OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography

## Abstract

Abstract Prostate cancer is one of the most common and lethal cancers among men, making its early detection critically important. Although ultrasound imaging offers greater accessibility and cost-effectiveness compared to MRI, traditional transrectal ultrasound (TRUS) methods suffer from low sensitivity, especially in detecting anteriorly located tumors. Ultrasound computed tomography (USCT) provides quantitative tissue characterization, but its clinical implementation faces significant challenges, particularly under anatomically constrained limited-angle acquisition conditions specific to prostate imaging. To address these unmet needs, we introduce OpenPros , the first large-scale benchmark dataset explicitly developed for limited-view prostate USCT. Our dataset includes over 280,000 paired samples of realistic 2D speed-of-sound (SOS) phantoms and corresponding ultrasound full-waveform data, generated from anatomically accurate 3D digital prostate models derived from real clinical MRI/CT scans and ex vivo ultrasound measurements, annotated by medical experts. Simulations are conducted under clinically realistic configurations using advanced finite-difference time-domain (FDTD) and Runge-Kutta acoustic wave solvers, both provided as open-source components. Through comprehensive baseline experiments, we demonstrate that state-of-the-art deep learning methods surpass traditional physics-based approaches in both inference efficiency and reconstruction accuracy. Nevertheless, current deep learning models still fall short of delivering clinically acceptable high-resolution images with sufficient accuracy. By publicly releasing OpenPros , we aim to encourage the development of advanced machine learning algorithms capable of bridging this performance gap and producing clinically usable, high-resolution, and highly accurate prostate ultrasound images. The dataset is publicly accessible at https://open-pros.github.io/ .

## 1 Introduction

Prostate cancer is the second most common malignancy among men and is one of the leading causes of cancer-related deaths worldwide, and every eight men suffers from it radtke2020re; tosoian2024development. Since the 5-year survival rate for prostate cancer patients significantly drops from nearly 100% to approximately 34% once the disease progresses from localized or regional stages to distant metastases PCaStats, early detection of aggressive prostate cancer is of vital importance. Medical imaging plays an essential role in this early detection. Among the available imaging modalities, multiparametric MRI (mpMRI) is currently recognized as the most advanced and accurate imaging tool for detecting and localizing clinically significant prostate cancer. However, the high cost and limited accessibility of mpMRI restrict its widespread adoption, particularly in rural or low-resource settings de2014accuracy; kasivisvanathan2018mri.

In contrast, ultrasound imaging is widely accessible, cost-effective, and capable of real-time imaging. Prostate ultrasound is typically performed transrectally, producing B-mode (brightness-mode) images. Although transrectal ultrasound (TRUS) is the clinical standard for routine prostate evaluations and biopsy guidance, it has a sensitivity of only 30%–50% for detecting clinically significant tumors and a specificity of 70%–80% beemsterboer1999changing; chen2016utility. Studies have further shown that tumors located in the anterior or apical prostate regions are often undetectable with TRUS due to poor soft-tissue contrast and restricted acoustic windows, and TRUS cannot reliably distinguish malignant lesions from benign conditions such as chronic prostatitis marivcic2010transrectal.

Ultrasound computed tomography (USCT) has emerged as a promising alternative, reconstructing quantitative tissue parameters such as speed-of-sound (SOS) and acoustic attenuation, which serve as potential biomarkers for malignancy wu2024multiparametric; williams2021mp22. However, the anatomical constraints of prostate imaging inherently limit the acquisition aperture, creating a challenging scenario referred to as the *limited-angle condition*. Unlike idealized imaging setups where transducers surround the entire imaging domain, prostate imaging is anatomically restricted to transrectal and transabdominal placements, resulting in sparse and angularly limited data. Traditional physics-based methods typically struggle under these conditions, demonstrating slow convergence, severe ill-posedness, and significant reconstruction artifacts wang2025mask; gilboy2020dual. Developing robust USCT algorithms capable of accurately handling limited-angle data is thus critically needed for clinical prostate imaging.

Furthermore, clinical translation of prostate USCT faces considerable barriers due to the complexity and specialization of current imaging systems. To date, only two USCT systems (SoftVue and QTscan) have received U.S. FDA approval, and both systems focus exclusively on full-angle breast imaging with custom hardware setups unsuited for prostate applications sandhu2015frequency; malik2018quantitative. These existing systems operate at relatively low frequencies, rely on patient positioning incompatible with prostate imaging, and require hours for reconstruction. Thus, there is an urgent need for efficient, generalizable, and clinically adaptable prostate-specific USCT platforms. Crucially, this advancement depends on the availability of realistic, anatomically precise digital phantoms and datasets, which are currently lacking in the field gilboy2020dual; aalamifar2017enabling.

Additionally, prostate imaging complexity is increased by high tissue heterogeneity and proximity to multiple adjacent organs and bony pelvic structures, invalidating simplified fluid medium assumptions typically used in breast imaging. These factors severely compromise USCT image reconstruction quality, further emphasizing the necessity of specialized prostate-specific datasets.

Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown potential for overcoming these limitations by learning complex mappings directly from ultrasound data to high-resolution SOS maps chugh2021survey; havaei2017brain. Data-driven approaches bypass computational bottlenecks encountered by iterative solvers and demonstrate the ability to reconstruct detailed tissue properties even under sparse and noisy acquisition conditions. Instead of hours to days of image reconstruction using physics-based methods and the requirement of expert reading as a follow-up, the relatively short inference time and the automatic analysis enable faster and easier diagnosis for better patient experience. Furthermore, transformer-based architectures have recently demonstrated remarkable performance in medical imaging by effectively modeling long-range spatial dependencies, a feature particularly beneficial for ultrasound tomography due to the extensive spatial interaction of acoustic waves.

Despite these advancements, progress has been significantly hampered by the lack of large-scale, high-fidelity datasets supporting the development, evaluation, and reproducibility of innovative reconstruction algorithms.
Existing USCT datasets are typically for breast imaging, which are either synthesized in simulation or derived from real phantoms. Related datasets are listed in Table [1](https://arxiv.org/html/2505.12261v1#S1.T1). They exhibit diversities and reflect anatomical realism to a good extent but not the best. There are also anatomical datasets for the male pelvic region but not for USCT purposes. The commercial anatomy softwares such as Zygote Body and Complete Anatomy provide different pricing options for viewing and downloading, but they generally lack anatomical varieties. Last but not least, no publicly available dataset adequately addresses the unique challenges posed by limited-view prostate ultrasound computed tomography and the existence of bones in the imaging view while simultaneously providing realistic wave propagation modeling and comprehensive full-waveform data.

