K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

Congcong Liu; Dong Liang; HaiFeng Wang; Ting Zhao; Xingyang Wu; Yihang Zhou; Zhuoxu Cui

REVIEW 5 major objections 4 minor 15 references

K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

T0 review · 5 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A k-space diffusion model trained only on single-slice images reconstructs simultaneous multislice MRI by folding Slice GRAPPA into the sampling loop, beating conventional SMS methods at 3x and 4x in-plane acceleration.

desk verdict The paper's combination of a single-slice-trained heat diffusion prior with Slice GRAPPA at sampling is new and plausible, but the evaluation never states a train/test split, so the big numbers are not yet trustworthy. read the letter →

arxiv 2501.03293 v2 pith:674ENM6A submitted 2025-01-06 eess.IV

Ting Zhao , Zhuoxu Cui , Congcong Liu , Xingyang Wu , Yihang Zhou , Dong Liang , Haifeng Wang This is my paper · ORCID

classification eess.IV

keywords simultaneousmulti-sliceMRIk-spacediffusionmodelheatSliceGRAPPACAIPIRINHAreconstructionin-planeaccelerationgenerativeprior

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

Simultaneous multislice (SMS) MRI excites several slices at once to cut scan time, but its reconstruction has resisted deep learning because SMS training data vary with multiband factor and CAIPIRINHA shift. The paper argues that an SMS dataset is unnecessary: a k-space diffusion model trained on ordinary single-slice images can serve as the prior, with the SMS physics imposed at sampling time. At every reverse-diffusion step the method recombines per-slice estimates into SMS k-space, enforces data consistency against the measured SMS data, and separates the slices again with Slice GRAPPA. On simulated T2-weighted brain data with MB=3, the method reports PSNR 38.08 dB at 3x and 36.91 dB at 4x in-plane acceleration, outperforming Slice GRAPPA+SENSE and SMS-COOKIE, and it shows no in-plane aliasing up to 8x.

What carries the argument

The central object is the SMS-constrained reverse diffusion sampler built on a heat-diffusion k-space model, in which the forward process $d\hat z = \hat G_t \odot \hat z(0) dt + \sqrt{d\sigma^2(t)/dt}\,SS^* dw$ attenuates k-space with a 2-D Gaussian $\hat G_t$ and coil-sensitivity-weighted noise, and a network $h_\theta$ trained by score matching learns to invert that attenuation. During sampling, the method takes each slice estimate, composes the multi-slice k-space $\hat x^{\mathrm{sms}}_{0|t}$, performs data consistency with the measured $\hat x^{\mathrm{sms}}_{00}$, applies Slice GRAPPA kernels estimated from 32 ACS lines to separate the slices again, and uses SPIRiT kernels in initialization, repeating predictor-corrector steps through the reverse SDE.

What would settle it

Acquire genuine SMS k-space data on a scanner with MB=3 and a 2/3 FOV CAIPIRINHA shift, run the single-slice-trained diffusion model with Slice GRAPPA consistency, and compare against Slice GRAPPA+SENSE at 4x in-plane acceleration: if the PSNR advantage is much smaller than the reported 36.91 dB versus 24.43 dB gap, or if visible slice leakage appears in the separated slices, the simulated-data assumption is the likely cause.

Watch

Extended reading notes

Core claim

The central claim is that SMS-specific training data are not needed for high-quality SMS reconstruction by a generative diffusion model: a prior learned on single-slice k-space is sufficient, provided the SMS forward model is injected during inference. The method's sampling loop alternates heat-diffusion reverse steps with an SMS constraint: single-slice estimates are combined into simulated SMS k-space, corrected against the acquired k-space in a data-consistency step, and decomposed again by Slice GRAPPA. This combined loop removes the inter-slice aliasing that Slice GRAPPA alone leaves behind and resolves the in-plane aliasing that Slice GRAPPA+SENSE and SMS-COOKIE show at 4x, while staying stable through 8x in-plane acceleration with only gradual high-frequency blurring.

