Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging

Hyunju Lee; Kyungtaek Jun

REVIEW 3 major objections 5 minor 25 references

Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Region-by-region quantum refinement reconstructs a 100×100 CT image from 2,500 qubits.

desk verdict The region-wise QUBO refinement genuinely cuts qubit count for 100x100 reconstructions, but the advertised 90% dose reduction does not follow from the method—the refinement stage uses the full-resolution sinogram. read the letter →

arxiv 2504.20654 v1 pith:UFRZVOP5 submitted 2025-04-29 quant-ph

Hyunju Lee , Kyungtaek Jun This is my paper · ORCID

classification quant-ph

keywords quantumannealingtomographicreconstructionQUBOregion-wiserefinementsinogramdownscalingsparse-viewCTlow-doseimagingqubitefficiency

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

The paper proposes a way to make quantum-assisted tomographic reconstruction scale to higher resolutions without a full-image quadratic unconstrained binary optimization (QUBO) problem. Its core idea is to reconstruct a small image from a downscaled sinogram, upscale it, and then refine one fixed-size region at a time while the rest of the image stays fixed. On a 100×100 binary Shepp-Logan phantom this recovers the exact target image using four 50×50 region solves, i.e. 2,500 qubits per solve; on a 100×100 integer-valued phantom with pixel values 0–3 it uses 7,500 qubits and closely matches the target. The authors further argue that starting a 500×500 reconstruction from a 50×50 initialization could cut projection data and radiation dose by about 90%.

What carries the argument

The load-bearing mechanism is the region-wise QUBO built from a zero-masked sinogram difference. For a selected region $S_l$, the pixels are encoded with $m$ binary variables each, the region's contribution is isolated as $D(\theta,s)=P(\theta,s)-P_z(\theta,s)$, and the objective $\sum_{\theta,s}(I_P(\theta,s)-D(\theta,s))^2$ is expanded into $\mathbf{x}^\top\mathbf{Q}\mathbf{x}+\mathbf{c}^\top\mathbf{x}$. Because only $n^2 m$ variables enter each solve, qubit count depends on region size rather than final image size; repeated passes over all regions, with interpolation and filtering between passes, drive convergence.

What would settle it

Measure the radiation exposure of the actual acquisition that produces the full-resolution sinogram $P$ used in Equations (6)–(9), then check whether the downscaling step changes delivered dose; if the dose is fixed by acquiring $P$, the claimed 90% reduction is absent. A direct alternative is to acquire a true 50×50 sinogram on a phantom and test whether the refinement pipeline still matches the 100×100 reconstruction.

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Extended reading notes

Core claim

The central discovery is that the qubit bottleneck in quantum tomography can be bypassed by treating reconstruction as coarse-to-fine refinement over spatial regions rather than as one large binary optimization. Each refinement step builds a local QUBO whose objective is the squared difference between the projection of a qubit-encoded region and the target contribution $D(\theta,s)=P(\theta,s)-P_z(\theta,s)$ obtained by subtracting a zero-masked sinogram from the original sinogram. With 100 projection angles, a 100×100 binary phantom is reconstructed from a 50×50 start after two passes over four regions, and a sparse-view 50-angle integer phantom is recovered after smoother interpolation and Gaussian filtering. These results are offered as evidence that 2,500 and 7,500 qubits respectively suffice for 100×100 binary and integer-valued images, and that the pipeline extends naturally to sparse-view acquisition.

Load-bearing premise

The dose-reduction claim rests on the assumption that reducing the sinogram that enters the reconstruction also reduces what is physically acquired; the paper's experiments downscale an already-acquired full-resolution sinogram, so the dose saving is assumed rather than measured.

Editorial extensions

If this is right

A 100×100 binary tomographic image can be reconstructed with 2,500 qubits, and a 100×100 integer-valued image with 7,500 qubits, under the four-region scheme demonstrated.
Sparse-view sinograms with half the projection angles pass through the same region-wise refinement pipeline without algorithmic changes.
If the low-resolution sinogram is acquired directly, the paper's $(1-n/N)$ formula implies starting a 500×500 reconstruction at 50×50 would require roughly 90% less projection data and radiation dose.
Overlapping or adaptively chosen regions are supported, so regions of clinical interest could be refined selectively rather than the whole image.
Initialization smoothness matters: nearest-neighbor upscaling stalls refinement, while smoother interpolation plus Gaussian filtering ($\sigma=1$) lets the second pass converge.