Table: Table 1: Comparison between our OpenPros and other existing datasets for the male pelvic region or for medical ultrasound computed tomography. The symbols ✓, ✗, and NA indicate that the dataset contains, does not contain, or is not applicable to the corresponding feature, respectively.

Motivated by these challenges and inspired by the success of OpenFWI deng2022openfwi and $\mathbf{E}^{FWI}$ feng2023mathbf datasets, we introduce OpenPros, the first large-scale dataset specifically designed for limited-angle prostate USCT scenarios. A schematic illustration of the overall pipelines of OpenPros is shown in Figure [1](https://arxiv.org/html/2505.12261v1#S0.F1). Our dataset comprises over 280,000 paired 2D SOS phantoms and ultrasound full-waveform data derived from anatomically realistic 3D prostate models generated from clinical MRI/CT scans and ex vivo ultrasound measurements, annotated meticulously by clinical experts. OpenPros serves as a critical benchmark facilitating advances in computational efficiency, limited-angle reconstruction accuracy, rapid clinical adaptability, and comprehensive method comparisons across various imaging conditions, including ray-based, single scattering, high-frequency, and limited-angle scenarios.

In summary, our contributions to the community include:

- 1.
Large-scale, anatomically realistic benchmark dataset: The first comprehensive prostate USCT dataset, derived from clinical MRI/CT scans and detailed expert annotations, designed explicitly to address limited-angle imaging conditions.
- 2.
High-fidelity, publicly available simulation tools: Advanced finite-difference time-domain (FDTD) and Runge-Kutta implicit iterative acoustic solvers, openly accessible alongside our dataset to facilitate reproducibility and method development.
- 3.
Comprehensive benchmarking of inversion methods: Thorough evaluation of physics-based and state-of-the-art deep learning methods under realistic limited-angle conditions, establishing clear performance baselines and guiding future algorithmic improvements.

The remainder of this paper is structured as follows: In Section [2](https://arxiv.org/html/2505.12261v1#S2), we overview the fundamentals of USCT and our task setup. Section [3](https://arxiv.org/html/2505.12261v1#S3) details our dataset construction. Section [4](https://arxiv.org/html/2505.12261v1#S4) describes benchmarking experiments. In Section [6](https://arxiv.org/html/2505.12261v1#S6), we discuss dataset strengths, limitations, and future research directions. Finally, we conclude in Section [7](https://arxiv.org/html/2505.12261v1#S7) by summarizing our key contributions and the broader implications of OpenPros.

## 2 Ultrasound Computed Tomography and Forward Modeling

In the context of USCT, the forward problem involves simulating acoustic wave propagation through soft tissues, which is governed by the acoustic wave equation. Assuming an isotropic medium with constant density, the forward modeling equation is given by:

$$ $\nabla^{2}p-\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}=s,$ (1) $$

where $\nabla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y^
{2}}$ in 2D, $c(x,y)$ denotes the spatially varying SOS map, $p(x,y,t)$ is the acoustic pressure field, and $s(x,y,t)$ represents the ultrasound source. In our simulations, the source $s$ is prescribed as a controlled ultrasound excitation. Clinically, the primary goal of ultrasound tomography is to reconstruct spatially varying SOS maps from recorded pressure fields, enabling accurate tissue characterization and anomaly detection, such as identifying tumors or lesions.

The forward modeling of ultrasound propagation thus entails computing the pressure field $p$ from a given SOS distribution $c$, represented by the highly nonlinear mapping $p=f(c)$, where $f(\cdot)$ encapsulates the complex wave propagation phenomena defined by Equation ([1](https://arxiv.org/html/2505.12261v1#S2.E1)). In practice, the recorded ultrasound signals form a 4-dimensional tensor $p\in\mathbb{R}^{S\times R\times T\times B}$, where S is the number of sources, $R$ is the number of receivers, T represents the number of time steps, and B denotes the batch dimension. Specifically, in our simulated prostate dataset, we set $S=20$, $R=322$, and $T=1000$. The output SOS maps to be reconstructed are represented as 3-dimensional tensors $c\in\mathbb{R}^{H\times W\times B}$, where $H$ and $W$ represent the spatial height and width dimensions, respectively. In our specific configuration, each SOS map has a spatial resolution of $401\times 161$ grid points.

Data-driven USCT leverages neural networks to directly approximate the inverse mapping $c=f^{-1}(p)$, as demonstrated in recent studies wu2019inversionnet. Thus, the specific task addressed in this paper is the supervised learning problem, formulated as $\min_{\theta}\mathbb{E}{p,c}\left[\mathcal{L}(c,\hat{c})\right]$, where $\hat{c}=f{\theta}^{-1}(p)$. Here, $f_{\theta}^{-1}$ represents a deep neural network parameterized by $\theta$, trained on pairs of simulated ultrasound signals $p$ and corresponding ground-truth SOS maps $c$. The training objective $\mathcal{L}$ typically incorporates quantitative metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Structural Similarity Index Measure (SSIM) and Pearson Correlation Coefficient (PCC).

## 3 OpenPros Dataset

OpenPros is the first large-scale benchmark dataset explicitly designed to facilitate research in limited-angle prostate ultrasound computed tomography (USCT). It contains anatomically realistic 2D speed-of-sound phantoms, organ segmentation labels, and corresponding simulated ultrasound waveforms derived from detailed 3D digital prostate models. The patient level anatomy ID is named as 3_01, 3_02, 3_03 and 3_04. The prostate level anatomy ID is named as the date of acquisition (in total 62 prostates). Detailed naming strategy an be found in the Appendix [C](https://arxiv.org/html/2505.12261v1#A3).

In the following subsections, we first show the basic statistics of our dataset and highlight the related domain interests. We then describe the design strategies of 3D/2D prostate phantoms which maximize the fidelity. At the end, we discuss the ultrasound data simulation setups.