Load-bearing premise

The load-bearing premise is that k-space data simulated from single-slice T2-weighted brain images with MB=3, a 2/3 FOV CAIPIRINHA shift, and 32 ACS lines faithfully reproduces real simultaneous multislice acquisitions, including slice leakage, noise correlation, and coil geometry.

Editorial extensions

If this is right

A single diffusion model trained on single-slice k-space can reconstruct SMS data across different multiband and CAIPIRINHA configurations by swapping the Slice GRAPPA and data-consistency constraint, avoiding SMS data collection for training.
SMS reconstruction is no longer limited by the in-plane acceleration ceiling of traditional slice-separation methods; the paper's experiments show usable reconstructions up to 8x without in-plane aliasing.
At 3x and 4x in-plane acceleration, the method's reported PSNR and SSIM exceed both Slice GRAPPA+SENSE and SMS-COOKIE, e.g., PSNR 36.91 dB versus 24.43 dB and 27.19 dB at 4x.
Because the SMS physical model is imposed during sampling rather than learned, adapting the method to a new SMS acquisition mode should require changing the constraint, not retraining the network.

Reading between the lines

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

If the approach transfers to real scanner data, the practical bottleneck for SMS deep learning shifts from building large SMS training sets to obtaining accurate Slice GRAPPA kernels and coil sensitivity maps at scan time.
The same train-on-single-slices / constrain-at-sampling recipe could apply to other k-space undersampling tasks with known forward models but scarce paired training data, such as multi-echo or diffusion-weighted acquisitions.
The rising NMSE at higher acceleration factors identifies high-frequency information as the limiting component, so a frequency-weighted or high-frequency-preserving loss is a natural next experiment.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

What's actually new here: the authors take a k-space heat diffusion model, train it on single-slice fastMRI data, and then inject the Slice GRAPPA physics as a constraint during reverse diffusion sampling. That lets them avoid SMS training data entirely, which is a real advantage given how variable SMS acquisitions are. The results over Slice GRAPPA+SENSE and SMS-COOKIE are large — 12.5 dB at 4x — and the qualitative claim that aliasing disappears is plausible if the prior is doing what they say.

Credit where due: the combination is not in the prior literature, it is a sensible extension of their own heat diffusion work, and the sampling strategy handles different MB and CAIPIRINHA shifts at inference without retraining. They also honestly note that high-frequency detail degrades at 8x.

But there is a hole in the evaluation that a referee must probe first. The paper says the model was trained on the fastMRI T2 brain dataset and that test SMS data were simulated from the same fastMRI T2 brain dataset, cropped to 320x320. No train/test split, volume IDs, or patient-level separation is reported. If the same anatomy appeared during training, the diffusion model has an unfair advantage over the linear baselines, and the reported gains would be memorization artifacts. This is not an accusation of misconduct; it is a missing verification step. The authors need to state the split, ideally at the volume level, or the headline comparison is biased.

Beyond that, the evaluation is thin in ways that matter. It is simulated SMS data only, with no real acquisitions, so slice leakage and coil geometry effects are untested. No error bars or repeated runs, so the dB differences between baselines could be noise. The comparison set is two conventional methods; the discussion of H-DSLR and MoDL is a hand-wave and should be backed by numbers or dropped. Some hyperparameters (diffusion steps, noise schedule) are missing, though that is typical for a short paper.

The central idea holds up — I do not see circularity or a fitted-parameter problem. The method is not a performance; it is a genuine attempt to combine physics and a diffusion prior. The main issue is evaluation hygiene, not the underlying approach.

Who this is for: people working on SMS reconstruction, especially those who want to avoid collecting SMS training data. It deserves a serious referee, but the referee should demand the train/test split and more rigorous comparison before accepting. If the split is clean, this is a nice contribution; if not, the numbers cannot be trusted.

