Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods

Kim-Doang Nguyen; Ovidiu C. Andronesi; Tan-Hanh Pham; Xianqi Li

REVIEW 4 major objections 6 minor 31 references

Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper proposes that a mask-free U-Net can estimate missing MRSI metabolic-map voxels by implicitly detecting zero-valued regions from spatial context, outperforming linear and cubic interpolation on simulated and real patient data.

desk verdict First mask-free deep learning baseline for MRSI missing-voxel estimation, with plausible gains over interpolation, but the central assumption that real missing voxels are exact zeros is untested, so treat the generalization claims with caution. read the letter →

arxiv 2505.06811 v1 pith:FWBJAYHA submitted 2025-05-11 eess.IV cs.CV

Tan-Hanh Pham , Ovidiu C. Andronesi , Xianqi Li , Kim-Doang Nguyen This is my paper · ORCID

classification eess.IVcs.CV

keywords MRSImissingdataestimationmask-freedeeplearningU-Net3DprogressivetrainingmetabolicmaprestorationMagneticResonanceSpectroscopicImaging

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

Magnetic resonance spectroscopic imaging (MRSI) maps brain metabolites, but motion, field inhomogeneity, and failed spectral fitting often leave voxels missing or unusable. This paper tries to show that a U-Net trained without any explicit mask can learn to detect those missing voxels implicitly from spatial context and estimate their values, replacing hand-made masks and outperforming linear and cubic interpolation. On simulated 2D data with 20% missing voxels the model reaches an MSE of 0.002 and an SSIM of 0.97; on simulated 3D data with 15% missing it reaches an MSE of 0.001 and an SSIM of 0.98. The same model also restores real patient NAA and tCho metabolic maps with simulated missing regions, without retraining. If this holds, MRSI restoration becomes simpler in practice because no mask-generation step is needed.

What carries the argument

The load-bearing mechanism is the U-Net architecture, an encoder-decoder convolutional network whose skip connections pass high-resolution spatial detail from the encoder to the decoder. In this paper it comes in 2D and 3D forms, with four encoder blocks, a 512-channel bottleneck, and transposed-convolution decoding. The network is trained with a composite loss combining mean squared error and structural similarity, and with a progressive curriculum that starts at 5% missing voxels and gradually increases to 10% and 15%. That curriculum is what pushes the model to learn generalized restoration rather than memorizing one degradation level. The mask-free behavior itself is carried by the input representation: corrupted voxels are encoded as zeros, and the network learns to recognize these zeroed clusters as regions to reconstruct from surrounding context.

What would settle it

Take real MRSI metabolic maps with naturally corrupted voxels, corrupt additional regions to create a reference, and compare prediction error on voxels that carry non-zero noise rather than exact zeros; if the model's MSE and SSIM degrade sharply relative to zero-filled inputs, the zero-representation premise is the weak link.

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Extended reading notes

Core claim

The paper's central claim is that missing or corrupted voxels in MRSI metabolic maps can be estimated by a mask-free deep network that learns to treat zero-valued regions as data to be filled. Using 2D and 3D U-Net architectures trained on synthetic volumes generated from segmented MRI tissue maps, the model reconstructs the full metabolic map from a corrupted input in a single forward pass. The authors report that this approach outperforms cubic and linear interpolation on both 2D and 3D simulated datasets, with the 2D model achieving MSE 0.002 and SSIM 0.97 at 20% missing voxels and the 3D model achieving MSE 0.001 and SSIM 0.98 at 15% missing voxels. They also show qualitative restoration of real 2D NAA and tCho metabolic maps with simulated missing regions, including areas around ventricles and tumor-involved tissue, suggesting the model generalizes beyond its synthetic training distribution.

Load-bearing premise

The model assumes that every missing or corrupted voxel is represented by a zero in the input volume, exactly as in the simulated training data; real bad voxels that still contain noise, partial signal, or artifacts may not be recognized as missing.

Editorial extensions

If this is right

Clinical MRSI pipelines can skip explicit mask generation: the network finds and fills missing voxels from the corrupted volume alone.
Restoration quality on quantitative metrics is substantially higher than interpolation, with MSE dropping from 0.004-0.008 with interpolation to 0.001-0.002 with the U-Net models.
A model trained only on synthetic tissue-weighted volumes transfers to real patient metabolic maps, including NAA and tCho, without retraining.
The progressive training schedule is a simple, reusable recipe for making restoration networks handle varying amounts of data loss.
Higher-fidelity 3D metabolic maps could make MRSI more usable for monitoring tumors, epilepsy, and neurodegeneration in research and clinical settings.

