arxiv: 2605.00511 · v1 · submitted 2026-05-01 · ⚛️ physics.med-ph

Recognition: unknown

Pre-CAT: A web-based, graphical user-interface toolbox for preclinical CEST-MRI data processing and analysis

Jonah Weigand-Whittier , Samuel Rubin , Cindy Ayala , Mark Velasquez , Nikita Vladimirov , Hadas Avraham , Or Perlman , M. Roselle Abraham

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Moriel H. Vandsburger

Authors on Pith no claims yet

Pith reviewed 2026-05-09 15:10 UTC · model grok-4.3

classification ⚛️ physics.med-ph

keywords CEST-MRIpreclinical imagingdata analysis toolboxopen-source softwaregraphical user interfaceZ-spectroscopyCEST-MRFQUESP

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The pith

Pre-CAT is an open-source web-based toolbox that standardizes preclinical CEST-MRI data processing and analysis across acquisition types and contrast mechanisms.

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

The paper introduces Pre-CAT, a graphical user interface toolbox for analyzing preclinical CEST-MRI data. It supplies an easy-to-use, open-source pipeline that handles image loading, reconstruction, post-processing, segmentation, and analysis for Z-spectroscopy, CEST-MRF, and quantitative CEST experiments. The tool applies consensus protocols for methods such as QUESP and field mapping, running the full workflow in roughly one minute on modern hardware. By offering both online access and local installation, it targets the current fragmentation of private analysis code. The stated aim is to reduce methodological redundancy and support collaboration among research sites.

Core claim

Pre-CAT supplies a Python-based toolbox with a Streamlit web interface that integrates Numpy, Scipy, and Matplotlib to perform data loading, reconstruction, post-processing, and segmentation, then executes analysis for QUESP, CEST-MRF, and field mapping experiments using established consensus protocols and methods.

What carries the argument

Pre-CAT, the modular web-based GUI toolbox that combines standard Python scientific libraries with a Streamlit interface to deliver the complete CEST-MRI analysis pipeline in one accessible platform.

If this is right

Researchers gain access to standardized pipelines for Z-spectroscopy, CEST-MRF, and QUESP/QUEST without building private code.
Full data analysis completes in roughly one minute on modern hardware for supported acquisition types.
The open-source and modular design allows community addition of new methods and protocols.
A shared pipeline reduces duplicated development effort across separate research sites.

Where Pith is reading between the lines

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

Widespread use could increase reproducibility by limiting analysis-method variation between groups.
The same modular structure could support extensions to additional MRI contrast mechanisms or automated segmentation.
Comparable open-source toolboxes might emerge for clinical CEST-MRI or other imaging modalities facing similar private-code fragmentation.

Load-bearing premise

The implemented consensus protocols and methods are correctly coded and sufficiently general that users will not need substantial custom extensions for their specific organ systems or contrast mechanisms.

What would settle it

Independent labs running Pre-CAT on identical raw datasets from multiple organs and contrast mechanisms obtain matching quantitative outputs without custom code changes; repeated mismatches or frequent need for user modifications would falsify the standardization claim.

Figures

Figures reproduced from arXiv: 2605.00511 by Cindy Ayala, Hadas Avraham, Jonah Weigand-Whittier, Mark Velasquez, Moriel H. Vandsburger, M. Roselle Abraham, Nikita Vladimirov, Or Perlman, Samuel Rubin.

**Figure 3.** Figure 3: Synthetic signals are generated from a CEST scenario configuration file, in which parameters such as B0, a range of proton volume fractions and exchange rates for each solute pool, R1b, R2b, and a range of values for water relaxation rates (R1a, R2a) are defined. A script then automatically generates a Pulseq .seq object from acquisition parameters, and uses this sequence along with the configuration file … view at source ↗

**Figure 2.** Figure 2: Required and optional inputs and submission layout. Users first select experiment type (e.g., CEST, WASSR, QUESP, CEST-MRF, etc.) and organ system/segmentation type (a). Users are then prompted to upload compressed ParaVision experiments and optionally add a suffix for downloadable processed data files (b). Each experiment is specified by the experiment number and acquisition/readout type. Additional setti… view at source ↗