Referee Report

5 major / 4 minor

Summary. The paper proposes an SMS (simultaneous multislice) MRI reconstruction method that combines a k-space heat-diffusion model (trained only on single-slice FastMRI T2 images) with Slice GRAPPA applied during the reverse-diffusion sampling process. The authors simulate SMS k-space data from cropped 320x320 FastMRI T2 images with MB=3, a 2/3 FOV CAIPIRINHA shift, and 32 ACS lines, and compare against Slice GRAPPA+SENSE and SMS-COOKIE at in-plane acceleration factors 3x and 4x. They report consistently higher PSNR/SSIM and lower NMSE (e.g., PSNR 36.91 dB vs. 24.43 dB at 4x), and further show that their method maintains relatively high quality up to 8x acceleration. The central claim is that the method does not require SMS data for training yet outperforms traditional SMS reconstruction methods and supports higher in-plane acceleration factors.

Significance. If the reported gains hold on held-out data and on real SMS acquisitions, the paper offers a practically important result: a deep-learning SMS reconstruction method that avoids the need to curate diverse SMS training datasets (with variable MB factors and CAIPIRINHA shifts) by training only on single-slice k-space data and injecting the SMS physics through Slice GRAPPA during sampling. The method builds on the authors' prior heat-diffusion framework (Ref. [7]) and their ISMRM abstract (Ref. [6]), and the paper provides quantitative comparisons at multiple acceleration factors. However, the evaluation is currently too weak to establish the central claim: the training and test data appear to come from the same FastMRI T2 dataset without any reported split, the test data are simulated rather than real SMS acquisitions, no error bars or statistical significance are given, and only two traditional baselines are used. The paper also does not release code, trained models, or test-set identifiers, which limits reproducibility. With a proper held-out evaluation and additional baselines, the contribution could be significant for the SMS-MRI community.

major comments (5)

[Section 3.1, Table 1] The paper never reports a train/test split. Section 3.1 states that the FastMRI T2 brain dataset was used to train the Heat Diffusion model and that SMS test data were simulated from the same FastMRI T2 brain dataset cropped to 320x320, but no volume identifiers, patient-level separation, or number of test slices is given. Since the diffusion model is trained to reconstruct high-frequency k-space content, any test volume that appeared in training (or came from the same patient) could inflate the reported gains, for example the 12.5 dB improvement over Slice GRAPPA+SENSE at 4x. The authors must specify and implement a held-out patient-level split, or retrain on a disjoint set, before the headline quantitative claims can be considered reliable.
[Section 3.1, Section 4.1] The evaluation uses only simulated SMS data with a single parameter set (MB=3, 2/3 FOV CAIPIRINHA shift, 32 ACS lines, uniform undersampling). No real SMS acquisitions, variable MB/CAIPI patterns, or realistic slice-leakage and noise-correlation effects are considered. The claim that the method outperforms Slice GRAPPA+SENSE and SMS-COOKIE therefore rests on an untested assumption that simulation faithfully captures the SMS forward model. The authors should validate on at least one real SMS dataset or realistic phantom scan, or clearly scope the claim to simulated data.
[Section 2.2, Section 2.3] The method description omits several parameters needed for reproducibility: the number of reverse-diffusion steps, the noise schedule σ(t), the Gaussian kernel parameters G_t, the predictor-corrector settings, the number of Monte Carlo samples, and the details of the SPIRiT initialization step. Without these, experiments cannot be reproduced, and it is unclear whether the comparison to the baselines is performed under matched computational budgets. The authors should provide a complete algorithmic specification or release code.
[Table 1, Section 4.1] The quantitative comparison reports single-point PSNR/NMSE/SSIM values without error bars, confidence intervals, or the number of test volumes/slices. The extremely large differences may be statistically meaningful, but the magnitude of noise is unknown. The authors should report mean and standard deviation over multiple test scans, and ideally per-volume results, to support the claimed superiority.
[Section 4.3] The paper compares only two traditional non-deep-learning baselines (Slice GRAPPA+SENSE and SMS-COOKIE). Because the proposed method is a generative deep-learning approach, the absence of any SMS-aware deep-learning baseline (e.g., SMS-RAKI, VCC-RAKI, or a diffusion-based SMS method) makes the claim of significant advantage over existing state-of-the-art overly broad. Adding at least one such baseline is important for positioning the contribution.

