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arxiv: 1906.08195 · v1 · pith:G46V6H2Wnew · submitted 2019-06-19 · ⚛️ physics.med-ph · eess.IV

Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting

Arnold Julian Vinoj Benjamin , Pedro A. Gomez , Mohammad Golbabaee , Zaid Mahbub , Tim Sprenger , Marion I. Menzel Michael Davies , Ian Marshall This is my paper

Pith reviewed 2026-05-25 19:49 UTC · model grok-4.3

classification ⚛️ physics.med-ph eess.IV
keywords MR FingerprintingEcho Planar ImagingT1 mappingT2 mappingCartesian readoutmulti-shot EPIparametric mappingaccelerated imaging
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The pith

Multi-shot EPI readout produces T1 and T2 maps matching spiral MR Fingerprinting within 3-4 percent deviation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a Cartesian version of MR Fingerprinting that acquires highly subsampled images with a 16-shot echo planar imaging sequence and recovers T1 and T2 maps via an iterative projection algorithm. The approach uses a linearly varying flip angle train and completes each slice in 8 seconds. When tested on a phantom and a healthy volunteer brain, the resulting maps agree closely with those from a conventional spiral MRF scan using identical parameters. A reader would care because the work supplies a practical Cartesian alternative that could reduce dependence on spiral trajectories for quantitative imaging.

Core claim

The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral - MRF. Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (deviation less than 3% for T1 and less than 4% for T2) for the healthy volunteer brain. The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual quality of the multi-parametric maps generated by the multi-shot EPI-MRF and spiral-MRF implementations were comparable.

What carries the argument

The 16-shot EPI readout paired with an iterative projection algorithm that recovers T1 and T2 from the subsampled Cartesian data.

If this is right

  • Acquisition completes in 8 seconds per slice.
  • T1 and T2 maps from the EPI implementation stay within 3 percent and 4 percent of spiral maps respectively.
  • Map values match previously reported literature ranges for brain tissue.
  • The method supplies a Cartesian readout option that can serve as an alternative to spiral MRF.
  • Visual quality of the resulting multi-parametric maps is comparable to the spiral version.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The Cartesian trajectory may integrate more readily with standard clinical scanner hardware that already supports EPI readouts.
  • Extending the 8-second per-slice timing to contiguous multi-slice coverage could shorten total exam times for whole-brain mapping.
  • The same iterative recovery step might be tested on other subsampling patterns or flip-angle schedules without altering the core hardware.
  • Because the work validates only healthy volunteer data, application to pathology would require separate confirmation of bias levels.

Load-bearing premise

The iterative projection algorithm recovers T1 and T2 values from the subsampled 16-shot EPI data without introducing systematic biases different from those of the spiral reference.

What would settle it

A side-by-side scan of the same brain regions in which T2 values from the 16-shot EPI differ systematically by more than 4 percent from the spiral reference would falsify the reported agreement.

Figures

Figures reproduced from arXiv: 1906.08195 by Arnold Julian Vinoj Benjamin, Ian Marshall, Marion I. Menzel Michael Davies, Mohammad Golbabaee, Pedro A. Gomez, Tim Sprenger, Zaid Mahbub.