Reading between the lines

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

The dose-reduction claim is not established by the reported experiments: those experiments acquire a full-resolution sinogram and then downscale it, so the radiation exposure is set by the full acquisition, not by the downscaled reconstruction input.
Because the region-wise loop is a block-coordinate descent over spatially disjoint blocks, a classical QUBO solver could run the same decomposition; comparing classical and quantum solvers on the same region subproblems would isolate what quantum annealing actually adds.
A natural testable extension is to acquire a true low-resolution sinogram at the detector level and check whether the same refinement pipeline recovers the 100×100 target; if it does, the dose-reduction claim would move from ratio arithmetic to measurement.

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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

Quick take: the qubit-saving idea is real, the dose-saving headline is not. The paper's refinement stage fits each region to the original full-resolution sinogram P (Eqs. 6-9), so downscaling P after acquisition doesn't reduce the radiation a patient receives. The 90% figure is just (1 - 50/500), and no 500x500 experiment is reported.

What's actually new: the region-wise iterative refinement pipeline, where a low-res QUBO reconstruction is upscaled and then refined region-by-region with the rest of the image fixed. That's a sensible engineering extension of their earlier QUBO work (refs 6, 10, 11), and the demonstrated 100x100 results are decent: 2,500 qubits for binary, 7,500 for integer-valued, with D-Wave energies landing close to the target minima. The finding that nearest-neighbor upsampling creates artifacts that block convergence, and that smoothing helps, is a useful practical detail.

Soft spots, in order of severity:

1. The dose-reduction claim is unsupported. The refinement objective in Section 2.2.1 is defined against P, the full-resolution measured sinogram. Downscaling after measurement is a computational choice, not an acquisition choice. Unless the low-resolution sinogram is physically acquired (or fewer angles are used) and the refinement runs against that reduced-acquisition data, the 90% dose claim is arithmetic on image sizes. The only reduced-acquisition experiment uses 50 angles instead of 100—a 50% view reduction, not 90%.

2. Missing support: no code, no data, no quantitative image metrics (MSE, SSIM), no classical baselines (e.g., FBP or TV-constrained reconstruction). The figures are visual only, and 'identical to the original' is asserted, not measured.

3. The target minimum energies are computed from the same formulation that is being solved, so the D-Wave agreement mainly shows the solver can find the stated minimum, not that the reconstruction is correct. That's fine as a solver check, but it doesn't validate the imaging.

The core technical contribution—qubit-efficient region-wise refinement—holds up for the demonstrated 100x100 cases. The paper is for researchers working on quantum annealing for CT reconstruction. It deserves a serious referee, but the authors need to remove or fundamentally reframe the dose claim, add real sparse-acquisition experiments, and bring in standard metrics and baselines before this is publishable.

I'd send it to review with a clear request for those revisions.

Referee Report

3 major / 5 minor

Summary. The paper proposes a hybrid quantum-classical tomographic reconstruction framework intended to reduce both qubit counts and radiation exposure. The method first downscales a sinogram, solves a low-resolution QUBO using the D-Wave hybrid solver, upscales the resulting image, and then iteratively refines spatial subregions by solving local QUBO problems derived from the original full-resolution sinogram. Two variants are presented: full-view sinogram downscaling and sparse-view acquisition with detector-axis downscaling. Experiments on 100x100 binary and integer-valued Shepp-Logan phantoms show visually accurate reconstructions using 2,500 and 7,500 qubits per region, respectively. The abstract and discussion further claim that reconstructing a 500x500 image from a 50x50 initialization could reduce projection data, and hence radiation dose, by up to 90%.

Significance. If the central qubit-reduction result holds, the contribution is a useful incremental step for quantum-annealing-based tomography: replacing a full 10,000-variable QUBO with four 2,500-variable regional QUBOs is practically meaningful for current D-Wave hardware, and the authors provide detailed D-Wave energy logs and a transparent iterative procedure for two phantom cases. However, the advertised clinical benefit, up to 90% radiation dose reduction, is not supported by the algorithm as written or by the experiments. The 500x500 case is never run, and the refinement stage fits each region to the original full-resolution sinogram rather than to a physically reduced-dose acquisition. The paper therefore contributes a qubit-reduction technique and a data-reduction heuristic, but not a demonstrated low-dose imaging protocol.

major comments (3)

