Modular Phantom Fabrication

Updated 14 December 2025

Modular phantom fabrication is a technique that constructs test objects from interchangeable, standardized modules, enabling flexible configurations across diverse experimental setups.
It enhances reproducibility by enforcing precise quality control and alignment, with assembly tolerances as strict as ±0.1 mm for marker spacing in physical implementations.
Digital frameworks in modular phantom design enable systematic benchmarking and algorithm validation through voxelwise simulation, modular processing pipelines, and standardized data outputs.

Modular phantom fabrication encompasses the design and assembly of physical or digital test objects ("phantoms") constructed from configurable, standardized subunits, each supporting adaptation to various experimental, simulation, or validation scenarios in biomedical imaging, robotics, or related fields. The modular approach allows extension, reconfiguration, and precise quality control, facilitating rigorous benchmarking, calibration, and ground-truth data generation for scientific studies.

1. Modular Phantom Concepts and Rationale

Modular phantoms are distinguished by their construction from repeatable, interchangeable modules, supporting scalability and tailored configuration. This paradigm is foundational in fields such as MRI geometric distortion assessment (Slagowski et al., 2020), 3D MR spectroscopy simulation (Sande et al., 20 Dec 2024), and modular aerial robotics (Mu et al., 2019). The design allows flexible adaptation to system constraints, domain-specific artifacts, and benchmarking needs, enabling reproducible assessment of acquisition pipelines, analysis algorithms, and hardware systems.

The key motivations include:

Configurability: Modular phantoms can be scaled, reshaped, or have modules swapped to fit various hardware or study designs.
Reproducibility: Standardized fabrication and rigorous tolerancing support reliable quantitative comparison across sessions, sites, or scanner platforms.
Extendibility: Additional components (e.g., tissue types, fiducials, payloads) can be incorporated to address evolving experimental requirements.

2. Physical Modular Phantom Fabrication in Biomedical Imaging

A canonical application is geometric distortion mapping in MRI, as described by Slagowski et al. (Slagowski et al., 2020). The physical modular phantom comprises a two-dimensional array of rectangular foam blocks (100 mm × 100 mm × 400 mm), each housing a lattice of high-contrast spherical markers at 50 mm pitch.

Block Design and Assembly

Materials: Closed-cell polyethylene or polyurethane foam (density ≈40 kg/m³), milled flat (<±1 mm) for coplanarity.
Labels and Orientation: Each block is stamped with a two-letter position code and oriented via a machined arrow (+Z).
Fiducial Markers: 6.00 mm ±0.05 mm spheres, embedded using interference-fit ABS/PLA plugs.
Grid Construction: Markers arranged on a Cartesian grid, with adjacent blocks sharing boundary markers, achieving a scalable, contiguous lattice throughout the assembled phantom.
Alignment: Blocks are mechanically aligned with stainless-steel dowel pins (Ø4 mm), inserted through pre-drilled holes into an acrylic baseplate or rails.
Fastening: Blocks can be held via adhesive or clamped with nylon screws to preserve geometric fidelity.

Quality Control

Assembly tolerances are maintained within ±0.1 mm for marker spacing and ±0.2 mm for block coplanarity. QC involves caliper or CMM measurement of plug coordinates, CT-based overlay validation, and repeated assembly trials to ensure reproducibility. Average marker positioning errors below 0.2 mm and assembly-induced setup variance below 0.1 mm have been established (Slagowski et al., 2020).

3. Digital Modular Phantom Frameworks in Spectroscopy

Digital modular phantom frameworks, exemplified by the 3D MRS digital brain phantom presented by van de Sande et al. (Sande et al., 20 Dec 2024), provide a computational analog for simulating complex imaging or spectroscopy data. The architecture is explicitly modular, comprising four major pipeline modules:

Module	Inputs & Processing	Outputs
Anatomical Importer	NIfTI label maps, relaxometry, resampling via TorchIO	"Skeleton" object: {labels, T1, T2*...}
MRS Phantom Builder	Skeleton, metabolite library, user settings, spatial gradients	DigitalPhantom object
Signal Simulator	Phantom, basis set, user profiles for water, macromolecules, lipids	Simulated MRSI grid (complex)
Resolution Adjustment	High-res spectra grid, downsample block size	Final NIfTI-MRS data, JSON metadata

This structured modularization ensures separation of anatomical map processing, metabolic parameterization, signal synthesis, and output formatting, enhancing extensibility, validation, and systematic benchmarking.