Figure 1
Figure 1. Figure 1: T1-T2 sensitivity of exemplary values of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) at 3T that were simulated for the unbalanced SSFP sequence using the EPG model for a) Linear Ramp FA Pattern from 1° to 70° with N = 500 repetitions and b) Pseudorandom FA pattern with N = 1000 repetitions that was used by Jiang et al. [18] [PITH_FULL_IMAGE:figures/full_fig_p020_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) The 16 shot EPI trajectory showing Gx, Gy and Gz gradients. Note that the Gy gradients are slightly different for each of the 16 shots indicating that different lines of ky space are acquired at every shot. The spoiler gradient Gz dephases the transverse magnetization for every TR making the sequence unbalanced [18]. (b) The corresponding x and y zero order gradient moments for Gx and Gy were nulled to… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Figure showing the T1 sensitivity and (b) T2 sensitivity of the sequence for discriminating dictionary atoms when a variable flip angle ramp that linearly varied between 1° to 70° was used during the acquisition for 500 repetitions. Note that the Inversion pulse causes the initial T1 discrimination in (a). These sensitivities were observed at practical T1 and T2 values. Only a subset of the high resolu… view at source ↗
Figure 4
Figure 4. Figure 4: (a) Figure showing the temporal signal curve of one representative voxel from a subsampled EPI-MRF image along with its matched dictionary entry for a) phantom and b) healthy volunteer. Note that dictionary matching (DM) still works even in the presence of uniform subsampling artefacts in the image due to the noise￾like behavior of the signal in the temporal domain [PITH_FULL_IMAGE:figures/full_fig_p023_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Figure showing the highly aliased zero-filled (ZF) images and Iterative Projection Algorithm (IPA) reconstructed images at di erent repetition indexes ‘t’ of the tube phantom and the healthy volunteer for a) EPI - MRF (ramped FA, TR = 16 ms, N = 500 repetitions) and b) Spiral - MRF (ramped FA, TR = 16 ms, N = 500 repetitions) [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Figure showing the T1 and T2 maps (in seconds) of the tube phantom generated after Dictionary Matching (DM) for i) Spiral - MRF (ramped FA, TR = 16 ms, N = 500 repetitions) and ii) EPI - MRF (ramped FA, TR = 16 ms, N = 500 repetitions) [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (a) Arrangement of tubes with different T1 and T2 values in phantom. (b) Mean T1 (± standard deviation) of various tubes in phantom comparing Spiral - MRF (pseudorandom FA, varying TR and N = 1000 repetitions) in blue, Spiral - MRF (ramped FA, TR = 16 ms and N = 500 repetitions) in orange and EPI - MRF (ramped FA, TR = 16 ms and N = 500 repetitions) in gray. (c) Corresponding mean T2 values (± standard dev… view at source ↗
Figure 8
Figure 8. Figure 8: Figure showing the T1 and T2 maps (in seconds) of the healthy volunteer [PITH_FULL_IMAGE:figures/full_fig_p027_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: (a) Segmentation of white matter (WM) for the healthy volunteer showing [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Figure showing the T1 maps of a healthy volunteer generated using Dictionary Matching (DM) and Iterative Projection Algorithm (IPA) respectively for EPI - MRF (a). A comparison of IPA convergence is shown for EPI - MRF and Spiral - MRF (b). Note that DM is equivalent to a single iteration of IPA [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Figure showing the T1 maps of a healthy volunteer generated using [PITH_FULL_IMAGE:figures/full_fig_p030_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Supplementary Figure 1: Figure showing the T1 [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Supplementary Figure 2: (a) Figure showing the T1 maps of a healthy [PITH_FULL_IMAGE:figures/full_fig_p033_13.png] view at source ↗
read the original abstract

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The accuracy of parametric map estimations were improved by using an iterative projection algorithm. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on a phantom and a healthy volunteer brain in vivo. Results: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (deviation less than 3% for T1 and less than 4% for T2) for the healthy volunteer brain. The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual quality of the multi-parametric maps generated by the multi-shot EPI-MRF and spiral-MRF implementations were comparable. Conclusion: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral - MRF. This multi-shot approach achieved provides an alternative for performing MRF using an accelerated Cartesian readout, thereby increasing the potential usability of MRF.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper develops a Cartesian MR Fingerprinting implementation using a 16-shot EPI readout with a linearly varying flip-angle train for simultaneous T1/T2 mapping. An iterative projection algorithm is applied to improve parametric map accuracy from highly subsampled data. The method is tested on a phantom and one healthy volunteer brain, with acquisition time of 8 s per slice, and results are compared to a conventional spiral MRF implementation using identical parameters. The central empirical claim is that joint T1/T2 estimates agree with the spiral reference to within <3% (T1) and <4% (T2) in the volunteer, with comparable visual map quality and literature-consistent values.