[Section 2.2.1, Eqs. (6)-(9); Abstract; Section IV] The claimed radiation-dose reduction is unsupported because the refinement objective in Eqs. (6)-(9) minimizes the difference between the superposed region sinogram and the original full-resolution sinogram P. Patient dose is determined by how P is physically acquired, so downscaling P after acquisition does not reduce exposure. The sparse-view experiment reduces the number of projection angles from 100 to 50, which is a 50% reduction in views, and detector-axis pooling does not by itself lower dose; no experiment approaches the claimed 90% reduction. Please either revise the abstract and discussion to describe data-volume reduction in post-processing, or redesign the pipeline so refinement is performed against a sinogram physically acquired at reduced resolution and demonstrate the dose saving with an experiment.
[Section I and Section IV, 90% formula] The 90% reduction figure is arithmetically inconsistent with the paper's own downscaling model. Section 2.2.1 downscales both the angular and detector dimensions, with N_S = n_S d1 and M_S = m_S d2, so the number of sinogram elements scales as (n/N)^2, not as n/N. For N=500 and n=50, two-dimensional downscaling gives a 99% reduction in sinogram elements, while the claimed 90% corresponds only to one-dimensional detector downscaling. The paper should define precisely which acquisition parameters are assumed to scale with dose and reconcile the formula (1 - n/N) with the two-dimensional downscaling used in the method.
[Section III, Table I and Figs. 3-4] The accuracy claims rely on visual inspection and on the gap between the target minimum energy and the D-Wave minimum energy. The energy gap is a self-consistency check of the QUBO solve, not a measure of tomographic fidelity, because the target minimum is computed from the same objective being solved. No quantitative image error (e.g., MAE, MSE, SSIM) is reported for the final reconstructions. Please add such metrics, and state explicitly whether the binary reconstruction is exactly equal to the phantom or merely visually identical.

minor comments (5)

[Section 2.2.1, Eq. (10)] The phrase "moving the constant term to the left-hand side" is confusing; the constant term is subtracted from the expanded expression to obtain the QUBO form. Please rephrase for clarity.
[Table I] Target and D-Wave energies are reported with up to ten decimal places, which likely exceeds the precision justified by the sinogram data and the solver. Please report energies with an appropriate number of significant digits.
[Figure 2(b)] The caption says "1 × d vertical patch," but the text describes pooling along the detector/position axis. Please clarify the orientation and terminology so the figure matches the text.
[Section IV, simple decomposition] The estimates for maximum reconstructible image size under a 10,000-qubit constraint and the O(10^6) annealing computations are stated without derivation. Please either provide a short derivation or label these as rough heuristics with clear assumptions.
[End matter] The section titled "APPENDIX AND THE USE OF SUPPLEMENTAL FILES" is empty and should be removed, and the header date "NOVEMBER 2020" does not match the submission year.

Circularity Check

1 steps flagged · score 6.0 of 10

The advertised 90% dose reduction is the chosen resolution ratio (1 − n/N) restated as a result, while the refinement objective fits the original full-resolution sinogram P, making the dose claim circular by construction.

self definitional [Abstract; Section 2.2.1, Eq. (6); Section 4 (Discussion)]
"By initiating the reconstruction from a lower-resolution sinogram and refining it progressively, the total radiation exposure can theoretically be reduced by up to (1 − 𝑛/𝑁) × 100% where 𝑛 and 𝑁 denote the number of pixels along one spatial dimension of the initial and final images, respectively. ... The QUBO objective for region 𝑆𝑙 is then formulated using the difference between the original and zero-masked sinograms as follows: ∑ [{𝐼𝑃 − (𝑃 − 𝑃𝑧)}(𝜃, 𝑠)]2"

The 90% reduction is exactly 1 − n/N for the chosen n = 50 and N = 500, so the claimed 'reduction in projection data' is the definitional ratio of initial to final image dimensions, not a measured consequence of the reconstruction process. Moreover, Eq. (6) minimizes against the original full-resolution sinogram P(θ,s), so the radiation dose is fixed by the acquisition of P; downscaling P after acquisition cannot lower patient exposure. The headline dose-reduction prediction is therefore a restatement of the user-chosen input sizes, equivalent to its input by construction.