minor comments (4)

[Throughout] There are typographical errors: "SNESE" in Section 4.1, "k-sapce" in the Introduction, and inconsistent spacing after periods (e.g., in the abstract). These should be corrected.
[Section 2.2, Eq. (5)] The score-matching loss in Eq. (5) uses S^*G(t)⊙(h_θ(ẑ(t),t) - ẑ(0)), but the relationship between the network output and the score ∇ẑ log p_t(ẑ) is not stated explicitly. A brief derivation or reference would help the reader connect Eq. (5) to the reverse SDE in Eq. (4).
[Section 4.2, Table 2] Table 2 reports results up to 8x, but the text in Section 4.2 says the method "does not exhibit undersampling artifacts even at 8x," while the NMSE worsens to 0.0305 and PSNR drops to 30.5 dB. The wording overstates the visual quality; consider a more nuanced claim consistent with the admitted high-frequency loss.
[Figures] Figure 2 and Figure 3 are referenced in the text, but the caption and figure content are not fully described (e.g., which slices, which coil configuration, and the display window). Including slice numbers and error-map color scales would improve interpretability.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: predictions are not equal to inputs by construction; a missing train/test split is a leakage risk, not a circular step.

full rationale

The derivation chain is self-contained with respect to the SMS claim. The heat-diffusion prior is trained on single-slice k-space data (Section 3.1), the Slice-GRAPPA kernel and coil sensitivities are calibrated from ACS lines at test time, and the reverse-sampling data-consistency step (Eq. 6 plus Section 2.3) does not inject the comparison baselines or the reported PSNR values as inputs. The self-citations ([6], [7]) supply the underlying diffusion formulation, but the SMS-specific result is not used to define that formulation, so these are dependencies rather than circular closures. The one substantive validity concern is experimental reporting: Section 3.1 states that the fastMRI T2 brain dataset was used both for training the diffusion model and for simulating SMS test data, without specifying a patient-level train/test split. If volumes overlap, the Table 1 gains could be memorization artifacts. This is a correctness/leakage risk, not a circularity of the derivation, and it does not change the conclusion that the output is not equal to the input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a learned diffusion prior (heat diffusion) and standard MRI reconstruction tools (Slice GRAPPA, SPIRiT). No new physical entities are postulated. The main free parameters are the trained network weights and experimental choices (ACS lines, CAIPIRINHA shift).

free parameters (4)

Heat diffusion network weights θ = not stated; trained on fastMRI single-slice data
The reconstruction quality depends on the learned score network hθ; its parameters are fitted to single-slice MRI data during training (Eq. 5).
Number of ACS lines = 32
Chosen for Slice GRAPPA and SPIRiT kernel estimation; affects reconstruction quality (Section 3.1).
CAIPIRINHA shift = 2/3 FOV
Used to minimize inter-slice leakage in the simulated SMS acquisition (Section 3.1).
Noise schedule and Gaussian kernel parameters (σ(t), G_t) = unspecified
Taken from Heat Diffusion prior work; not detailed in this paper (Section 2.2).

assumptions (4)

domain assumption Heat diffusion forward/reverse SDE accurately models k-space attenuation and enables score-based reconstruction.
The method relies on the Heat Diffusion model from Ref [7] without re-deriving or validating it (Section 2.2).
domain assumption Single-slice k-space statistics learned from fastMRI generalize to SMS data after Slice GRAPPA separation.
Training on single-slice data and applying to SMS assumes the prior remains useful after the constraint; not explicitly validated.
domain assumption Simulated SMS data faithfully represents real simultaneous multislice acquisitions.
The evaluation uses simulated SMS data from 320x320 fastMRI T2 images; real acquisition effects such as slice leakage, noise correlation, and coil sensitivities may differ (Section 3.1).
domain assumption Slice GRAPPA and SPIRiT kernels computed from ACS lines remain valid during iterative diffusion sampling.
The sampling process repeatedly applies these kernels to intermediate data; their accuracy is assumed to hold (Section 2.3).