Significance. If the iterative projection recovers parameters without trajectory-specific bias, the work supplies a practical accelerated Cartesian MRF alternative that could increase accessibility by using standard EPI hardware rather than specialized spiral trajectories. The short per-slice time is a clear practical advantage. However, the current evidence base—one volunteer, no error bars or statistical tests—limits the strength of the claim that the approach is bias-free relative to spiral MRF.

major comments (2)
  1. [Results] Results section (volunteer comparison paragraph): the headline agreement (<3% T1, <4% T2) is reported from a single subject without ROI statistics, error bars, or multi-subject data; this leaves open whether the iterative projection introduces systematic EPI-specific biases (ghosting, eddy currents, slice-profile effects) that differ from the spiral reference, directly undermining the central claim of equivalence.
  2. [Methods] Methods (iterative projection description): no convergence criteria, iteration count, residual aliasing analysis, or explicit validation of the projection step against ground-truth phantoms are supplied; without these, it is impossible to confirm that the algorithm recovers T1/T2 equivalently for the two trajectories.
minor comments (2)
  1. [Abstract] Abstract: grammar error ('The accuracy of parametric map estimations were improved').
  2. [Abstract] Abstract/Results: the statement that T1/T2 'agree with the conventional values previously reported in the literature' lacks quantitative comparison or citation of the reference values used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestions. We address each of the major comments below, indicating the revisions we plan to make to the manuscript.

read point-by-point responses

  1. Referee: [Results] Results section (volunteer comparison paragraph): the headline agreement (<3% T1, <4% T2) is reported from a single subject without ROI statistics, error bars, or multi-subject data; this leaves open whether the iterative projection introduces systematic EPI-specific biases (ghosting, eddy currents, slice-profile effects) that differ from the spiral reference, directly undermining the central claim of equivalence.

    Authors: We agree that the volunteer data is from a single subject and that additional statistical measures would strengthen the presentation. In the revised manuscript, we will include ROI-based mean and standard deviation values for T1 and T2 in the volunteer brain to provide quantitative measures of agreement beyond the headline percentages. We will also add a brief discussion addressing potential EPI-specific artifacts such as ghosting and eddy currents, noting that the close agreement with the spiral reference and literature values indicates that any such biases are minimal in this implementation. While multi-subject data would be ideal for a more comprehensive validation, the current study serves as a proof-of-concept demonstration, with the phantom results providing supporting evidence across multiple measurements. revision: partial

  2. Referee: [Methods] Methods (iterative projection description): no convergence criteria, iteration count, residual aliasing analysis, or explicit validation of the projection step against ground-truth phantoms are supplied; without these, it is impossible to confirm that the algorithm recovers T1/T2 equivalently for the two trajectories.

    Authors: We acknowledge the need for more detailed description of the iterative projection algorithm. In the revised version of the manuscript, we will expand the Methods section to include the specific convergence criteria used, the number of iterations performed, an analysis of residual aliasing after projection, and explicit validation results of the projection step using the ground-truth phantom data. This will allow readers to better assess the performance of the algorithm for both trajectories. revision: yes

Circularity Check

0 steps flagged

No circularity; results from direct empirical comparison to spiral reference

full rationale

The paper develops a multi-shot EPI Cartesian MRF sequence and validates it by acquiring data on phantom and one volunteer, applying an iterative projection algorithm to reconstruct T1/T2 maps, then reporting quantitative agreement (<3% T1, <4% T2) with a conventional spiral MRF implementation using identical acquisition parameters. No derivation chain, equations, or first-principles predictions are presented that reduce by construction to fitted inputs, self-citations, or ansatzes. The central result is an external empirical match rather than a self-referential claim; the iterative algorithm's performance is assessed via that match, not defined by it. This is a standard non-circular validation study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on standard MRI physics (Bloch equations, relaxation) and the unstated details of the iterative projection algorithm; no new entities or fitted constants are introduced in the abstract.

pith-pipeline@v0.9.0 · 5813 in / 974 out tokens · 20101 ms · 2026-05-25T19:49:50.788452+00:00 · methodology

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Reference graph

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    M. Golbabaee, Z. Chen, Y. Wiaux, M. E. Davies, Cover tree compressed sensing for fast mr fingerprint recovery, in: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1–6. FIGURES Figure 1: T1-T2 sensitivity of exemplary values of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) at 3T that wer...

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