Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration
•Temporal Diffusion Ratio (TDR) maps areas of restricted diffusion using two different gradient waveforms.•The two optimised gradient waveforms have: long δ + low G and short δ + high G.•If data is noisy, calculating TDR using HARDI acqisition subsets increases accuracy.•First demonstration of TDR i...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 269; p. 119930 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.04.2023
Elsevier Limited Elsevier |
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| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2023.119930 |
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| Abstract | •Temporal Diffusion Ratio (TDR) maps areas of restricted diffusion using two different gradient waveforms.•The two optimised gradient waveforms have: long δ + low G and short δ + high G.•If data is noisy, calculating TDR using HARDI acqisition subsets increases accuracy.•First demonstration of TDR in pre-clinical imaging.•TDR values are strongly correlated with axon diameter in rat spinal cord.
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. |
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| AbstractList | •Temporal Diffusion Ratio (TDR) maps areas of restricted diffusion using two different gradient waveforms.•The two optimised gradient waveforms have: long δ + low G and short δ + high G.•If data is noisy, calculating TDR using HARDI acqisition subsets increases accuracy.•First demonstration of TDR in pre-clinical imaging.•TDR values are strongly correlated with axon diameter in rat spinal cord.
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique ( Dell’Acqua et al., proc. ISMRM 2019 ) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general. |
| ArticleNumber | 119930 |
| Author | Palombo, Marco Warner, William Drobnjak, Ivana Cruz, Renata Jones, Derek K. Dell'Acqua, Flavio Callaghan, Ross Shemesh, Noam Ianus, Andrada |
| AuthorAffiliation | d Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal e AINOSTICS Ltd., Manchester, United Kingdom b Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom c School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom f NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom a Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom |
| AuthorAffiliation_xml | – name: c School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom – name: e AINOSTICS Ltd., Manchester, United Kingdom – name: b Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom – name: f NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom – name: a Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom – name: d Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal |
| Author_xml | – sequence: 1 givenname: William surname: Warner fullname: Warner, William organization: Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom – sequence: 2 givenname: Marco surname: Palombo fullname: Palombo, Marco organization: Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom – sequence: 3 givenname: Renata surname: Cruz fullname: Cruz, Renata organization: Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal – sequence: 4 givenname: Ross orcidid: 0000-0003-1178-6005 surname: Callaghan fullname: Callaghan, Ross organization: AINOSTICS Ltd., Manchester, United Kingdom – sequence: 5 givenname: Noam surname: Shemesh fullname: Shemesh, Noam organization: Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal – sequence: 6 givenname: Derek K. surname: Jones fullname: Jones, Derek K. organization: Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom – sequence: 7 givenname: Flavio surname: Dell'Acqua fullname: Dell'Acqua, Flavio organization: NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom – sequence: 8 givenname: Andrada orcidid: 0000-0001-9893-1724 surname: Ianus fullname: Ianus, Andrada email: andrada.ianus@neuro.fchampalimaud.org organization: Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal – sequence: 9 givenname: Ivana orcidid: 0000-0002-3989-1715 surname: Drobnjak fullname: Drobnjak, Ivana email: i.drobnjak@ucl.ac.uk organization: Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36750150$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_hbm_26420 crossref_primary_10_1002_nbm_5087 crossref_primary_10_7554_eLife_101069_3 crossref_primary_10_1016_j_jmr_2024_107745 crossref_primary_10_1002_mp_17453 crossref_primary_10_3389_fninf_2023_1208073 crossref_primary_10_7554_eLife_101069 |
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| Keywords | Spinal cord TDR Diffusion Microstructure Optimization Axon diameter |
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| SubjectTerms | Amyotrophic lateral sclerosis Animals Axon diameter Axons Computer Simulation Diffusion Diffusion Magnetic Resonance Imaging - methods Image Processing, Computer-Assisted - methods Kurtosis Microstructure Optimization Rats Spinal cord TDR |
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| Title | Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration |
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