### 3.1 Dataset Statistics

OpenPros consists of 280,000 paired examples of 2D SOS phantoms and ultrasound data, systematically derived from realistic 3D digital models. The essential characteristics and data dimensions are summarized in Table [2](https://arxiv.org/html/2505.12261v1#S3.T2).

**Table 2: Dataset summary for the OpenPros USCT dataset. SOS maps are formatted as (sample $\times$ channel (#physical params, SOS here) $\times$ depth (vertical) $\times$ width (horizontal)); ultrasound data as (sample $\times$ channel (#sources) $\times$ time $\times$ #receivers).**
| Dataset | Size | #Train / #Validation / #Test | Ultrasound Data Shape | SOS Map Shape |
| --- | --- | --- | --- | --- |
| OpenPros | 6.8 TB | 224K / 28K / 28K | $1140\times 40\times 1000\times 161$ | $1140\times 1\times 401\times 161$ |
|  |  |  |  |  |

OpenPros supports various critical research topics, including:

Tissue Interfaces: Clearly defined interfaces among prostate, bladder, and surrounding tissues, essential for organ boundary delineation and accurate pathology differentiation. Segmentation labels enhance precision in evaluating inversion algorithms.

Lesion Characterization: Realistically modeled synthetic lesions (e.g., tumors) introduce SOS discontinuities, challenging algorithms in lesion detection and characterization, critical for diagnostic accuracy.

Clinical Variability and Realistic Imaging Conditions: Systematic slicing of high-resolution 3D digital models captures realistic anatomical variability, with advanced finite-difference time-domain (FDTD) simulations reflecting clinical imaging conditions, including limited aperture, acoustic noise, and tissue heterogeneity.

Anatomy-Aware Inversion: Integrated segmentation masks guide targeted inversion studies, emphasizing reconstruction accuracy in clinically significant regions, thus promoting anatomically detailed reconstruction assessments.

Our sophisticated data generation pipeline, encompassing digital phantom modeling, detailed anatomical labeling, and precise acoustic simulations, significantly enhances the clinical relevance and diversity of the dataset.

### 3.2 3D Prostate Phantom Design

Figure: Figure 2: (a) Anatomical structure and probe placement. Two probes-abdominal (on the body surface) and transrectal (in the rectum)-are used in our simulation. Image courtesy of Complete Anatomy. (b) 3D digital SOS phantom. SOS distribution in the anatomically realistic prostate model.
Refer to caption: extracted/6450222/Fig/3D_sketch.png

It is important to note that the 3D digital phantoms were derived from human CT/MRI nyholm2018mr and USCT scans of ex vivo prostate specimens parikh2024mp30; wiskin2022imaging; williams2021mp22. Major organs were annotated by experts using T2-weighted MRI, fat was segmented from T1-weighted MRI, bones were segmented from X-ray CT, and the speed of sound and attenuation of ex vivo prostate samples were measured using a QT scanner (QT Imaging Inc., Novato, California, USA). Speed of sound of other organs were acquired from ITIS foundation tissue database baumgartner2024database. To best mimic the tissue heterogeneity, we employed Gaussian distributions with given mean values and standard deviations from the tissue database to assign speed of sound in different tissue types. These derivations from human data reduces the reliance on synthetic simulation and maximizes the fidelity of the dataset, especially in the prostate area. Additional details on phantom construction, applications, and open access availability can be found in wu2024realistic. The 3D abdominal anatomical structure and the ultrasound probe placement sketch can be found in Figure [2](https://arxiv.org/html/2505.12261v1#S3.F2)(a) and the illustration of 3D phantom can be found in Figure [2](https://arxiv.org/html/2505.12261v1#S3.F2)(b).

### 3.3 2D Prostate Phantom Extraction

We generate 2D speed-of-sound phantoms by slicing our anatomically realistic 3D prostate volumes under clinically realistic probe configurations. For each phantom, we place one transrectal probe in the rectum and one abdominal probe on the body surface, then sample cross-sections across a range of rotations ($\pm 45^{\circ}$) and small random perturbations. This process yields 280 K paired 2D SOS maps and corresponding ultrasound waveforms that faithfully capture patient-specific anatomy and limited-angle acquisition variability.

More detailed 2D phantom extraction strategy can be found in Appendix [B](https://arxiv.org/html/2505.12261v1#A2)

### 3.4 Ultrasound Data Simulation

We simulate ultrasound wave propagation using a finite-difference time-domain (FDTD) solver based on the 2D acoustic wave equation discussed above. The numerical scheme adopts fourth-order accuracy in space and second-order accuracy in time, offering a reliable trade-off between numerical precision and computational efficiency. This configuration is particularly well-suited for capturing fine-grained wave interactions in heterogeneous prostate tissue environments.

Each simulation is conducted under two acquisition configurations, placing sources and receivers along the top and bottom boundaries of the computational grid. A total of 20 sources (10 at the top and 10 at the bottom, shown as yellow stars in Figure [3](https://arxiv.org/html/2505.12261v1#S3.F3)) are uniformly distributed along each boundary. For every source, 322 receivers (shown as red dots in Figure [3](https://arxiv.org/html/2505.12261v1#S3.F3)) are placed across the entire lateral extent of the domain, enabling comprehensive capture of the scattered wavefield. A Ricker wavelet with a 1 MHz peak frequency serves as the excitation pulse, consistent with clinical transducer characteristics. The wavefield is recorded over 1,000 time steps at a sampling interval of $\Delta t=1\times 10^{-7}$ seconds, covering a total duration of $100~{}\mu s$. To suppress artificial reflections, 120 grid points of absorbing boundary condition (ABC) are applied to each boundary. Two examples of our simulations and the corresponding SOS maps are shown in Figure [3](https://arxiv.org/html/2505.12261v1#S3.F3).

Figure: Figure 3: Examples of simulated ultrasound data and phantoms: without (top) and with (bottom) bone in the phantoms. We show two example channels of reflections and transmissions with sources (yellow stars) and receivers (red dots) on the two probes. Our PDE solvers can simulate complex and realistic ultrasound wave phenomena, such like transmissions, reflections, direct waves and multi-scatterings.
Refer to caption: extracted/6450222/Fig/data_samples_2.png

The spatial discretization of the domain uses a grid spacing of 0.375 mm, resulting in a field of view of approximately 60 mm in depth and 150 mm in width. This configuration mirrors the anatomical scale of the prostate and its surrounding structures. A summary of the physical simulation parameters and the physical meaning of the dimension is shown in Table [3](https://arxiv.org/html/2505.12261v1#S3.T3).

