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REVIEW 3 major objections 7 minor 31 references

MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI

T0 review · 3 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that feeding a short sequence of neighboring cardiac MRI frames, instead of a single target frame, into a Mamba-based bi-directional state-space network produces smoother and more accurate deformation fields for ED-to-ES…

desk verdict Plausible Mamba-based cardiac motion tracker whose sequence-input advantage is asserted more strongly than the quantitative evidence supports. read the letter →

arxiv 2507.17678 v1 pith:T2FCNBDY submitted 2025-07-23 eess.IV cs.CV

Jiahui Yin , Xinxing Cheng , Jinming Duan , Yan Pang , Declan O'Regan , Hadrien Reynaud , Qingjie Meng This is my paper · ORCID
classification eess.IVcs.CV
keywords cardiacmotiontrackingMambastatespacemodeldeformableimageregistrationcineMRItemporalconsistencymyocardialstrainsequentialimages
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Cardiac motion tracking in cine MRI is usually done by registering one reference frame to one target frame, which ignores the heart's continuous motion. This paper argues that including the target frame's neighbors in the input gives the network temporal context, leading to smoother and more consistent motion fields. The proposed MCM network processes a five-frame target sequence with bi-directional Mamba scanning and a dual-path fusion head, and on the ACDC and M&Ms datasets it achieves higher Dice overlap when warping end-diastolic segmentations to end-systole, plus lower percentages of negative Jacobian determinants and lower volume distortion than dDemons, VoxelMorph, TransMorph, and MambaMorph. A sympathetic reader would care because temporal consistency is exactly what strain analysis and cardiac function assessment need, and the method promises it without a large increase in inference time.

What carries the argument

The load-bearing components are (1) the bi-directional scanning Mamba (BiSM) block, which splits the deformation feature map into spatial positions, orders those positions along the temporal axis in both forward and backward directions, feeds each direction through its own state-space model (SSM), and sums the two outputs to capture temporal dynamics from both sides; and (2) the dual-path fusion head (DFH), which applies forward and backward 3D convolutions along the sequence dimension and averages the results before a 2D convolution outputs the motion field Φt. Together they let the network integrate neighboring frames without explicitly learning a recurrence over the whole cardiac cycle, and the paper attributes the gains in temporal consistency and smoothness to this design.

What would settle it

Warp the end-diastolic segmentation to every intermediate frame, not just end-systole, and compute Dice against manually traced or propagated segmentations on those frames; if a single-pair baseline such as TransMorph or MambaMorph matches or beats the sequence-based MCM on those intermediate frames, the core temporal-consistency advantage would be refuted.

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Extended reading notes

Core claim

The paper's central claim is that motion estimation for cine cardiac MRI improves when the network sees a sequence of target frames centered on the frame of interest, not just an isolated pair. The network takes the end-diastolic reference frame I0 together with St = {I_{t−K},...,I_t,...,I_{t+K}} (K=2, five frames total), pairs each target with I0, and processes the pairs through hierarchical bi-directional Mamba blocks. Each block scans the sequence both forward and backward in time, feeding the spatially ordered tokens into parallel state-space models and summing their outputs; a dual-path fusion head later combines forward and backward 3D convolutions on the fused feature map. On three short-axis slices of the ACDC dataset, the method reaches 84.6% mid-ventricular Dice versus 83.7% for TransMorph and 83.7% for MambaMorph, and it reports the lowest |J|<0% and ||J|−1| values on all three slices, which the paper interprets as smoother, more diffeomorphic, and more volume-preserving deformation fields. The same pattern holds on the M&Ms dataset. The paper also shows that removing the bi-directional blocks or the fusion head reduces performance, and that increasing the sequence length from one to five frames yields visibly smoother motion traces across the cardiac cycle.

Load-bearing premise

The quantitative evidence only spans the end-diastolic to end-systolic transition on three short-axis slices, so the paper's broader claim of temporally consistent motion across the whole cardiac cycle relies on visual inspection of motion traces rather than measurements on intermediate frames.