Mathematical Framework

Key mathematical operations include:

Voxelwise spectral synthesis:

$S_{\rm metab}(x,\omega) = \sum_{i=1}^N C_i(x)\, M_i(\omega-\phi_i)\, \exp\bigl[-R2^*_i(x)\,t\bigr]$

Spatial smoothing (Gaussian convolution):

$\widetilde{C}_i(x) = (G_\sigma * C_i)(x)$

Downsampling by block-wise averaging:

$S_{\rm down}(k,\omega) = \frac{1}{|B_k|} \sum_{x\in B_k} S(x,\omega)$

Noise modeling: Additive $\mathcal{CN}(0, \sigma_{\rm noise}^2)$ at each voxel-frequency pair.

Interoperability

Outputs are written in standard file formats (NIfTI, NIfTI-MRS) for downstream compatibility with external toolboxes and pipelines, supporting direct integration into machine learning workflows and quantitative algorithm development.

4. Control and Fabrication in Modular Robotic Phantoms

In aerial robotics, the modular phantom principle underlies systems such as the Universal Flying Objects (UFO) platform (Mu et al., 2019). Here, any rigid "phantom frame" is converted into a multirotor by attaching modular control and propelling units.

Hardware Modules

Control Module: 3D-printed housing, IMU, autopilot (Pixfalcon STM32F4), and communication subsystems.
Propelling Modules: 16.5 cm booms with brushless motors, onboard IMU, and position-calibrated via IMU-based estimation.
Attachment Method: Gel-pad adhesives or screws to rigid surfaces (≥3 mm thickness).

Adaptive Configuration Estimation

IMU-based estimation: Collects real-time sensor data from all modules, calibrates mass, orientation, and position.
Kinematics and Dynamics: The system automatically assembles the thrust–moment mapping matrix $\bm A_u$ based on the arrangement.
Adaptive Control: An online geometric controller tracks full SE(3) outputs (position, yaw) while refining dynamic parameters via Lyapunov-stable adaptation laws.

Validation and Tolerances

Experiments with 4–8 module phantoms (payload 0.2–0.8 kg) demonstrate stable hover and tracking, with post-adaptation RMS position error <5 cm and <1.5 cm in z (Mu et al., 2019). Assembly tolerances of ±2 mm for module alignment are recommended to avoid off-axis thrust.

5. Tolerances, Quality Assurance, and Reproducibility

Stringent tolerancing and QC protocols are fundamental to modular phantom utility, whether in analog or digital domains.

Physical Phantoms

Assembly error sources: Block misalignment, plug hole location accuracy (<0.1 mm), foam deformation.
QC protocols: Measurement of sample points, multimodal imaging (CT overlays), repeated assembly checks.
Operational thresholds: Marker-positioning error <0.1 mm; total assembly variance <0.13 mm (Slagowski et al., 2020).

Digital Phantoms

Data export in standardized formats: Ensures cross-platform comparability and enables automated testing.
Verification: Ground-truth parameter recovery (e.g., RMSE between simulated and estimated maps), systematic artifact and SNR perturbation, lesion insertion for benchmarking analysis algorithms (Sande et al., 20 Dec 2024).

6. Scalability, Customization, and Extensibility

Modular phantoms are purposefully designed for extension:

Physical scaling: The number of blocks or modules can be adjusted to cover varying fields of view or payload requirements, always maintaining grid continuity and integration with QC frameworks. For example, a 600 mm × 300 mm × 800 mm MRI distortion phantom requires 36 foam blocks in a 6×3×2 arrangement (Slagowski et al., 2020).
Anatomical extensibility (digital): New skeletons introduced by importing label maps and relabeling. Tissue types, metabolite profiles, and artifact models are directly extensible by updating data tables or configuration files in Python-based implementations (Sande et al., 20 Dec 2024).
Robotic adaptation: Additional flight modules or payload configurations are supportable via the generic dynamic estimation machinery, with symmetry maximized for control stability (Mu et al., 2019).

7. Applications and Future Directions

Modular phantom fabrication underpins critical tasks across biomedical imaging and robotics, with specific usage including:

Validation of geometric fidelity in MRI systems, informing distortion correction and radiotherapy planning (Slagowski et al., 2020).
Algorithm development and benchmarking in MRS and MRSI, where digital phantoms provide known ground truth for quantification pipelines (Sande et al., 20 Dec 2024).
Aerial robotics research, supporting fast prototyping and validation of control methodologies, with full hardware–software–parameter pipeline integration (Mu et al., 2019).

Planned extensions include advanced artifact simulation (e.g., k-space errors, spatially varying B0/B1 fields), inclusion of additional tissue or metabolite models, and more generalized mechanical and software frameworks to further enhance the flexibility and applicability of modular phantom platforms.