Reading between the lines

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

If real-world bad voxels carry non-zero noise, artifacts, or partial spectral signal rather than exact zeros, the implicit detection learned from zero-filled training data may misfire; a testable fix is to augment training with artifact-like corruptions.
The same mask-free estimation idea could extend to other metabolite maps, such as creatine and lactate, or to joint restoration of multiple metabolic volumes, since the network only needs the corrupted input as a cue.
The zero-fill assumption also suggests a potential failure mode for 3D real data, which the paper did not evaluate; a direct 3D real-data comparison with ground truth would be the 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

Colleague,

Quick read of arXiv:2505.06811. The paper applies a standard 2D/3D U-Net to the problem of filling missing voxels in MRSI metabolic maps, without requiring an explicit mask. That is genuinely new for MRSI—prior deep learning work in the area has focused on spectral quality control and super-resolution, not voxel-wise recovery. The reported gains over linear/cubic interpolation are plausible: on synthetic data with 20% (2D) and 15% (3D) missing voxels, they get MSE 0.002/0.001 and SSIM 0.97/0.98. The progressive training strategy is a minor twist on curriculum learning, and the architecture itself is off-the-shelf. That is fine for a first application, but it means the contribution is "first deep learning baseline for this task," not an algorithmic advance.

The soft spot is real and central. Both training and every quantitative evaluation (including the "real data" tests in Figs. 5 and 6) corrupt voxels by setting them to zero (Algorithm 1, line 17). The model learns to treat exact zeros as missing. Real MRSI voxels lost to motion, B0 inhomogeneity, or failed fitting are not guaranteed to be zero; they can contain noise, artifacts, or partial signal. The only experiment with naturally missing voxels (Fig. 7) has no ground truth and is qualitative. So the central claim—that the method generalizes to real missing data—rests on an untested equivalence between real missingness and zero-fill. The authors concede they did not evaluate on 3D real data and depend on simulated training data, but the zero-fill issue is more fundamental than those omissions.

Also worth noting: the synthetic data is a weighted sum of tissue segmentations (GM, WM, tumor), not actual MRSI acquisitions. That is a weak proxy. And there are no deep learning baselines, no error bars, and no released code or data. These are fixable, but they limit how much weight I'd give the numbers.

Who should read this: anyone working on MRSI preprocessing or missing-voxel restoration. It is a reasonable first demonstration, and the idea of mask-free inpainting is worth testing on real corruption. But I'd want to see the code and an evaluation on naturally corrupted voxels before relying on it.

Recommendation: send it to peer review, but with the expectation of major revisions. The referee should push for real-world validation with non-zero corruption, comparison against a masked inpainting baseline, and public code/data. If those come back, it could be a useful contribution to a niche field.

Referee Report

4 major / 6 minor

Summary. The paper proposes a mask-free deep learning framework based on 2D and 3D U-Net architectures for estimating missing voxels in MRSI metabolic maps. Synthetic MRSI volumes are generated as a weighted sum of tissue segmentations (Eq. 1), missing voxels are set to zero via Algorithm 1, and a progressive training strategy is employed. The method is evaluated on synthetic 2D and 3D data with 20% and 15% missing voxels, respectively, and on real patient datasets with simulated missing regions. The reported results show lower MSE and higher SSIM than linear and cubic interpolation, but the evaluation is largely in-distribution and relies on the zero-fill assumption.