Editorial extensions

If this is right

  • If the central claim is correct, cardiac motion tracking systems can adopt sequence-aware architectures to obtain smoother deformation fields for the same registration loss, without changing the training objective or requiring paired intermediate-frame segmentations.
  • The bi-directional Mamba block and dual-path fusion head are modular; they could be inserted into other deformable registration pipelines that currently use single frame pairs.
  • Lower negative-Jacobian percentages and lower ||J|−1| suggest the estimated fields are closer to diffeomorphic and volume-preserving, which is directly relevant to strain and myocardial thickness measurements in clinical workflow.
  • The inference time stays around 17 ms even with five input frames, so the temporal-context benefit does not obviously trade away real-time usability.
  • The paper's loss function is a standard intensity-similarity plus smoothness regularization, so the sequence design can be combined with other losses such as segmentation-guided or biomechanics-informed terms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test the authors leave implicit is to evaluate motion fields on intermediate cardiac frames using propagated segmentations or tracked landmarks; if the temporal smoothing does not improve accuracy there, the central advantage over single-pair methods would be weakened.
  • Because the Mamba recurrence is linear in sequence length, pushing K beyond 2 (e.g., Nf = 7 or 9) might further smooth the motion traces, provided GPU memory, which the paper reports grows with Nf, can be managed.
  • The same sequence-pairing idea could be extended to 3D cardiac volumes or to across-slice temporal tracking in short-axis stacks, where the additional temporal context may reduce slice-to-slice inconsistencies.
  • One could probe the contribution of the dual-path fusion head separately with a diagnostic that compares forward-only, backward-only, and averaged fusion on intermediate frames, rather than only on the ED-to-ES endpoints.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This manuscript proposes MCM, an unsupervised deep network for 2D short-axis cine MRI cardiac motion tracking. MCM registers the end-diastolic reference frame I0 to a target frame It using, in addition to I0 and It, a sequence of K neighboring target frames (Nf = 2K+1; K = 2 in the experiments). The paired input sequence is processed by a hierarchical encoder of bi-directional Mamba blocks, whose bi-directional scanning Mamba (BiSM) applies forward and backward SSM scans, followed by a motion decoder with a dual-path fusion head (DFH) that combines forward/backward Conv3D fusion with 2D convolution to emit the deformation field Φt. The model is trained with an MSE similarity loss plus a gradient-based smoothness regularizer (Eq. 6). Experiments on ACDC and M&Ms compare MCM against dDemons, VoxelMorph, TransMorph and MambaMorph on three short-axis slices (basal, mid, apical) for ED-to-ES tracking, reporting Dice, |J|<0% and ||J|−1|. The paper reports higher Dice and lower Jacobian-based regularity metrics for MCM on both datasets, ablation results for BiSM, DFH, Nf and λ, and inference-time/VRAM measurements. The code is released.

Significance. If the reported results hold, this is a useful incremental contribution to cardiac motion tracking: it demonstrates that a Mamba-based architecture consuming a short window of neighboring target frames achieves slightly higher ED-ES Dice and markedly lower |J|<0% than strong registration baselines on two public datasets, with essentially flat inference time as Nf grows. The paper's strengths are that all baselines are run from their official implementations, the ablations isolate the contributions of the bi-directional scanning and the fusion head, the λ sensitivity is reported, and the code is released, which makes the main quantitative claims reproducible. The main limitation is that the paper's signature claim of smooth, temporally consistent tracking across the whole cardiac cycle is supported only by a qualitative figure (Fig. 4), while the quantitative evaluation covers only the ED-ES transition; the paper's own Discussion acknowledges that increasing Nf from 1 to 5 improves Dice by only about 0.5 percentage points.