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Cite this review

Pith. "Pith review of K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging." pith.science (2026). https://pith.science/paper/674ENM6A

@misc{pith2026250103293,
  author       = {Pith},
  title        = {Pith review of: K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/674ENM6A}},
  note         = {Machine review of arXiv:2501.03293}
}

read the original abstract

Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This study proposed a novel k-space diffusion model of SMS reconstruction that does not utilize SMS data for training. Instead, it incorporates Slice GRAPPA during the sampling process to reconstruct SMS data from different acquisition modes.Our results demonstrated that this method outperforms traditional SMS reconstruction methods and can achieve higher acceleration factors without in-plane aliasing.

Figures

Figures reproduced from arXiv: 2501.03293 by the authors.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

[7]

Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,

Kawin Setsompop et al., “Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,” Magnetic reso- nance in medicine, vol. 67, no. 5, pp. 1210–1224, 2012

work page 2012
[6]

Simultaneous multislice (sms) imaging techniques,

Markus Barth et al., “Simultaneous multislice (sms) imaging techniques,” Magnetic resonance in medicine, vol. 75, no. 1, pp. 63–81, 2016

work page 2016
[1]

Whereas conventional methods have an unavoidable signal-to-noise ratio(SNR) loss, the SNR in SMS scans increases by the square root of the number of slices

INTRODUCTION Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique, which utilizes multi-band ra- diofrequency pulses to simultaneously excite multiple slices, thereby significantly reducing the scanning time of MRI, which is especially important for lengthy acquisitions like diffusion tensor imaging(DTI), volumetric T2-weighted sc...

work page
[2]

K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

METHOD 2.1. Slice GRAPPA In Slice-GRAPPA, a separate calibration scan is acquired for each slice and used to estimate a set of slice-specific kernels. The kernels are applied to the SMS data to synthesize an en- tirely new k-space for each slice. The process of SMS data acquisition can be regarded as: ˆxsms 00 = Dˆxi + n (1) where D is the encoding matrix...

work page Pith review arXiv 2025
[3]

Dataset FastMRI Dataset was used to train the Heat Diffusion Model

EXPERIMENT 3.1. Dataset FastMRI Dataset was used to train the Heat Diffusion Model. For sampling test, we simulated SMS data using fastMRI T2 brain dataset and cropped the image to 320 x 320. We set MB=3 with different in-plane acceleration factors. To en- sure minimal inter-slice leakage, a 2/3 field-of-view (FOV) CAIPIRINHA shift was applied to the thre...

work page
[4]

Compared to traditional SMS methods The methodology proposed in this study was compared with traditional Slice-GRAPPA+SNESE and SMS-COOKIE[8]

RESULTS AND DISCUSSION 4.1. Compared to traditional SMS methods The methodology proposed in this study was compared with traditional Slice-GRAPPA+SNESE and SMS-COOKIE[8]. The uniform undersampling mask was used, with in-plane un- dersampling factors of 3x and 4x. Results of 4x are shown in Fig.2. Under the condition of 4x, the Slice GRAPPA+SENSE and SMS-C...

work page
[5]

Our proposed method does not require SMS data for training, thereby avoiding is- sues associated with the variability and difficulty in obtaining SMS dataset

CONCLUSION This study proposed a SMS reconstruction method based on Heat Diffusion and slice GRAPPA. Our proposed method does not require SMS data for training, thereby avoiding is- sues associated with the variability and difficulty in obtaining SMS dataset. Moreover, due to the superior reconstruction performance of diffusion model , our proposed recons...