Significance. If the results hold, the method could provide a convenient mask-free approach for restoring MRSI metabolic maps, with the progressive training strategy being a practical contribution. The paper is clearly written and the 2D/3D architectural extension is straightforward. However, the evidence is not yet sufficient to support generalization to real MRSI corruption: the synthetic data are simplistic, the missingness is always zero-filled, and no comparison to state-of-the-art inpainting is provided. The credit for attempting real-data evaluation and for the progressive training design should be acknowledged.

major comments (4)

[II-B, IV-C] The central assumption that missing voxels are exactly zero is load-bearing: Algorithm 1 (line 17) replaces all missing voxels with zero, and the same zero-fill procedure is used to create the inputs in the real-data experiments of Section IV-C (Figs. 5 and 6). The model therefore learns to inpaint exact zeros, and the reported quantitative gains (Tables I and II) do not demonstrate performance on realistically corrupted voxels, which may contain noise, partial signals, or spectral artifacts. The only experiment with naturally missing data (Fig. 7) is qualitative and lacks ground truth. Please either add experiments with non-zero corruption models or explicitly restrict the claims to zero-filled missing data.
[II-A, IV-A] The synthetic MRSI volumes are generated from tissue segmentation maps (Eq. 1) and the same pipeline is used to create both training and test sets; the real-data tests in Figs. 5 and 6 also apply the same simulation (Section II-B). Consequently, the quantitative evaluation is in-distribution with respect to both image statistics and corruption mechanism, which likely overstates the model's generalization to real MRSI data. There are no standard deviations or significance tests across test samples, so the reported margin over interpolation (e.g., MSE 0.002 vs. 0.004 in Table I) may not be statistically robust.
[IV-A, IV-B] The baselines are limited to linear and cubic interpolation. Since the paper's stated contribution is a mask-free deep learning framework, it should be compared with mask-based inpainting methods (for example, the references [13] and [14]) and with a mask-based deep learning baseline. Without such comparisons, the relative benefit of the mask-free design is not established.
[IV-B, Table II] There is a factual inconsistency: the text reports 'an SSIM of 0.05' for linear interpolation, while Table II lists SSIM values of 0.95 for both training and test. Please correct the text or the table. In addition, the meaning of 'Training Data' columns for interpolation methods should be clarified, as interpolators do not have a training phase.

minor comments (6)

[Algorithm 1, line 11] The line 'Sample cluster size randomly s∈R^{D×H×W}, s∼Uniform(smin,smax)' treats s as a high-dimensional tensor, although the subsequent loop uses s as a scalar; please fix the notation.
[III] Please specify how interpolation baselines are applied (e.g., scipy.ndimage.map_coordinates or similar) and whether they are evaluated on the same zero-filled inputs.
[Figure 7] The regions of naturally missing data are not indicated; adding an overlay or arrows would help the reader assess the reconstruction.
[IV] The paper reports point estimates of MSE/SSIM without confidence intervals; adding standard deviations over test volumes would improve interpretability.
[II-C] The phrase 'the bottleneck is in the center of the network' is vague; please specify the bottleneck's role and dimensions.
[IV-A] It is unclear why the 2D model is evaluated at 20% missing while progressive training reaches only 15%; please explain the intended extrapolation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the synthetic training/evaluation pipeline is in-distribution, but the predictions are learned outputs against independent ground truth rather than algebraic reductions of the inputs.

full rationale

The paper's derivation chain is not circular. Synthetic MRSI ground-truth volumes are generated independently from multi-modal MRI segmentations via Eq. (1), and missing voxels are created by Algorithm 1 by setting selected brain voxels to zero. The U-Net is then trained to map zero-filled inputs back to the original volumes and evaluated on held-out synthetic and real metabolic maps corrupted with the same zero-fill procedure. This is a standard supervised inpainting setup: the model output is a learned function of the input, not a quantity that is definitionally equal to the input or to a fitted parameter. The real-data experiments in Figs. 5 and 6 do use the same synthetic corruption algorithm rather than naturally corrupted voxels, which limits the strength of the generalization claim, but the paper explicitly states that naturally missing real data lacks ground truth and is shown only qualitatively (Fig. 7). That is a validation gap, not a circular reduction. Self-citations to prior U-Net and MRSI super-resolution work are contextual and not load-bearing; no uniqueness theorem is invoked to force the architecture or the results. No equation or metric in the paper is equivalent by construction to its own inputs, so the appropriate finding is no significant circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several domain assumptions: synthetic MRSI realism, zero-value representation of missing data, and the accuracy of tissue segmentations. These are not validated against real MRSI artifacts. The hand-chosen weights in the synthetic generator and the loss weight are free parameters that affect the training distribution.

free parameters (5)