full rationale

The core reconstruction pipeline is not circular: it defines a QUBO from sinogram differences, solves it region-wise with a D-Wave hybrid solver, and tests against known phantoms. The qubit counts (2,500 for the binary case and 7,500 for the integer-valued case) follow directly from the stated pixel encodings and region sizes, and the convergence of the solver toward the reported target minima is an empirical check on the optimization, though the target minima are computed from the same QUBO formulation rather than from an independent source. The circularity is confined to the second headline benefit: the 'up to 90% reduction in radiation dose' is the arithmetic quantity (1 − n/N), asserted for a 500×500 case that is not actually experimented on, while the refinement objective in Eq. (6) uses the original full-resolution sinogram P. Self-citations to the authors' earlier QUBO tomography papers are present but are not load-bearing for this defect; those prior results are externally published and are not invoked as uniqueness theorems. Overall score 6: one central advertised prediction reduces by construction, while the qubit-efficiency contribution remains self-contained and non-circular.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central method rests on the authors' prior QUBO tomography work plus several hand-chosen numerical choices (downscaling factor, 1/2 normalization, Gaussian sigma, iteration count). No new physical entities are introduced.

free parameters (4)

sinogram downscaling ratio (d1, d2 or n/N) = d1=d2=2, n=50, N=100
Hand-chosen; defines the initial low-resolution image size and the claimed dose reduction via (1 - n/N).
downscaled sinogram normalization factor = 1/2
Applied in Section 3.1 to the 50x50 sinogram to 'account for reduced path length'; no derivation given.
Gaussian filter sigma = sigma=1
Chosen in Section 3.2 to remove dot artifacts after nearest-neighbor interpolation failed; hand-tuned.
number of refinement iterations = 2 per experiment
Stopping criterion not defined; the paper stops when artifacts disappear visually.

assumptions (6)

domain assumption Parallel-beam Radon transform with line integrals is an accurate model of the imaging process.
Used throughout for sinogram generation and QUBO construction (Eq. 5).
ad hoc to paper The 1/2 scaling of the downscaled sinogram correctly represents the low-resolution image's line integrals.
Section 3.1 states this correction without proof.
domain assumption The QUBO formulation and binary encoding from refs [10, 11] are correct and applicable here.
The paper reuses Eq. (2)-(3) from self-cited prior work.
domain assumption D-Wave hybrid solver returns a solution close enough to the global minimum for the refinement to converge.
Table 1 shows early energy gaps up to 57,000, yet the paper claims exact recovery after two iterations.
ad hoc to paper Region-wise refinement with fixed surrounding pixels converges to the correct image.
No convergence proof; the paper admits NN interpolation prevents convergence, showing sensitivity to initialization.
domain assumption The method applies to fan-beam, cone-beam, synchrotron, and electron tomography without modification.
Stated in Introduction and Discussion, but no experiments or derivations for non-parallel geometries.

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Cite this review

Pith. "Pith review of Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging." pith.science (2026). https://pith.science/paper/UFRZVOP5

@misc{pith2026250420654,
  author       = {Pith},
  title        = {Pith review of: Quantum-Assisted Tomographic Image Refinement with Limited Qubits for High-Resolution Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UFRZVOP5}},
  note         = {Machine review of arXiv:2504.20654}
}

read the original abstract

We propose a quantum-assisted reconstruction framework for high-resolution tomographic imaging that significantly reduces both qubit requirements and radiation exposure. Conventional quantum reconstruction methods require solving QUBO (Quadratic Unconstrained Binary Optimization) problems over full-resolution image grids, which limits scalability under current hardware constraints. Our method addresses this by combining sinogram downscaling with region-wise iterative refinement, allowing reconstruction to begin from a reduced-resolution sinogram and image, then progressively upscaled and optimized region by region. Experimental validation on binary and integer-valued Shepp-Logan phantoms demonstrates accurate reconstructions under both dense and sparsely sampled projection conditions using significantly fewer qubits. We observed that nearest-neighbor interpolation may cause edge artifacts that hinder convergence, which can be mitigated by smoother interpolation and Gaussian filtering. Notably, reconstructing a 500 by 500 image from a 50 by 50 initialization demonstrates the potential for up to 90% reduction in projection data, corresponding to a similar reduction in radiation dose. These findings highlight the practicality and scalability of the proposed method for quantum-enhanced tomographic reconstruction, offering a promising direction for low-dose, high-fidelity imaging with current-generation quantum devices.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018). Quantum circuit learning. Physical Review A, 98(3), 032309

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arXiv 2024

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