Table: Table 3: Physical Meaning of the Prostate USCT Dataset

Importantly, the simulated ultrasound data for each sample contains 40 distinct channels, organized to reflect practical probe configurations: Channels (0–9) Source on body surface, receiver on body surface; (10–19) source on body surface, receiver in rectum; (20–29) source in rectum, receiver in rectum; and (30–39) source in rectum, receiver on body surface. This setup emulates both conventional transabdominal and transrectal imaging pathways, enabling detailed studies of transmission and reflection across diverse acoustic paths.

## 4 OpenPros Benchmarks

**Table 4: Quantitative results of benchmarking methods on the prostate USCT dataset.**
| Method | MAE$\downarrow$ | RMSE$\downarrow$ | SSIM$\uparrow$ | PCC$\uparrow$ |
| --- | --- | --- | --- | --- |
| InversionNet | 0.0089 | 0.0433 | 0.9845 | 0.9798 |
| ViT-Inversion | 0.0123 | 0.0566 | 0.9774 | 0.9728 |

OpenPros lets us systematically investigate three core questions under limited-angle prostate USCT: (1) Inference efficiency, (2) Reconstruction accuracy, and (3) OOD generalization.

#### Methods & Metrics

We compare two physics-based baselines-Delay-and-Sum Beamforming and multi-stage physics-based USCT inversion-against two data-driven models: InversionNet, and ViT-Inversion. Performance is measured with four metrics: mean absolute error (MAE) and root-mean-square error (RMSE) for numerical fidelity, plus SSIM and Pearson correlation (PCC) for perceptual and structural similarity. All models are trained and evaluated under identical settings on NVIDIA H100 GPUs.

### 4.1 Benchmark Methods for Prostate USCT

Figure: Figure 4: Benchmark results for limited-angle prostate USCT. Each column shows a different inversion method on the same phantom: (col 1) ground-truth SOS map; (col 2) Delay-and-Sum beamforming; (col 3) physics-based USCT; (col 4) InversionNet; (col 5) ViT-Inversion. Rows correspond to three representative prostate slices illustrating challenging (top), moderate (middle), and simple (bottom) anatomical scenarios. Zoom-in figures of the prostate region (orange squares) are shown in Figure [5](https://arxiv.org/html/2505.12261v1#S4.F5).
Refer to caption: extracted/6450222/Fig/baseline_results.png

Beamforming, a classical ultrasound imaging method, reconstructs images by aligning and summing received ultrasound echoes according to assumed sound speeds and propagation paths. In this baseline, beamforming serves as a fast, widely-adopted approach for generating initial ultrasound images, highlighting the inherent limitations under restricted-view conditions.

Physics-based USCT is perform in a three-stage multi-frequency framework. Starting from a smoothed initial SOS model, we first invert low-frequency data, then mid-frequency data, and finally full-band data. At each stage, synthetic waveforms are generated via our forward operator and compared to observed data; the SOS model is updated by minimizing the waveform misfit.

InversionNet wu2019inversionnet proposed a fully-convolutional network to model the seismic inversion process. With the encoder and the decoder, the network was trained in a supervised scheme by taking 2D (time $\times$ # of receivers) seismic data from multiple sources as the input and predicting 2D (depth $\times$ length) velocity maps as the output.

ViT-Inversion: A Vision Transformer that partitions the 4D waveform tensor $[B,S,T,R]$ into spatio-temporal patches, embeds them into tokens, and applies multi-head self-attention to capture long-range wave interactions. A lightweight upsampling CNN refines the patch-wise outputs into full-resolution SOS maps.

(Training details in App. [E](https://arxiv.org/html/2505.12261v1#A5).)

### 4.2 Result Analysis

#### Quantitative Results

Table [4](https://arxiv.org/html/2505.12261v1#S4.T4) reports mean absolute error (MAE), root mean squared error (RMSE), structural similarity index (SSIM), and Pearson correlation coefficient (PCC) for our two learned baselines. Traditional physics-based USCT (not shown) achieves an RMSE around 0.16 and SSIM $\sim 0.90$, roughly halving the error of simple beamforming but still leaving significant room for improvement. In contrast, InversionNet reduces RMSE to 0.0433-nearly a fourfold improvement over physics-based inversion-and attains the highest MAE (0.0089), SSIM (0.9845), and PCC (0.9798). ViT-Inversion also outperforms the physics-based approach, cutting RMSE in half (0.0566) and achieving an SSIM of 0.9774.

Figure: Figure 5: Zoom-in comparison of prostate regions. Enlarged views (orange squares in Figure [4](https://arxiv.org/html/2505.12261v1#S4.F4)) showing detailed reconstruction quality within the prostate region across baseline methods: (col 1) Ground truth; (col 2) Delay-and-Sum beamforming; (col 3) physics-based USCT; (col 4) InversionNet; (col 5) ViT-Inversion. Each row corresponds to the same anatomical scenario as in Figure [4](https://arxiv.org/html/2505.12261v1#S4.F4). Note that although the learned methods recover general anatomical shapes more clearly, the fine internal structures and boundaries remain poorly resolved.
Refer to caption: extracted/6450222/Fig/result_zoom.png

#### Qualitative Observations

Figure [4](https://arxiv.org/html/2505.12261v1#S4.F4) presents three representative prostate slices under challenging (top), moderate (middle), and simple (bottom) anatomical conditions. Delay-and-Sum beamforming yields noisy, low-contrast images incapable of resolving detailed prostate structures. Physics-based USCT significantly reduces these artifacts and better recovers the general gland shape but produces overly blurred images lacking fine anatomical details. Machine learning-based methods, including InversionNet and ViT-Inversion, markedly outperform physics-based USCT in reconstructing the global anatomical structure and boundaries. However, the zoom-in prostate images shown in Figure [5](https://arxiv.org/html/2505.12261v1#S4.F5) illustrate that despite better overall shape reconstruction, these learned methods still cannot accurately resolve fine structures within the prostate. The internal prostate structures remain smoothed, and small lesions or detailed boundaries are not distinctly reconstructed, indicating significant room for improvement in imaging resolution and accuracy.