major comments (3)
  1. [Discussion; Sec. 3 (Comparison study); Fig. 4] The central claim that sequence-based tracking yields 'smooth and temporally consistent' motion (abstract and contribution 1) is not quantitatively established. All quantitative comparisons in Tables 1 and 2, and the ablations in Table 2, evaluate only the ED-to-ES transition on three slices, because ground-truth contours exist only at ED and ES. The only whole-cycle evidence is the qualitative sampling-line visualization in Fig. 4, which contains no error bars, no comparison with any baseline method, and no statement of what quantity is plotted; the Discussion itself acknowledges that increasing Nf from 1 to 5 yields only modest quantitative gains (e.g., +0.5% Dice) while producing 'visibly smoother' fields. This leaves open the alternative explanation that the Dice gains come from increased model capacity or input diversity rather than from temporal modeling, so the paper's central differentiator is not supported by the presented evidence. I would ask for a quantitative temporal-consistency evaluation across all frames of the cycle, for example temporal smoothness statistics of the displacement magnitude or Jacobian determinant, or contour-propagation consistency against the ED and ES annotations, with the Nf=1 configuration and at least one single-pair baseline plotted alongside MCM in Fig. 4.
  2. [Table 1] The Dice gains over the strongest baselines are around one percentage point with overlapping standard deviations (e.g., ACDC mid: Ours 84.6(4.9) vs TransMorph 83.7(4.9); M&Ms apical: Ours 77.6(11.5) vs TransMorph 76.4(11.7)), and the paper reports no statistical significance tests. It is also not stated whether the mean and standard deviation in Table 1 are computed over subjects or over individual slices, which directly affects the effective sample size in a per-slice analysis. A paired non-parametric test over subjects (e.g., Wilcoxon signed-rank or bootstrap) is needed to support the claim in Sec. 3 that the proposed method 'outperforms all baseline methods'.
  3. [Abstract and contribution (1); Table 3] The abstract's claim that the method learns the continuous dynamics of the myocardium 'without increasing computational complexity' is at odds with Table 3, which reports GPU VRAM growing from 3.2 GB (Nf=1) to 7.8 GB (Nf=3) to 12.4 GB (Nf=5), roughly a fourfold increase, and which lists only inference-time VRAM for the baselines (1.5-3.6 GB), not training VRAM. The statement that is actually supported by Table 3 is that inference time is roughly constant as Nf grows (16.3 to 17.1 ms); the abstract and contribution (1) should be qualified accordingly, and the Discussion's acknowledgment of 'the cost of increased GPU memory usage' should be reflected in the paper's framing.
minor comments (7)
  1. [Table 3] The citation labels in Table 3 are inconsistent with Table 1 and the reference list: 'VM [23]' should be 'VM [1]' and 'TM [1]' should be 'TM [7]'.
  2. [References [2]] Reference [2] attributes 'A New Approach to Linear Filtering and Prediction Problems' to T. Basar; this is R.E. Kalman's 1960 paper, and the citation should be corrected.
  3. [Sec. 2.1 (BiSM)] The description of the bi-directional scanning does not specify the exact token ordering fed to the SSMs, namely whether the sequence is over the Nf frames for each spatial position, or a flattened Np×Nf sequence, and in which interleaving order; the paper should state this precisely.
  4. [Table 2 and Fig. 4(b)] The configurations of the 'Without BMBs' and 'without DFH' ablations are not described; the reader cannot tell what replaces the Mamba blocks and the fusion head in those variants, respectively.
  5. [Eq. (4)] In Eq. (4) the symbol FM is reused for the fused feature on the left-hand side after being used as the input on the right-hand side; a distinct symbol for the output of the averaging step would avoid the self-referential notation.
  6. [Fig. 4] The caption of Fig. 4 should state what quantity is plotted against the temporal axis in panels (b) and (c), and the panels should include a baseline trace (at least the Nf=1 variant) so the claimed improvement in smoothness is visible rather than implicit.
  7. [Sec. 3 (Comparison study)] In the comparison-study paragraph, 'comparing the wrapped segmentation' should read 'warped segmentation'; the paper should also state the number of test subjects and slices behind the reported means and standard deviations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method is an empirical registration network evaluated on held-out test slices, with no prediction that reduces to a fitted input.