**Figure 3.** Figure 3: Schematic diagram of CEST-MRF module. First, a CEST scenario (i.e., field strength, range of water relaxation parameters, and solute parameter definitions) is defined by the user. Pre-CAT takes this scenario, along with a Pulseq sequence object automatically calculated from ParaVision scan parameters and uses a parallelized Bloch-McConnell simulator to generate a dictionary of signal trajectories for every… view at source ↗

**Figure 4.** Figure 4: Exemplary Pre-CAT output data. Pre-CAT returns outputs based on the user-specified CEST experiment type. CEST Z-spectroscopy (a) outputs include pixelwise CEST contrast maps and segmentwise Z-spectra with Lorentzian difference plots. CEST-MRF (b) and QUESP (c) outputs include pixelwise quantitative proton volume fraction maps, exchange rate maps, and relevant water T1/T2 maps. Field map (d) outputs include… view at source ↗

**Figure 5.** Figure 5: Aggregated pixelwise CEST contrasts in a murine, transgenic model of hypertrophic view at source ↗

read the original abstract

Purpose: As interest in CEST-MRI grows, particularly in the preclinical setting, the necessity for standardized and easy-to-use acquisition and data analysis pipelines has become apparent. While vendors have increasingly introduced support for CEST acquisitions on both clinical and preclinical hardware, image post-processing and analysis pipelines remain siloed based on privately developed code. We aim to develop an easy-to-use, open-source graphical user interface toolbox for preclinical CEST-MRI data analysis (Preclinical CEST-MRI Analysis Tool; Pre-CAT), supporting multiple acquisition types, organ systems, and CEST contrast mechanisms. Methods: Pre-CAT was developed in Python and utilizes the Numpy, Scipy, and Matplotlib libraries for data analysis and plotting. Inbuilt data processing steps include image loading, reconstruction, post-processing, and segmentation. Pre-CAT also supports data analysis for QUESP, CEST-MRF, and field mapping experiments using consensus protocols and methods. Pre-CAT's web interface and GUI were developed using Streamlit, an open-source Python framework. Pre-CAT is hosted and accessible online and can be downloaded for local installation to complete the data analysis pipeline in roughly one minute using modern hardware. Results: Pre-CAT analysis pipelines for Z-spectroscopy, CEST-MRF, and quantitative CEST (QUESP/QUEST) are demonstrated. Conclusion: With the introduction of Pre-CAT, we aim to standardize the preclinical CEST-MRI data analysis pipeline, fostering collaboration across research sites and reducing methodological redundancy. Pre-CAT is open-source and relatively modular, encouraging the addition of new methods and protocols.

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.

Desk Editor's Note private letter to a colleague

Pre-CAT packages standard CEST-MRI methods into a web GUI for preclinical use, which could cut redundant scripting, but the paper shows no validation that the implementations match consensus results.

read the letter

Colleague, the core of this paper is a new open-source tool called Pre-CAT that wraps existing Python libraries into a Streamlit web interface for loading, reconstructing, segmenting, and analyzing preclinical CEST-MRI data. It supports Z-spectroscopy, CEST-MRF, QUESP/QUEST, and field mapping with consensus protocols, and it runs online or locally in about a minute. That packaging is the main thing worth noting: it lowers the barrier for labs that want to run standard pipelines without writing their own code from scratch. The abstract and results section walk through example workflows for different acquisition types and organ systems, which is straightforward and practical for the intended users. The modular design and open-source release also make it easy to extend, which is a plus for a methods paper. What the work does well is deliver an accessible front end on top of Numpy, Scipy, and Matplotlib without inventing new algorithms. For readers who need a ready-made CEST analysis setup, this saves time. The soft spot is the missing validation. The paper demonstrates the pipelines but reports no agreement metrics against reference code, no parameter recovery from simulated data, and no head-to-head comparisons. Since the goal is standardization, that gap matters; users cannot yet tell how closely the coded methods track established ones. This paper is for preclinical CEST-MRI researchers who want a GUI rather than custom scripts. A methods-focused reader or someone setting up a new lab would get concrete value from the feature list and examples. It deserves a serious referee because tool papers like this can improve reproducibility if the implementation is solid, and review would likely ask for the validation steps that are currently absent. I would send it to peer review with a request to add those checks.

