MR fingerprinting of the prostate

Lo, Wei‐Ching; Jiang, Yun; Ahad, James; Gulani, Vikas; Seiberlich, Nicole

doi:10.1007/s10334-022-01012-8

Cited by 4 publications

(5 citation statements)

References 104 publications

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“…It should also be noted that some of the signal loss in the prostate is believed to originate from streaking artefacts, as this is signal propagating through the image. A few studies have been published on MRF of the prostate in prostate cancer patients, 3,5,[32][33][34][35][36] primarily relying on a spiral readout trajectory. Lo et al 5 concluded that prostate MRF measurements of T 1 and T 2 are repeatable and reproducible between MRI scanners at different centres, reporting T 1 and T 2 values noticeably higher than our radial MRF values.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, magnetic resonance fingerprinting (MRF) T 1 and T 2 maps, together with apparent diffusion coefficient (ADC) maps, have shown promise in discriminating cancer from noncancer and healthy prostate tissue in both the peripheral and transition zones. 3 One of the key features of MRF is the use of incoherent undersampling in combination with a model-based reconstruction to generate multiparametric maps. [4][5][6] Whereas in traditional MRI extreme acceleration factors result in unacceptable artefacts, these incoherent undersampling artefacts have a relatively benign effect on the MRF reconstruction process.…”

Section: Introductionmentioning

confidence: 99%

“…Looking forward, novel methods open the door to quantitative prostate MRI in clinically viable acquisition times. In particular, magnetic resonance fingerprinting (MRF) T 1 and T 2 maps, together with apparent diffusion coefficient (ADC) maps, have shown promise in discriminating cancer from noncancer and healthy prostate tissue in both the peripheral and transition zones 3 …”

Section: Introductionmentioning

confidence: 99%

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Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region‐optimised virtual coils

Sørland,

Trimble,

et al. 2024

NMR in Biomedicine

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High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak‐like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region‐optimised virtual (ROVir) coils is a beamforming‐based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k‐space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal‐to‐noise ratio (SNR) with and without streak correction were examined. In addition, a purpose‐built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak‐like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region‐optimised virtual coils

Sørland,

Trimble,

et al. 2024

NMR in Biomedicine

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“…In Figure 8c, TI short and TI long are chosen to show small changes in gray matter T 1 . It would be interesting to compare this sequence with MP2RAGE and FLAWS hc for detecting cortical plaques in MS. MR fingerprinting has suggested that the primary difference between normal prostate tissue and prostate cancer is a decrease in tissue T 1 [27]. However, differences in T 1 between normal and cancerous tissues in the prostate are insufficient to produce contrast on standard FSE or IR T 1 -weighted FSE sequences.…”

Section: Clinical Experience With Dsirmentioning

confidence: 99%

Ultra-High Contrast MRI: Using Divided Subtracted Inversion Recovery (dSIR) and Divided Echo Subtraction (dES) Sequences to Study the Brain and Musculoskeletal System

Cornfeld,

Condron,

Newburn

et al. 2024

Bioengineering

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Divided and subtracted MRI is a novel imaging processing technique, where the difference of two images is divided by their sum. When the sequence parameters are chosen properly, this results in images with a high T1 or T2 weighting over a small range of tissues with specific T1 and T2 values. In the T1 domain, we describe the implementation of the divided Subtracted Inversion Recovery Sequence (dSIR), which is used to image very small changes in T1 from normal in white matter. dSIR has shown widespread changes in otherwise normal-appearing white matter in patients suffering from mild traumatic brain injury (mTBI), substance abuse, and ischemic leukoencephalopathy. It can also be targeted to measure small changes in T1 from normal in other tissues. In the T2 domain, we describe the divided echo subtraction (dES) sequence that is used to image musculoskeletal tissues with a very short T2*. These tissues include fascia, tendons, and aponeuroses. In this manuscript, we explain how this contrast is generated, review how these techniques are used in our research, and discuss the current challenges and limitations of this technique.

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“…1 One quantitative technique is MR fingerprinting (MRF), a US Food and Drug Administration-approved MRI method that performs simultaneous measurements of various tissue properties, including T 1 and T 2 relaxation times. 2 Quantitative analysis of MRF maps has been beneficial in better understanding and tracking of healthy function, disease diagnosis, and disease progression in areas such as the brain, [3][4][5][6][7] heart, 8,9 liver, [10][11][12] prostate, 13 and kidney. 14,15 However, before any widespread clinical adoption, quantitative methods such as MRF need to offer significant, provable advantages over established, qualitative MRI scans.…”

Section: Introductionmentioning

confidence: 99%

Quantifying 3D MR fingerprinting (3D‐MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases

Dupuis,

Chen,

Hansen

et al. 2024

Magnetic Resonance in Med

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PurposeQuantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point‐of‐care than weighted imaging. However, few direct cross‐modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross‐modal imaging performance in vivo via atlas‐based sampling.MethodsWe acquire whole‐brain 3D‐MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions.ResultsVoxel values within MRF‐derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor‐reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images.ConclusionMRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.

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MR fingerprinting of the prostate

Cited by 4 publications

References 104 publications

Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region‐optimised virtual coils

Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region‐optimised virtual coils

Ultra-High Contrast MRI: Using Divided Subtracted Inversion Recovery (dSIR) and Divided Echo Subtraction (dES) Sequences to Study the Brain and Musculoskeletal System

Quantifying 3D MR fingerprinting (3D‐MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases

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