GM weight = 0.1
Hand-chosen coefficient in synthetic MRSI generation (Eq. 1); not justified by data.
WM weight = 0.12
Hand-chosen coefficient in synthetic MRSI generation (Eq. 1).
Tumor weight range tau = sampled from [0.2,...,0.8]
Arbitrarily chosen range to simulate pathological metabolic intensities.
Missing cluster size range = 1 to 3 voxels
Chosen simulation parameter for missing voxel clusters (Algorithm 1).
Loss weight alpha = 0.5
Empirically set in composite MSE+SSIM loss (Eq. 2).

assumptions (5)

domain assumption Synthetic MRSI maps created by weighted sum of GM, WM, CSF, and tumor segmentations are realistic proxies for real MRSI metabolic maps.
Invoked in Section II A; if false, training on these maps may not transfer to real data.
domain assumption Missing voxels are represented by zero intensity in the input volume.
Algorithm 1 sets voxels to 0; the model learns to restore zeros, which may not match real artifact patterns.
domain assumption ITK-SNAP and FSL segmentations are accurate enough to construct meaningful synthetic maps.
Used in Section II A without validation of segmentation quality.
domain assumption U-Net with MSE and SSIM loss can learn mask-free inpainting of MRSI maps.
The central learning hypothesis; plausible but not proven by theory.
domain assumption The train/test split of 75 volumes avoids patient-level leakage.
The paper does not describe whether slices from the same volume appear in both training and test sets.

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Cite this review

Pith. "Pith review of Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods." pith.science (2026). https://pith.science/paper/FWBJAYHA

@misc{pith2026250506811,
  author       = {Pith},
  title        = {Pith review of: Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FWBJAYHA}},
  note         = {Machine review of arXiv:2505.06811}
}

read the original abstract

Magnetic Resonance Spectroscopic Imaging (MRSI) is a powerful tool for non-invasive mapping of brain metabolites, providing critical insights into neurological conditions. However, its utility is often limited by missing or corrupted data due to motion artifacts, magnetic field inhomogeneities, or failed spectral fitting-especially in high resolution 3D acquisitions. To address this, we propose the first deep learning-based, mask-free framework for estimating missing data in MRSI metabolic maps. Unlike conventional restoration methods that rely on explicit masks to identify missing regions, our approach implicitly detects and estimates these areas using contextual spatial features through 2D and 3D U-Net architectures. We also introduce a progressive training strategy to enhance robustness under varying levels of data degradation. Our method is evaluated on both simulated and real patient datasets and consistently outperforms traditional interpolation techniques such as cubic and linear interpolation. The 2D model achieves an MSE of 0.002 and an SSIM of 0.97 with 20% missing voxels, while the 3D model reaches an MSE of 0.001 and an SSIM of 0.98 with 15% missing voxels. Qualitative results show improved fidelity in estimating missing data, particularly in metabolically heterogeneous regions and ventricular regions. Importantly, our model generalizes well to real-world datasets without requiring retraining or mask input. These findings demonstrate the effectiveness and broad applicability of mask-free deep learning for MRSI restoration, with strong potential for clinical and research integration.

Figures

Figures reproduced from arXiv: 2505.06811 by the authors.

**Figure 2.** 3D deep neural network architecture for MRSI metabolic maps missing data estimation. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Comparison of 2D simulated MRSI metabolic map restoration results on 20% missing data using the proposed deep [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Missing data estimation by the proposed model on [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 6.** Figure 6: Restoration results for 15% missing data estimation [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Inference outcomes for estimating missing data in 2D [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 26 canonical work pages

[13]

Robust smoothing of gridded data in one and higher dimen- sions with missing values,

D. Garcia, “Robust smoothing of gridded data in one and higher dimen- sions with missing values,”Computational statistics & data analysis, vol. 54, no. 4, pp. 1167–1178, 2010

work page 2010
[14]

Inpainting as a technique for estimation of missing voxels in brain imaging,

A. Torrado-Carvajal, D. S. Albrecht, J. Lee, O. C. Andronesi, E.-M. Ratai, V . Napadow, and M. L. Loggia, “Inpainting as a technique for estimation of missing voxels in brain imaging,”Annals of biomedical engineering, vol. 49, pp. 345–353, 2021

work page 2021
[1]

Accelerated mr spectroscopic imaging—a review of current and emerging techniques,

W. Bogner, R. Otazo, and A. Henning, “Accelerated mr spectroscopic imaging—a review of current and emerging techniques,”NMR in Biomedicine, vol. 34, no. 5, p. e4314, 2021

work page 2021
[2]