#### Inference Efficiency Comparison

In addition to superior accuracy, data-driven methods offer remarkable computational efficiency suitable for real-time imaging applications, as summarized in Table [7](https://arxiv.org/html/2505.12261v1#A5.T7) (see appendix). Traditional physics-based inversions, such as beamforming and multi-stage USCT, incur significant computational overheads, requiring approximately 4 hours and 24 hours per sample, respectively. In sharp contrast, the data-driven approaches achieve near-instantaneous reconstructions: InversionNet requires only 4.9 milliseconds per sample, while ViT-Inversion completes inference in roughly 8.9 milliseconds due to its transformer architecture. This stark difference highlights the practical feasibility and potential clinical value of learned models in enabling rapid, real-time prostate imaging.

## 5 Ablation Study

To assess how well our models generalize to truly unseen anatomies, we conducted three out-of-distribution tests using data splits that reflect realistic clinical scenarios: (1) Patient-Level Generalization: Train on patients 3_01, 3_02, 3_03, test on an entirely unseen patient 3_04. (2) Leave-One-Prostate-Out: Train on 60 prostates from all four patients except two held-out prostate (i.e. 2022-06-06 & 2022-06-09), test on those withheld prostates. (3) Combined Generalization: Train on 60 prostates drawn only from patients 3_01–3_03, test on both remaining prostates of patient 3_04.

Table [5](https://arxiv.org/html/2505.12261v1#S5.T5) summarizes MAE, RMSE, SSIM, and PCC for InversionNet and ViT-Inversion under each scenario. In the *patient-level generalization* test, all methods suffer substantial performance degradation. InversionNet’s MAE increases from 0.0089 (within-distribution) to 0.0653 and its RMSE from 0.0433 to 0.2465, while SSIM falls to 0.9111. ViT-Inversion experiences a similar drop, with MAE rising to 0.0802, RMSE to 0.2898, and SSIM to 0.9055. This indicates that entirely novel patient anatomies pose a significant challenge for learned inversion models.

By contrast, the *leave-one-prostate-out* test shows only minimal accuracy loss. InversionNet maintains MAE=0.0070, RMSE=0.0291, and SSIM=0.9886, nearly matching in-distribution performance. ViT-Inversion also remains strong with MAE=0.0100, RMSE=0.0424, and SSIM=0.9823. These results suggest that intra-patient anatomical variability is comparatively easier for the networks to handle.

The most demanding *combined OOD* scenario amplifies the accuracy drop. InversionNet’s SSIM settles at 0.9137 (MAE=0.0624, RMSE=0.2370), and ViT-Inversion achieves SSIM=0.8972 (MAE=0.0862, RMSE=0.3045). Although InversionNet retains slightly higher fidelity under these extreme conditions, neither method reaches within-distribution levels, underscoring that truly patient-agnostic reconstruction remains an open challenge.

**Table 5: Generalization Test Results. Evaluation of inversion methods on unseen prostate anatomies.**
| Scenario | Method | MAE$\downarrow$ | RMSE$\downarrow$ | SSIM$\uparrow$ | PCC$\uparrow$ |
| --- | --- | --- | --- | --- | --- |
| Patient-level | InversionNet | 0.0653 | 0.2465 | 0.9111 | 0.6574 |
| ViT-Inversion | 0.0802 | 0.2898 | 0.9055 | 0.6098 |  |
| Leave-one-prostate | InversionNet | 0.0070 | 0.0291 | 0.9886 | 0.9891 |
| ViT-Inversion | 0.0100 | 0.0424 | 0.9823 | 0.9852 |  |
| Combined OOD | InversionNet | 0.0624 | 0.2370 | 0.9137 | 0.6753 |
| ViT-Inversion | 0.0862 | 0.3045 | 0.8972 | 0.5774 |  |

Overall, these ablations demonstrate that while machine learning based methods significantly outperform physics-based USCT under matched conditions, their ability to generalize to entirely unseen anatomies is still limited.

## 6 Discussion

Our OpenPros dataset offers an unprecedented resource for developing limited-angle prostate USCT algorithms. With over 280,000 paired SOS phantoms and ultrasound simulations derived from clinical data, it realistically captures tissue heterogeneity and anatomical constraints of prostate imaging. Our open-source FDTD and Runge-Kutta solvers ensure transparent benchmarking and reproducibility.

However, the dataset currently includes a limited number of patient anatomies, potentially underrepresenting certain anatomical variations. Additionally, we simulate only SOS distributions, omitting other critical acoustic parameters like attenuation and density. Our 2D simulations do not account for three-dimensional propagation and out-of-plane scattering, simplifying some real-world conditions.

Future work will expand the patient dataset, introduce multiparametric acoustic maps, and extend simulations to three dimensions. Incorporating clinically relevant pathologies and carefully designed out-of-distribution scenarios will further enhance the robustness and clinical applicability of future USCT solutions.

## 7 Conclusion

In this work, we have introduced OpenPros, the first comprehensive, large-scale benchmark dataset specifically designed for limited-angle prostate ultrasound computed tomography. With over 280,000 expertly annotated 2D speed-of-sound phantoms paired with high-fidelity simulated ultrasound data, OpenPros facilitates efficient benchmarking of both physics-based and advanced deep learning reconstruction algorithms. Our baseline experiments clearly demonstrate that deep learning approaches significantly outperform conventional physics-based methods in terms of inference speed and image quality. However, critical challenges remain, notably in resolving fine anatomical details within the prostate and achieving robust generalization across unseen anatomies. By making OpenPros publicly available, we encourage the research community to leverage and expand this foundational resource, ultimately advancing toward clinically viable, high-resolution prostate imaging solutions.

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## Appendix A OpenPros Public Resources and Licenses

First and foremost, the reproducibility of OpenPros benchmarks is guaranteed by a number of public resources, listed below. Remarkably, we have a group (link available below) where any related discussion is welcome. Our team also promises to maintain the platform and support further developments based on the community feedback.