work page
[8]

Accelerated volumetric mri with a sense/grappa combination,

Martin Blaimer et al., “Accelerated volumetric mri with a sense/grappa combination,” Journal of Magnetic Res- onance Imaging: An Official Journal of the Interna- tional Society for Magnetic Resonance in Medicine, vol. 24, no. 2, pp. 444–450, 2006

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Ting Zhao et al., “Mr optimized reconstruction of si- multaneous multi-slice imaging using diffusion model,” 2024 Proc. of the International Society for Magnetic Resonance in Medicine (ISMRM), 2024

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Physics-informed deepmri: Bridg- ing the gap from heat diffusion to k-space interpolation,

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [7]

Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,

Kawin Setsompop et al., “Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,” Magnetic reso- nance in medicine, vol. 67, no. 5, pp. 1210–1224, 2012

work page 2012

[2] [6]

Simultaneous multislice (sms) imaging techniques,

Markus Barth et al., “Simultaneous multislice (sms) imaging techniques,” Magnetic resonance in medicine, vol. 75, no. 1, pp. 63–81, 2016

work page 2016

[3] [1]

Whereas conventional methods have an unavoidable signal-to-noise ratio(SNR) loss, the SNR in SMS scans increases by the square root of the number of slices

INTRODUCTION Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique, which utilizes multi-band ra- diofrequency pulses to simultaneously excite multiple slices, thereby significantly reducing the scanning time of MRI, which is especially important for lengthy acquisitions like diffusion tensor imaging(DTI), volumetric T2-weighted sc...

work page

[4] [2]

K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging

METHOD 2.1. Slice GRAPPA In Slice-GRAPPA, a separate calibration scan is acquired for each slice and used to estimate a set of slice-specific kernels. The kernels are applied to the SMS data to synthesize an en- tirely new k-space for each slice. The process of SMS data acquisition can be regarded as: ˆxsms 00 = Dˆxi + n (1) where D is the encoding matrix...

work page Pith review arXiv 2025

[5] [3]

Dataset FastMRI Dataset was used to train the Heat Diffusion Model

EXPERIMENT 3.1. Dataset FastMRI Dataset was used to train the Heat Diffusion Model. For sampling test, we simulated SMS data using fastMRI T2 brain dataset and cropped the image to 320 x 320. We set MB=3 with different in-plane acceleration factors. To en- sure minimal inter-slice leakage, a 2/3 field-of-view (FOV) CAIPIRINHA shift was applied to the thre...

work page

[6] [4]

Compared to traditional SMS methods The methodology proposed in this study was compared with traditional Slice-GRAPPA+SNESE and SMS-COOKIE[8]

RESULTS AND DISCUSSION 4.1. Compared to traditional SMS methods The methodology proposed in this study was compared with traditional Slice-GRAPPA+SNESE and SMS-COOKIE[8]. The uniform undersampling mask was used, with in-plane un- dersampling factors of 3x and 4x. Results of 4x are shown in Fig.2. Under the condition of 4x, the Slice GRAPPA+SENSE and SMS-C...

work page

[7] [5]

Our proposed method does not require SMS data for training, thereby avoiding is- sues associated with the variability and difficulty in obtaining SMS dataset

CONCLUSION This study proposed a SMS reconstruction method based on Heat Diffusion and slice GRAPPA. Our proposed method does not require SMS data for training, thereby avoiding is- sues associated with the variability and difficulty in obtaining SMS dataset. Moreover, due to the superior reconstruction performance of diffusion model , our proposed recons...

work page

[8] [8]

Accelerated volumetric mri with a sense/grappa combination,

Martin Blaimer et al., “Accelerated volumetric mri with a sense/grappa combination,” Journal of Magnetic Res- onance Imaging: An Official Journal of the Interna- tional Society for Magnetic Resonance in Medicine, vol. 24, no. 2, pp. 444–450, 2006

work page 2006