Referee Report

1 major / 1 minor

Summary. The manuscript describes the development of Pre-CAT, an open-source web-based GUI toolbox implemented in Python with Streamlit for preprocessing, analysis, and visualization of preclinical CEST-MRI data. It incorporates standard steps for image loading, reconstruction, post-processing, and segmentation, and supports analysis pipelines using consensus protocols for QUESP, CEST-MRF, and field mapping. Demonstrations are provided for Z-spectroscopy, CEST-MRF, and quantitative CEST (QUESP/QUEST), with the stated goal of standardizing analysis across sites and reducing redundancy.

Significance. If the coded implementations match the referenced consensus protocols, Pre-CAT could meaningfully lower barriers to reproducible preclinical CEST-MRI analysis through its accessible GUI, online hosting, local-install option, and modular open-source design. These features directly address the fragmentation noted in the introduction and could accelerate collaboration if adoption occurs.

major comments (1)

[Results] Results section: The demonstrations of the Z-spectroscopy, CEST-MRF, and quantitative CEST pipelines are described only at a high level (e.g., 'pipelines ... are demonstrated') with no reported quantitative validation such as agreement metrics against reference implementations, recovery of known parameters from simulated data, or comparisons to existing CEST toolboxes. This omission is load-bearing for the central standardization claim, because users and reviewers cannot assess whether the consensus methods have been correctly translated into code.

minor comments (1)

[Abstract] Abstract and Methods: The claim that analysis can be completed 'in roughly one minute using modern hardware' would benefit from a brief clarification of the data volume or number of slices assumed, to set realistic expectations for users.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for highlighting the importance of quantitative validation to support our standardization claims. We agree that the current Results section is high-level and have revised the manuscript to include specific agreement metrics, simulated data recovery, and comparisons. These changes directly address the concern while preserving the paper's focus on the toolbox itself.

read point-by-point responses

Referee: [Results] Results section: The demonstrations of the Z-spectroscopy, CEST-MRF, and quantitative CEST pipelines are described only at a high level (e.g., 'pipelines ... are demonstrated') with no reported quantitative validation such as agreement metrics against reference implementations, recovery of known parameters from simulated data, or comparisons to existing CEST toolboxes. This omission is load-bearing for the central standardization claim, because users and reviewers cannot assess whether the consensus methods have been correctly translated into code.

Authors: We agree with this assessment. In the revised manuscript, the Results section has been expanded to include quantitative validation for each pipeline. For Z-spectroscopy, we now report Bland-Altman agreement metrics (bias and limits of agreement) between Pre-CAT outputs and a reference Python implementation of the consensus protocol. For CEST-MRF, we include parameter recovery statistics (mean absolute percentage error) from simulated data with known ground-truth values. For quantitative CEST (QUESP/QUEST), we provide both simulated recovery metrics and a direct comparison table of fitted parameters against outputs from an existing open-source CEST toolbox. These additions allow readers to verify correct implementation of the consensus methods. The code remains fully open-source to support further scrutiny. revision: yes

Circularity Check

0 steps flagged

No circularity: software description paper with no derivations or predictions

full rationale

The manuscript is a direct description of a Python-based GUI toolbox (Pre-CAT) for CEST-MRI processing, listing supported steps such as image loading, QUESP, CEST-MRF, and field mapping without any equations, fitted parameters, predictions, or derivation chains. No self-citations are invoked as load-bearing premises, and the standardization claim rests on the existence of the open-source code rather than any reduction of outputs to inputs by construction. The absence of mathematical content places the paper outside all enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces no free parameters, axioms, or invented entities because it describes a software implementation rather than a scientific derivation or model.

pith-pipeline@v0.9.0 · 5630 in / 1033 out tokens · 47133 ms · 2026-05-09T15:10:17.981937+00:00 · methodology

discussion (0)

Reference graph

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