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arxiv: 2504.11349 · v3 · submitted 2025-04-15 · 💻 cs.CV · cs.AI· cs.GR

Representation Paradigms in AI-based 3D Radiological Image Reconstruction: A Systematic Review

Yuezhe Yang , Lei Bi , Boyu Yang , Yaqian Wang , Yang He , Yige Peng , Zhe Jin , Xingbo Dong
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Jinman Kim
This is my paper

Pith reviewed 2026-05-22 19:32 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.GR
keywords 3D image reconstructionradiological imagingAI methodsrepresentation paradigmsimplicit neural representationsradiance fieldssystematic reviewmedical image analysis
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The pith

AI-based 3D radiological image reconstruction methods are grouped into four families according to how the target is parameterized.

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

The review surveys current AI techniques for turning 2D radiological scans into 3D volumes and sorts them by the mathematical form used to represent the final image. Four families emerge: discrete grids, explicit basis expansions, explicit primitives, and implicit neural representations. The authors map how these forms relate to one another and single out radiance-field methods as one kind of implicit neural approach. They also collect the standard metrics, public datasets, open challenges, and suggested next steps for the field. A shared taxonomy makes it easier to compare techniques and to see where new hybrids might fit.

Core claim

The central claim is that state-of-the-art AI algorithms for 3D radiological image reconstruction can be organized into four representation families—discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations—according to how the reconstructed target is parameterized, with the added clarification that radiance field methods form a specialized subtype of implicit neural representations.

What carries the argument

The taxonomy of four representation families defined by how the reconstructed target is parameterized.

If this is right

  • Methods within each family can be compared more directly on shared benchmarks.
  • Hybrid algorithms can be designed by mixing elements from different representation families.
  • Radiance-field techniques can be evaluated as a focused subset of implicit neural methods.
  • Future surveys can track progress by measuring how new papers populate each family.
  • Clinical choices of reconstruction algorithm can be guided by the suitability of a given representation for a specific imaging task.

Where Pith is reading between the lines

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

  • The same four-family lens might usefully organize 3D reconstruction work outside radiology, such as in industrial CT or cryo-electron microscopy.
  • New neural architectures could deliberately straddle family boundaries and thereby test the taxonomy's boundaries.
  • If the taxonomy proves durable, it could shorten the time needed to adapt a method from one medical imaging modality to another.

Load-bearing premise

The four chosen families form a complete and stable classification that captures the essential differences among existing methods without leaving out important hybrids or new approaches.

What would settle it

Publication of a well-cited AI method for 3D radiological reconstruction that cannot be placed in any of the four families or that requires a fifth category.

Figures

Figures reproduced from arXiv: 2504.11349 by Boyu Yang, Jinman Kim, Lei Bi, Xingbo Dong, Yang He, Yaqian Wang, Yige Peng, Yuezhe Yang, Zhe Jin.

Figure 1
Figure 1. Figure 1: A brief chronology of the development of techniques related to radiological image reconstruction in literature. However, existing reviews often focus on specific models or summarize only a subset of technologies, frequently over￾looking implicit imaging methods (Ahishakiye et al., 2021; Gothwal et al., 2022; Yasmin et al., 2012). This narrow focus makes it difficult for readers to comprehensively understan… view at source ↗
Figure 2
Figure 2. Figure 2: Five different forms of reconstruction representations in radiological imaging, with the first row showing explicit representations and the second row implicit ones. Higher citation counts are prioritized, as a higher citation count often reflects greater recognition and impact within the field. 3) Representative journals or conferences: Priority is given to papers published in prestigious journals or lead… view at source ↗
Figure 4
Figure 4. Figure 4: Three distinct types of tasks in radiological image reconstruction. While these classification schemes offer valuable in￾sights, they do not address methodological considerations from an artificial intelligence perspective. In the context of 3D representation, reconstruction methods can be cate￾gorized as explicit or implicit. Explicit representations use directly observable formats, such as points, volume… view at source ↗
Figure 5
Figure 5. Figure 5: Categorization results of the reviewed literature, including: (a) classification based on imaging modalities, (b) publication year, and (c) representation forms used in reconstruction methods. discrete structure between points while preserving continu￾ity within individual points. Implicit representations, in con￾trast, rely on continuous functions, such as neural radiance fields, to model imaging in a non… view at source ↗
read the original abstract

The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D image reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) has emerged as a promising approach for improving reconstruction accuracy while reducing acquisition and processing time, thereby minimizing patient radiation exposure and discomfort and ultimately benefiting clinical diagnosis. This review surveys state-of-the-art AI-based 3D reconstruction algorithms in radiological imaging and organizes them into four representation families according to how the reconstructed target is parameterized: discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations. In particular, the review clarifies the relationships among these representation forms and highlights radiance field methods as a specialized subtype of implicit neural representation. In addition, we summarize commonly used evaluation metrics and benchmark datasets for radiological image reconstruction. Finally, we discuss the current state of development, major challenges, and future research directions in this rapidly evolving field. Our project is available at: https://github.com/Bean-Young/AI4Radiology.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper is a systematic review of AI-based 3D radiological image reconstruction algorithms. It organizes state-of-the-art methods into four representation families according to how the reconstructed target is parameterized—discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations—while clarifying relationships among these forms and identifying radiance field methods as a specialized subtype of implicit neural representations. The review also summarizes commonly used evaluation metrics and benchmark datasets and discusses the current state of development, major challenges, and future research directions. A GitHub repository is provided for project resources.

