity, sensitivity, and accuracy of and determine the cutoff value for the ADC in stratifying PTCs with tumor aggressiveness. RESULTS There was no significant difference in ADC values between normal thyroid tissues and PTCs. However, ADC values of PTCs with extrathyroidal extension (ETE; 1.53±0.25×10(-3) mm2/s) were significantly lower than corresponding values from PTCs without ETE (2.37±0.67×10(-3) mm2/s; p<0.005). ADC values identified 3 papillary carcinoma patients with extrathyroidal extension that would have otherwise been candidates for observation based on ultrasound evaluations. The cutoff value of ADC to discriminate PTCs with and without ETE was determined at 1.85×10(-3) mm2/s with a sensitivity of 85%, specificity of 85%, and ROC curve area of 0.85. CONCLUSION ADC value derived from DW-MRI before surgery has the potential to stratify ETE in patients with PTCs.
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Affiliation(s)
- Yonggang Lu
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Andre L. Moreira
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Ashok R. Shaha
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
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28
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Paulson ES, Erickson B, Schultz C, Allen Li X. Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. Med Phys 2014; 42:28-39. [DOI: 10.1118/1.4896096] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
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29
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André ED, Grinberg F, Farrher E, Maximov II, Shah NJ, Meyer C, Jaspar M, Muto V, Phillips C, Balteau E. Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging. PLoS One 2014; 9:e94531. [PMID: 24722363 PMCID: PMC3983191 DOI: 10.1371/journal.pone.0094531] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Accepted: 03/18/2014] [Indexed: 11/18/2022] Open
Abstract
Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.
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Affiliation(s)
- Elodie D. André
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Farida Grinberg
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
- Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany
- * E-mail:
| | | | - Ivan I. Maximov
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine - 4, Juelich, Germany
- Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany
| | | | - Mathieu Jaspar
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Vincenzo Muto
- Cyclotron Research Centre, University of Liège, Liège, Belgium
| | - Christophe Phillips
- Cyclotron Research Centre, University of Liège, Liège, Belgium
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
| | - Evelyne Balteau
- Cyclotron Research Centre, University of Liège, Liège, Belgium
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Maximov II, Farrher E, Grinberg F, Shah NJ. Spatially variable Rician noise in magnetic resonance imaging. Med Image Anal 2011; 16:536-48. [PMID: 22209560 DOI: 10.1016/j.media.2011.12.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2011] [Revised: 11/25/2011] [Accepted: 12/02/2011] [Indexed: 12/01/2022]
Abstract
Magnetic resonance images tend to be influenced by various random factors usually referred to as "noise". The principal sources of noise and related artefacts can be divided into two types: arising from hardware (acquisition coil arrays, gradient coils, field inhomogeneity); and arising from the subject (physiological noise including body motion, cardiac pulsation or respiratory motion). These factors negatively affect the resolution and reproducibility of the images. Therefore, a proper noise treatment is important for improving the performance of clinical and research investigations. Noise reduction becomes especially critical for the images with a low signal-to-noise ratio, such as those typically acquired in diffusion tensor imaging at high diffusion weightings. The standard methods of signal correction usually assume a uniform distribution of the standard deviation of the noise across the image and evaluate a single correction parameter for the whole image. We pursue a more advanced approach based on the assumption of an inhomogeneous distribution of noise in space and evaluate correction factors for each voxel individually. The Rician nature of the underlying noise is considered for low and high signal-to-noise ratios. The approach developed here has been examined using numerical simulations and in vivo brain diffusion tensor imaging experiments. The efficacy and usefulness of this approach is demonstrated here and the resultant effective tool is described.
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Affiliation(s)
- Ivan I Maximov
- Institute of Neuroscience and Medicine (INM-4), Research Centre Jülich GmbH, Jülich, Germany.
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31
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Kristoffersen A. Estimating non-gaussian diffusion model parameters in the presence of physiological noise and rician signal bias. J Magn Reson Imaging 2011; 35:181-9. [PMID: 21972173 DOI: 10.1002/jmri.22826] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Accepted: 09/02/2011] [Indexed: 11/07/2022] Open
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