Nmr chemical shift imaging in three dimensions

T. R. Brown, B. Kincaid, and K. Ugurbil, “Nmr chemical shift imaging in three dimensions.”Proceedings of the National Academy of Sciences, vol. 79, no. 11, pp. 3523–3526, 1982

work page 1982
[3]

Spatially resolved high resolution spectroscopy by “four-dimensional

A. Maudsley, S. Hilal, W. Perman, and H. Simon, “Spatially resolved high resolution spectroscopy by “four-dimensional” nmr,”Journal of Magnetic Resonance (1969), vol. 51, no. 1, pp. 147–152, 1983

work page 1969
[4]

Multivendor implementation and comparison of volumetric whole-brain echo-planar mr spectroscopic imaging,

M. Sabati, S. Sheriff, M. Gu, J. Wei, H. Zhu, P. B. Barker, D. M. Spiel- man, J. R. Alger, and A. A. Maudsley, “Multivendor implementation and comparison of volumetric whole-brain echo-planar mr spectroscopic imaging,”Magnetic resonance in medicine, vol. 74, no. 5, pp. 1209– 1220, 2015

work page 2015
[5]

An advanced mri and mrsi data fusion scheme for enhancing unsupervised brain tumor differentiation,

Y . Li, X. Liu, F. Wei, D. M. Sima, S. Van Cauter, U. Himmelreich, Y . Pi, G. Hu, Y . Yao, and S. Van Huffel, “An advanced mri and mrsi data fusion scheme for enhancing unsupervised brain tumor differentiation,” Computers in biology and medicine, vol. 81, pp. 121–129, 2017. 8

work page 2017
[6]

Super- resolution whole-brain 3d mr spectroscopic imaging for mapping d- 2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1–mutated human gliomas,

X. Li, B. Strasser, K. Jafari-Khouzani, B. Thapa, J. Small, D. P. Cahill, J. Dietrich, T. T. Batchelor, and O. C. Andronesi, “Super- resolution whole-brain 3d mr spectroscopic imaging for mapping d- 2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1–mutated human gliomas,”Radiology, vol. 294, no. 3, pp. 589–597, 2020

work page 2020

Show all 31 references

[7]

Motion correction methods for mrs: experts’ consensus recom- mendations,

O. C. Andronesi, P. K. Bhattacharyya, W. Bogner, I.-Y . Choi, A. T. Hess, P. Lee, E. M. Meintjes, M. D. Tisdall, M. Zaitsev, and A. van der Kouwe, “Motion correction methods for mrs: experts’ consensus recom- mendations,”NMR in Biomedicine, vol. 34, no. 5, p. e4364, 2021

2021
[8]

B0 shimming for in vivo magnetic resonance spectroscopy: experts’ consensus recommendations,

C. Juchem, C. Cudalbu, R. A. de Graaf, R. Gruetter, A. Henning, H. P. Hetherington, and V . O. Boer, “B0 shimming for in vivo magnetic resonance spectroscopy: experts’ consensus recommendations,”NMR in Biomedicine, vol. 34, no. 5, p. e4350, 2021

2021
[9]

Water and lipid suppression techniques for advanced 1h mrs and mrsi of the human brain: experts’ consensus recommendations,

I. Tk ´aˇc, D. Deelchand, W. Dreher, H. Hetherington, R. Kreis, C. Ku- maragamage, M. Pova ˇzan, D. M. Spielman, B. Strasser, and R. A. de Graaf, “Water and lipid suppression techniques for advanced 1h mrs and mrsi of the human brain: experts’ consensus recommendations,” NMR i...

2021
[10]

High- resolution 1h-mrsi of the brain using spice: data acquisition and image reconstruction,

F. Lam, C. Ma, B. Clifford, C. L. Johnson, and Z.-P. Liang, “High- resolution 1h-mrsi of the brain using spice: data acquisition and image reconstruction,”Magnetic resonance in medicine, vol. 76, no. 4, pp. 1059–1070, 2016

2016
[11]

Fast high-resolution brain metabolite mapping on a clinical 3t mri by accelerated h-fid-mrsi and low-rank constrained reconstruction,

A. Klauser, S. Courvoisier, J. Kasten, M. Kocher, M. Guerquin-Kern, D. Van De Ville, and F. Lazeyras, “Fast high-resolution brain metabolite mapping on a clinical 3t mri by accelerated h-fid-mrsi and low-rank constrained reconstruction,”Magnetic resonance in medicine, vol. 81,...