Significance. If the four-family taxonomy proves stable and comprehensive, the review would supply a useful organizing framework for navigating the rapidly growing literature on AI-driven 3D reconstruction in radiology, helping researchers identify relationships between approaches and prioritize future work. The open GitHub repository strengthens the contribution by supporting reproducibility and community access to the surveyed resources.

major comments (2)
  1. [§3] §3 (Representation Paradigms): The central claim that the four families clarify relationships rests on the assumption that methods can be assigned unambiguously. The manuscript does not supply explicit decision criteria or a classification flowchart for hybrid cases (e.g., grid-accelerated implicit fields or neural primitives with explicit discretization), leaving open the possibility that such methods are placed in a single family without discussion of the choice. This directly affects the claimed stability of the taxonomy.
  2. [§2] §2 (Literature Search and Selection): As a systematic review, the soundness of the organizational framework depends on transparent reporting of the search protocol, databases queried, keywords, and inclusion/exclusion criteria. While the abstract states the scope, the absence of quantitative summary statistics (number of papers screened, included, and per-family distribution) prevents full verification that the taxonomy captures the field without systematic omission.
minor comments (2)
  1. [Abstract] Abstract and §4: The statement that radiance fields are a 'specialized subtype' of implicit neural representations would benefit from a short clarifying sentence or diagram in the main text, as some readers may not immediately recognize the distinction.
  2. [Table 1] Table 1 (or equivalent summary table): Ensure that any overview table of representative papers includes a column or footnote indicating the primary representation family assigned to each method, to make the taxonomy operational for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our systematic review manuscript. We have carefully considered each major comment and outline our responses below, along with the revisions we will make to address the concerns raised.

read point-by-point responses

  1. Referee: [§3] §3 (Representation Paradigms): The central claim that the four families clarify relationships rests on the assumption that methods can be assigned unambiguously. The manuscript does not supply explicit decision criteria or a classification flowchart for hybrid cases (e.g., grid-accelerated implicit fields or neural primitives with explicit discretization), leaving open the possibility that such methods are placed in a single family without discussion of the choice. This directly affects the claimed stability of the taxonomy.

    Authors: We agree that explicit decision criteria and guidance for hybrid cases would enhance the taxonomy's clarity and claimed stability. While the manuscript discusses inter-paradigm relationships and provides representative examples, it does not include a dedicated flowchart or step-by-step assignment rules for ambiguous or hybrid methods. In the revised version, we will add a new subsection to §3 that defines primary classification criteria (based on the dominant parameterization of the reconstructed target) and includes a flowchart for handling hybrids, such as grid-accelerated implicit fields (classified under implicit neural representations with explicit acceleration noted) and neural primitives with discretization (classified under explicit primitives with discretization as a secondary implementation detail). revision: yes

  2. Referee: [§2] §2 (Literature Search and Selection): As a systematic review, the soundness of the organizational framework depends on transparent reporting of the search protocol, databases queried, keywords, and inclusion/exclusion criteria. While the abstract states the scope, the absence of quantitative summary statistics (number of papers screened, included, and per-family distribution) prevents full verification that the taxonomy captures the field without systematic omission.

    Authors: We recognize that quantitative reporting is essential for verifying the comprehensiveness of a systematic review. The current §2 describes the search strategy, databases, keywords, and inclusion/exclusion criteria at a high level but omits aggregate statistics and a PRISMA-style flow diagram. We will revise §2 to incorporate these elements, including the total number of papers identified through searches, the number screened and excluded with reasons, the final number included, and the distribution of included papers across the four representation families. This will be presented both in text and via a standard flow diagram to enable full assessment of the review's coverage. revision: yes

Circularity Check

0 steps flagged

No circularity: systematic review organizes external literature without self-referential derivations

full rationale

This paper is a survey that classifies existing AI-based 3D radiological reconstruction methods from the cited literature into four representation families based on parameterization (discrete grid, explicit basis expansion, explicit primitive, implicit neural). No equations, predictions, fitted quantities, or derivations are present. The taxonomy is an organizational lens drawn from external sources; relationships are clarified by reference to prior work rather than by reducing to the authors' own inputs or self-citations. The GitHub link is a supplementary resource, not a load-bearing claim. The central claim remains independent of any circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a systematic review the paper introduces no new mathematical derivations, fitted parameters, or postulated entities. It relies on standard review methodology and the cited body of radiological imaging literature.

pith-pipeline@v0.9.0 · 5734 in / 1230 out tokens · 42981 ms · 2026-05-22T19:32:43.721860+00:00 · methodology

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    organizes them into four representation families according to how the reconstructed target is parameterized: discrete grid representations, explicit basis expansion representations, explicit primitive representations, and implicit neural representations

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis

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    UltraGS combines Gaussian Splatting with physics-inspired acoustic modeling to enable real-time novel view synthesis from sensorless ultrasound, reporting SOTA PSNR/SSIM and 64 fps on a new clinical dataset.

Reference graph

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