- •
Website: [https://open-pros.github.io](https://open-pros.github.io)
- •
Dataset URL: [https://open-pros.github.io/datasets](https://open-pros.github.io/datasets)
- •
Github Benchmark Repository: [https://github.com/hanchenwang/OpenPros](https://github.com/hanchenwang/OpenPros)
- •
Pretrained Models:
InversionNet checkpoint can be downloaded at: [https://drive.google.com/file/d/1TVrrzmVzUQvUyDTZ4il-EGH5EG60yFBL/view?usp=share_link](https://drive.google.com/file/d/1TVrrzmVzUQvUyDTZ4il-EGH5EG60yFBL/view?usp=share_link).
ViT-Inversion checkpoint can be downloaded at: [https://drive.google.com/file/d/1TVrrzmVzUQvUyDTZ4il-EGH5EG60yFBL/view?usp=share_link](https://drive.google.com/file/d/1TVrrzmVzUQvUyDTZ4il-EGH5EG60yFBL/view?usp=share_link).
- •
Google Group: [https://groups.google.com/g/openfwi](https://groups.google.com/g/openfwi)

The codes are released on Github under OSS license and BSD-3 license. We also attach the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License to the data.

## Appendix B Details of 2D Phantom Extraction

Figure: Figure 6: Schematic of our 2D phantom extraction. The green plane indicates the area between transrectal and abdominal probes.
Refer to caption: extracted/6450222/Fig/slicing.png

Starting from our 3D prostate volumes (SOS maps and organ masks), we apply the following pipeline to produce each 2D phantom:

1. Volume loading and isotropic resampling.
We load the segmentation masks and SOS volumes from each patient scan, resample anisotropic voxels onto a uniform 0.375 mm grid, and pad exterior regions with a baseline SOS of 1,500 m/s to emulate coupling gel.

2. Initial probe placement.
A pair of valid points separated by  6 cm within the segmented rectum defines the transrectal transducer line. The abdominal transducer is then positioned 15 cm anterior along the same axis.

3. Systematic rotation and translation.
To emulate clinical acquisition angles, we rotate both probe lines jointly from –45° to +45° in 5° increments and translate them within ±1 mm in each Cartesian direction.

4. Slice extraction.
At each probe configuration, we extract the 2D plane containing both transducer lines. The resulting slice preserves the true anatomical interfaces, tissue heterogeneity, and relative probe geometry.

5. Random perturbations.
We add a small jitter of ±1° to each rotation angle and ±1 mm to each transducer coordinate to enrich variability.

Altogether, this procedure produces 280 000 unique 2D SOS phantoms with matching ultrasound data under limited-angle conditions. An illustration of the slicing geometry is shown in Figure [6](https://arxiv.org/html/2505.12261v1#A2.F6).

## Appendix C Naming of OpenPros

The data files follow a structured naming convention: 3_0{i}_P_{Date}_{Category}.npy, where {i} represents patient IDs (1–4), {Date} denotes unique prostate sample identifiers, and {Category} specifies data types, including ultrasound data (data), segmentation labels (mask), and SOS maps (sos). For example, 3_02_P_2022-06-06_sos.npy refers to SOS data for patient 3_02 and prostate sample 2022-06-06.

## Appendix D Evaluation Metrics

To evaluate the performance of the proposed CNN-based reconstruction methods, we employ four quantitative metrics that comprehensively assess numerical accuracy and perceptual similarity between the reconstructed and true SOS images. Here, we denote the true SOS image as $s$, the predicted image as $\hat{s}$, and $N$ as the total number of pixels in each image.

- •
Mean Squared Error (MSE) measures the pixel-wise squared differences:
$\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(s_{i}-\hat{s}_{i})^{2},$
(2)
where $s_{i}$ and $\hat{s}_{i}$ denote the true and predicted SOS values at pixel $i$.
- •
Mean Absolute Error (MAE) calculates the average absolute difference, providing robustness against outliers:
$\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|s_{i}-\hat{s}_{i}|.$
(3)
- •
Structural Similarity Index Measure (SSIM) assesses the perceptual quality, accounting for luminance, contrast, and structural similarities:
$\text{SSIM}(s,\hat{s})=\frac{(2\mu_{s}\mu_{\hat{s}}+C_{1})(2\sigma_{s\hat{s}}+
C_{2})}{(\mu_{s}^{2}+\mu_{\hat{s}}^{2}+C_{1})(\sigma_{s}^{2}+\sigma_{\hat{s}}^
{2}+C_{2})},$
(4)
where $\mu_{s},\mu_{\hat{s}}$ denote the means, $\sigma_{s},\sigma_{\hat{s}}$ the variances, and $\sigma_{s\hat{s}}$ the covariance between true and predicted images. Constants $C_{1}$ and $C_{2}$ stabilize division by weak denominators.
- •
Pearson Correlation Coefficient (PCC) quantifies the linear correlation between true and predicted images, measuring structural consistency:
$\text{PCC}=\frac{\sum_{i=1}^{N}(s_{i}-\bar{s})(\hat{s}_{i}-\bar{\hat{s}})}{
\sqrt{\sum_{i=1}^{N}(s_{i}-\bar{s})^{2}}\sqrt{\sum_{i=1}^{N}(\hat{s}_{i}-\bar{
\hat{s}})^{2}}},$
(5)
where $\bar{s}$ and $\bar{\hat{s}}$ denote the mean pixel values of the true and predicted SOS images, respectively.

## Appendix E Training Procedure

All baseline models (InversionNet and ViT-Inversion) were trained using a supervised learning approach on the proposed large-scale OpenPros dataset. The dataset comprises a total of 280,080 samples, with 255,360 used for training and 27,360 for validation/testing.

For fair comparison, we kept the training settings identical across all methods. We employed the Adam optimizer with an initial learning rate of $10^{-4}$, a batch size of 512, and trained each model for up to 120 epochs. Early stopping was implemented based on validation set performance to prevent overfitting. Table [6](https://arxiv.org/html/2505.12261v1#A5.T6) summarizes these training parameters.

**Table 6: Training parameters consistently used across all baseline methods.**
| Total Epochs | Training Samples | Test Samples | Batch Size | Optimizer | Learning Rate |
| --- | --- | --- | --- | --- | --- |
| 240 | 255,360 | 27,360 | 512 | Adam | $10^{-4}$ |

The model sizes and training times for each baseline method are listed in Table [7](https://arxiv.org/html/2505.12261v1#A5.T7). All experiments were conducted on NVIDIA H100 GPUs.