2019
[12]

Slice imputation: Multiple intermedi- ate slices interpolation for anisotropic 3d medical image segmentation,

Z. Wu, J. Wei, J. Wang, and R. Li, “Slice imputation: Multiple intermedi- ate slices interpolation for anisotropic 3d medical image segmentation,” Computers in biology and medicine, vol. 147, p. 105667, 2022

2022
[15]

Seunet-trans: A simple yet effective unet-transformer model for medical image segmentation,

T.-H. Pham, X. Li, and K.-D. Nguyen, “Seunet-trans: A simple yet effective unet-transformer model for medical image segmentation,”IEEE Access, 2024

2024
[16]

Deep learning for pet image reconstruction,

A. J. Reader, G. Corda, A. Mehranian, C. da Costa-Luis, S. Ellis, and J. A. Schnabel, “Deep learning for pet image reconstruction,”IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 1, pp. 1–25, 2020

2020
[17]

Adversarial in- painting of medical image modalities,

K. Armanious, Y . Mecky, S. Gatidis, and B. Yang, “Adversarial in- painting of medical image modalities,” inICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 3267–3271

2019
[18]

U-net: Convolutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” inMedical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. ...

2015
[19]

3d u-net: learning dense volumetric segmentation from sparse annotation,

¨O. C ¸ ic ¸ek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ron- neberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” inMedical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, Oct...

2016
[20]

Transform- ers for 3d medical image segmentation. arxiv 2021,

A. Hatamizadeh, D. Yang, H. Roth, and D. U. Xu, “Transform- ers for 3d medical image segmentation. arxiv 2021,”arXiv preprint arXiv:2103.10504, 2021

2021 arXiv
[21]

Denoising single mr spectra by deep learning: Miracle or mirage?

M. Dziadosz, R. Rizzo, S. P. Kyathanahally, and R. Kreis, “Denoising single mr spectra by deep learning: Miracle or mirage?”Magnetic resonance in medicine, vol. 90, no. 5, pp. 1749–1761, 2023

2023
[22]

A review of machine learning applications for the proton mr spectroscopy workflow,

D. M. van de Sande, J. P. Merkofer, S. Amirrajab, M. Veta, R. J. van Sloun, M. J. Versluis, J. F. Jansen, J. S. van den Brink, and M. Breeuwer, “A review of machine learning applications for the proton mr spectroscopy workflow,”Magnetic Resonance in Medicine, vol. 90, no. 4, p...

2023
[23]

Deep learning pipeline for quality filtering of mrsi spectra,

M. Raki ´c, F. Turco, G. Weng, F. Maes, D. M. Sima, and J. Slotboom, “Deep learning pipeline for quality filtering of mrsi spectra,”NMR in Biomedicine, vol. 37, no. 7, p. e5012, 2024

2024
[24]

Super-resolution 1h magnetic resonance spectroscopic imaging utilizing deep learning,

Z. Iqbal, D. Nguyen, G. Hangel, S. Motyka, W. Bogner, and S. Jiang, “Super-resolution 1h magnetic resonance spectroscopic imaging utilizing deep learning,”Frontiers in oncology, vol. 9, p. 1010, 2019

2019
[25]

Nimg-16. deep learning super-resolution mr spectroscopic imaging to map tumor metabolism in mutant idh glioma patients,

X. Li and O. Andronesi, “Nimg-16. deep learning super-resolution mr spectroscopic imaging to map tumor metabolism in mutant idh glioma patients,”Neuro-Oncology, vol. 23, no. Suppl 6, p. vi131, 2021

2021
[26]

Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma,

X. Li, B. Strasser, U. Neuberger, P. V ollmuth, M. Bendszus, W. Wick, J. Dietrich, T. T. Batchelor, D. P. Cahill, and O. C. Andronesi, “Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma,”Neuro...