full rationale

The paper makes no derivational claims that could reduce to their inputs by construction. The proposed MCM is an end-to-end trained network whose objective (Eq. 6) is a standard intensity-similarity plus gradient-smoothness loss; the reported metrics (Dice, |J|<0%, ||J|-1|) are measured on held-out test slices of ACDC and M&Ms, not used to define the network's output, and the hyperparameters K and lambda are selected on the validation sets as stated in the Implementation section. The central comparison against dDemons, VoxelMorph, TransMorph, and MambaMorph uses official code and publicly available data, so the performance claims are externally falsifiable rather than self-referential. The only apparent self-citations (e.g., prior cardiac motion tracking work by Meng et al.) appear as related work and are not invoked as a load-bearing justification for the proposed architecture or its results. The paper's temporal-consistency advantage is supported mainly by qualitative visual evidence (Fig. 4), and the Discussion concedes that increasing Nf from 1 to 5 yields modest quantitative gains; however, this is a limitation in the strength of evidence, not circular reasoning. No equation in the paper is equivalent to its input by definition, no fitted parameter is renamed as a prediction, and no uniqueness theorem or ansatz is smuggled in via self-citation. The result is therefore self-contained as an empirical study, and the circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method relies on standard image registration assumptions, two tuned hyperparameters, and the architectural premise that Mamba recurrence can model cardiac dynamics from a small frame window. No new physical or conceptual entities are introduced.

free parameters (2)
  • K (number of neighboring target frames) = 2
    Set in Section 3 implementation; ablation (Fig. 4c) shows Nf=5 yields better temporal smoothness but only +0.5% Dice vs Nf=1.
  • lambda (smoothness regularization weight) = 0.05
    Chosen for Eq. 6; Fig. 5 shows the trade-off between Dice and smoothness as lambda varies.
assumptions (3)
  • domain assumption Image intensity similarity (MSE) and smoothness regularization are valid priors for cardiac motion tracking.
    Eq. 6 defines the loss without biomechanical constraints; if the intensity constancy assumption fails (e.g., due to flow artifacts), the objective is misspecified.
  • domain assumption Warping the ED segmentation by the estimated motion field and comparing to the ES ground truth segmentation is a valid proxy for motion tracking accuracy.
    Used for all quantitative comparisons in Tables 1-2; errors near boundaries or through-plane motion are not captured.
  • domain assumption Mamba's state-space recurrence accurately captures temporal dynamics of cardiac motion from a window of 2K+1 frames.
    Underpins the Method section's motivation; no theoretical guarantee is given.

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Cite this review

Pith. "Pith review of MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI." pith.science (2026). https://pith.science/paper/T2FCNBDY

@misc{pith2026250717678,
  author       = {Pith},
  title        = {Pith review of: MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T2FCNBDY}},
  note         = {Machine review of arXiv:2507.17678}
}
read the original abstract

Myocardial motion tracking is important for assessing cardiac function and diagnosing cardiovascular diseases, for which cine cardiac magnetic resonance (CMR) has been established as the gold standard imaging modality. Many existing methods learn motion from single image pairs consisting of a reference frame and a randomly selected target frame from the cardiac cycle. However, these methods overlook the continuous nature of cardiac motion and often yield inconsistent and non-smooth motion estimations. In this work, we propose a novel Mamba-based cardiac motion tracking network (MCM) that explicitly incorporates target image sequence from the cardiac cycle to achieve smooth and temporally consistent motion tracking. By developing a bi-directional Mamba block equipped with a bi-directional scanning mechanism, our method facilitates the estimation of plausible deformation fields. With our proposed motion decoder that integrates motion information from frames adjacent to the target frame, our method further enhances temporal coherence. Moreover, by taking advantage of Mamba's structured state-space formulation, the proposed method learns the continuous dynamics of the myocardium from sequential images without increasing computational complexity. We evaluate the proposed method on two public datasets. The experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms both conventional and state-of-the-art learning-based cardiac motion tracking methods. The code is available at https://github.com/yjh-0104/MCM.

Figures

Figures reproduced from arXiv: 2507.17678 by the authors.

Figure 1
Figure 1. An overview of the proposed method: (a) The Hierarchical Mamba en [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The proposed bi-directional scanning Mamba (BiSM), including forward [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Motion tracking results using proposed method and baselines. We warp [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Temporal consistency across the cardiac cycle. The red line in (a) denotes [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Motion estimation with different values of λ [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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