**Table 7: Computational cost and model size comparison for baseline methods on the OpenPros prostate USCT dataset.**
| Method | Training Cost (GPU hour) | Inference Cost (GPU second/sample) | Model Size |
| --- | --- | --- | --- |
| Beamforming | N/A | 14400 | N/A |
| USCT | N/A | 86400 | N/A |
| InversionNet | 240 | 0.0049 | 20.45M |
| ViT-Inversion | 128 | 0.0089 | 28.33M |

## Appendix F Conventional k-Wave vs. Our Ultrasound Simulation Algorithms

Ultrasound imaging methods can be broadly categorized by their simulation paradigms. Conventional TRUS simulations, commonly used for B-mode imaging, rely on signal processing pipelines such as delay-and-sum (DAS) beamforming applied to simulated echoes. These simulations are often implemented using toolboxes like MATLAB k-Wave [24], which model acoustic wave propagation using pseudospectral methods and reconstruct images from envelope-detected signals. While fast and widely adopted in the clinical ultrasound community, this approach simplifies underlying physics and often introduces artifacts due to assumptions like homogeneous backgrounds, limited diffraction modeling, or approximate transducer responses.

In contrast, our dataset adopts a physically grounded modeling framework based on the 2D acoustic wave equation. We employ a finite-difference solver with fourth-order spatial and second-order temporal accuracy to simulate full-wave propagation through heterogeneous tissue. This method captures wavefront curvature, multi-path scattering, and fine-grained variations in the SOS map, offering a far more realistic approximation of ultrasound interactions in complex anatomical regions such as the prostate. This fidelity is especially critical under limited-angle acquisition constraints common in prostate imaging, where traditional methods often fail to reconstruct accurate quantitative maps.

Unlike DAS-based methods, our simulation does not rely on beamforming post-processing, allowing it to serve as a foundation for quantitative imaging tasks like USCT. While k-Wave simulations are computationally efficient for B-mode image formation, our FDTD method remains tractable at scale and better suited for generating ground truth waveforms for learning-based inverse solvers.

Moreover, we provide two forward modeling solvers as part of our open-source release: a finite-difference solver with fourth-order spatial and second-order temporal accuracy, and an alternative Runge-Kutta implicit iterative solver. The former prioritizes accuracy and efficiency in time-domain modeling, while the latter offers enhanced numerical stability and can serve as a basis for future 3D extensions. Both solvers are publicly available with our dataset, supporting reproducibility and extensibility for ultrasound tomography and machine learning research. Additionally, the solvers support execution on both GPU and CPU platforms.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 8
presentation: 3
questions: 1) The dataset is generated from four patient anatomies and 62 ex-vivo prostate samples. This needs to become more transparent in the paper -- from the very start.
2) How were the four patient anatomies selected? Are they representative? Please provide some info about where they come from: healthy, diseased, varied in size, age, pathology?
3) The paper slices 3D anatomies into many 2D samples. how do you ensure that (almost) identical slices or planes are not split across train/validation/test sets?
4) The large difference between ID and OOD results suggests overfitting to specific anatomies. This worries me a lot. Maybe the authors can also report confidence intervals across anatomies and make the OOD evaluation a central table in the main paper?
5) How is the performance affected by sensor or probe minor placement errors or SOS background shifts? A robustness study would help. 
6) It is not clear to me what will be finally released by the authors. waveforms, SOS maps, code, and 3D phantoms? What about the original MRI/CT data?
7) in terms of related work, a more SOTA approach would be to use neural operator learning -- something like this https://arxiv.org/abs/2304.03297 . I suggest you discuss this in the conclusions?
8-9) A couple of more technical questions: 
the forward model only focuses on SOS. Out-of-plane scattering, attenuation, and density variations are ignored. Have you tried to validate the simulator against 3D k-Wave simulations? How big are the errors when ignoring such things? 
And how does ex-vivo SOS differ from in-vivo values at body temperature?
soundness: 4
strengths: 1. Addresses a clinically important and computationally demanding imaging problem
2. Provides an open and standardized resource that can make it easier for other people to design and evaluate their own models
3. The selected benchmarks are reasonable and cover both physics-based and learned approaches
4. The paper is technically good and the simulation details are sufficient, I think, if someone wants to reproduce the results
summary: The authors build 3D digital phantoms of the male pelvis by combining four MRI/CT patient anatomies with 62 ex-vivo prostate SOS maps, resulting in 248 model variants. They then simulate two-dimensional acoustic wave propagation using a finite-difference time-domain solver, producing paired input–output samples: ultrasound waveforms and their corresponding ground-truth SOS maps. By slicing and rotating these models, they generate about 280K 2D examples meant to reflect the geometric and acoustic variability encountered in trans-rectal and trans-abdominal USCT setups.

They also use the dataset to compare some baselines:
1) classical beamforming,
2) iterative physics-based USCT inversion,
and 3,4) two learned models: InversionNet (cnn) and a ViTvariant.

The learned models achieve lower pixelwise errors and run orders of magnitude faster than physics-based inversion, though their outputs remain too smooth to resolve fine prostate boundaries or lesions. Unfortunately out-of-distribution tests, where one patient anatomy is held out, reveal major performance degradation. This i s evidence of poor generalization beyond the limited training anatomies.
weaknesses: My main concern: the dataset is based on only four patient anatomies and this is mentioned only deep in the methods and appendix (I think it should be mentioned in the abstract or, worst-case, introduction). The anatomical diversity is therefore extremely limited!