2022
[27]

Super-resolution mr spectroscopic imaging via diffusion models for tumor metabolism mapping,

X. Li, M. Alsubaie, S. M. Perera, L. Gu, S. B. Subasi, and O. C. Andronesi, “Super-resolution mr spectroscopic imaging via diffusion models for tumor metabolism mapping,”Available at SSRN 5228203, 2021

2021
[28]

User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability,

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” Neuroimage, vol. 31, no. 3, pp. 1116–1128, 2006

2006
[29]

Jenkinson, C

M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, “Fsl,”Neuroimage, vol. 62, no. 2, pp. 782–790, 2012

2012
[30]

Adam: A method for stochastic optimization,

D. P. Kingma, “Adam: A method for stochastic optimization,”arXiv preprint arXiv:1412.6980, 2014

2014 arXiv
[31]

Inter- subject stability and regional concentration estimates of 3d-fid-mrsi in the human brain at 7 t,

G. Hangel, B. Spurny-Dworak, P. Lazen, C. Cadrien, S. Sharma, L. Hingerl, E. He ˇckov´a, B. Strasser, S. Motyka, A. Lipkaet al., “Inter- subject stability and regional concentration estimates of 3d-fid-mrsi in the human brain at 7 t,”NMR in Biomedicine, vol. 34, no. 12, p. e4596, 2021

2021

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [13]

Robust smoothing of gridded data in one and higher dimen- sions with missing values,

D. Garcia, “Robust smoothing of gridded data in one and higher dimen- sions with missing values,”Computational statistics & data analysis, vol. 54, no. 4, pp. 1167–1178, 2010

work page 2010

[2] [14]

Inpainting as a technique for estimation of missing voxels in brain imaging,

A. Torrado-Carvajal, D. S. Albrecht, J. Lee, O. C. Andronesi, E.-M. Ratai, V . Napadow, and M. L. Loggia, “Inpainting as a technique for estimation of missing voxels in brain imaging,”Annals of biomedical engineering, vol. 49, pp. 345–353, 2021

work page 2021

[3] [1]

Accelerated mr spectroscopic imaging—a review of current and emerging techniques,

W. Bogner, R. Otazo, and A. Henning, “Accelerated mr spectroscopic imaging—a review of current and emerging techniques,”NMR in Biomedicine, vol. 34, no. 5, p. e4314, 2021

work page 2021

[4] [2]

Nmr chemical shift imaging in three dimensions

T. R. Brown, B. Kincaid, and K. Ugurbil, “Nmr chemical shift imaging in three dimensions.”Proceedings of the National Academy of Sciences, vol. 79, no. 11, pp. 3523–3526, 1982

work page 1982

[5] [3]

Spatially resolved high resolution spectroscopy by “four-dimensional

A. Maudsley, S. Hilal, W. Perman, and H. Simon, “Spatially resolved high resolution spectroscopy by “four-dimensional” nmr,”Journal of Magnetic Resonance (1969), vol. 51, no. 1, pp. 147–152, 1983

work page 1969

[6] [4]

Multivendor implementation and comparison of volumetric whole-brain echo-planar mr spectroscopic imaging,

M. Sabati, S. Sheriff, M. Gu, J. Wei, H. Zhu, P. B. Barker, D. M. Spiel- man, J. R. Alger, and A. A. Maudsley, “Multivendor implementation and comparison of volumetric whole-brain echo-planar mr spectroscopic imaging,”Magnetic resonance in medicine, vol. 74, no. 5, pp. 1209– 1220, 2015

work page 2015

[7] [5]

An advanced mri and mrsi data fusion scheme for enhancing unsupervised brain tumor differentiation,

Y . Li, X. Liu, F. Wei, D. M. Sima, S. Van Cauter, U. Himmelreich, Y . Pi, G. Hu, Y . Yao, and S. Van Huffel, “An advanced mri and mrsi data fusion scheme for enhancing unsupervised brain tumor differentiation,” Computers in biology and medicine, vol. 81, pp. 121–129, 2017. 8

work page 2017

[8] [6]

Super- resolution whole-brain 3d mr spectroscopic imaging for mapping d- 2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1–mutated human gliomas,

X. Li, B. Strasser, K. Jafari-Khouzani, B. Thapa, J. Small, D. P. Cahill, J. Dietrich, T. T. Batchelor, and O. C. Andronesi, “Super- resolution whole-brain 3d mr spectroscopic imaging for mapping d- 2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1–mutated human gliomas,”Radiology, vol. 294, no. 3, pp. 589–597, 2020

work page 2020