- All simulations are 2D and reconstruct only SOS, ignoring attenuation and density. 
- The machine-learning component is incremental (standard CNN and ViT baselines) without any more recent architectures or hybrid physics-learning integration. But this is not a major issue because I recognize that the main contribution of the paper is teh dataset.
- Evaluation focuses on image-level metrics rather than clinically more meaningful goals such as gland boundary accuracy or lesion detection
- The ex-vivo data used for SOS ground truth may differ from in-vivo conditions. This is not something the paper discusses or tries to quantify.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: - I am actually unsure about the suitability of this paper for ICLR. Why do the authors think this is important for the ICLR community (typically focused on neural network representations or similar)? To be clear, I think this paper is valuable for the broader machine learning community, but seems better suited for IEEE TCI, APL Bioengineering, or even CVPR or WACV.
- The paper begins with a discussion of prostate cancer. Is there any evidence that the generated dataset would help in detection of prostate cancer, given that the reconstruction quality (e.g. Fig 5) is so poor?
- What problem specifically would training on the generated dataset solve?
soundness: 4
strengths: - The paper is very well written and justified
- The paper does a good job of designing tasks for the dataset that could potentially determine its efficacy. The areas of (1) inference efficiency, (2) reconstruction accuracy, (3) and out-of-distribution generalization are all interesting and important.
- The paper does a good job of comparing to the USCT literature, including deep learning methods for image reconstruction.
summary: The authors present a dataset (OpenPros) for ultrasound computed tomography (USCT) of the prostate that is generated from 4 anatomical MRI+CT images, expert annotations of different anatomical areas, speed-of-sound measurment, and ultrasound simulation using a wave equation. The authors compare OpenPros to other available USCT datasets and include benchmarks for physics-based and data-driven methods that measure (1) inference efficiency, (2) reconstruction accuracy, (3) and out-of-distribution generalization. Overall the paper is well written, most of the methods are explained clearly, and the paper is reasonably well-justified as a dataset contribution to field. However, there is not much clarity around whether whether the simulated dataset is sufficient to serve as a comprehensive machine learning benchmark for image reconstruction e.g. are 280K examples are derived from just 4 patients, which limits the effectiveness of the presented benchmarks.
weaknesses: - The dataset size is a severe limitation in my opinion. It is not at all clear why 280K number of samples are needed for machine learning training. Ablation experiments controlling the number of generated samples could help here in showing the increased benefit from more samples, justifying the approach.
- The metrics in Table 5 are not given enough context. Figure 5 offers some insight into potential benefits of data-driven methods, but this could be better quantified using the annotations. 
- Additional analysis of the types of representations learned or exploited by models mapping the prostate tissue seems to be important for the community, but not included in the paper. Even an assessment of which views are easily reconstructed, and which fail, would make a significant difference in the quality of the paper and contribution for the ICLR community.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: 1. The motivation is cancer detection and the dataset claims to have lesions. Why is there no benchmark, metric, or qualitative result related to lesion detection or reconstruction in Section 4?

2. Given the N=4 patient limitation, how can the community be confident that models trained on this data are learning generalizable features of prostate USCT, rather than just overfitting to the 4 specific pelvic bone structures in the dataset?

3. Can you please clarify the actual aperture size used for the 322 receivers? Is it 60mm (Table 3) or 150mm (Section 3.4)? How does this compare to the real-world probes in Figure 2a you aim to model?
soundness: 3
strengths: 1. This is the first public, large-scale dataset specifically for limited-angle prostate USCT.

2. The method of creating phantoms by combining real MRI/CT anatomical structures with ex vivo SOS measurements is a key strength, ensuring high anatomical realism 

3. Providing the FDTD and Runge-Kutta solvers is a valuable contribution that enhances reproducibility and enables future work.

4. The authors are transparent about the failures of current DL methods, particularly in resolving fine details and in OOD generalization , which correctly frames this as a challenging open problem.
summary: This paper introduces OPENPROS, a new large-scale (280,000 samples) benchmark dataset for limited-angle prostate Ultrasound Computed Tomography (USCT) . The authors identify a critical gap in prostate cancer imaging: while USCT can reconstruct quantitative tissue biomarkers like Speed-of-Sound (SOS), it is hampered by anatomical constraints (e.g., the pelvic bone) that create a severe limited-angle acquisition problem . The lack of realistic data has hindered the development of robust reconstruction algorithms.

The primary contribution is the dataset itself, which features a high-fidelity data generation pipeline: 3D anatomical models are derived from real clinical MRI/CT scans (from 4 patients), and these are combined with real ex vivo ultrasound SOS measurements (from 62 prostate samples) . This anatomical ground truth is then used with advanced, open-sourced FDTD and Runge-Kutta wave solvers to generate paired SOS maps and full-waveform ultrasound data 

The paper also provides a comprehensive benchmark, comparing traditional physics-based methods against deep learning models (InversionNet, ViT-Inversion) . The key findings are: 1) DL methods are faster and more accurate at general reconstruction than physics-based methods . 2) Critically, current DL models fail to resolve fine internal prostate structures and exhibit poor generalization to unseen patient anatomies
weaknesses: 1. The most significant weakness is that the 280,000 samples are derived from the anatomical structures of only 4 patients. This homogeneity means models may simply overfit to these 4 anatomical configurations, and the "patient-level" OOD test (train on 3, test on 1) is statistically insufficient to make strong claims about generalization.

2. The paper is heavily motivated by cancer detection and claims the dataset includes "synthetic lesions". However, the entire benchmark and results section focuses only on reconstructing general anatomy . The clinical-facing motivation is completely disconnected from the actual technical evaluation.

3. The use of 2D simulation is a major simplification. It ignores real-world 3D propagation and out-of-plane scattering effects, meaning models trained on this data may not be at all applicable to real 3D clinical systems.

4. There is a contradiction in the text regarding the simulation aperture. The text implies a wide aperture ("entire lateral extent" of 150mm), while Table 3 suggests a 60mm length. Both 60mm and 150mm seem much larger than the clinical probes shown in Figure 2a. This suggests the simulation may be providing more data than a real probe, making the problem artificially easier.

---

confidence: 3
contribution: 2
initial_rating: 4
presentation: 3
questions: 1. “Our dataset comprises over 280,000 paired 2D SOS phantoms and ultrasound full-waveform data derived from anatomically realistic 3D prostate models generated from clinical MRI/CT scans and ex vivo ultrasound measurements, annotated meticulously by clinical experts.” Have all these patients underwent RP to take the whole prostate out so that you can do the ex vivo measurement? Or it is just ex vivo phantoms measurements?
2. “In our specific configuration, each SOS map has a spatial resolution of 401 × 161 grid points.” Is the physical resolution the same across different SOS maps?
3. Is the simulated ultrasound meaningful enough in your use case in comparison to real patients’ ultrasound?
4. “To best mimic the tissue heterogeneity, we employed Gaussian distributions with given mean values and standard deviations from the tissue database to assign speed of sound in different tissue types.” Do you have any references saying this approach could be reasonable?
5. When the authors introduced the samples, rather than only mention 280,000 slides, it would be better to also mention how many patients got involved in the study and the dataset, since generally you would like to split the train/test set by patients, not by slides to guarantee the independence.
soundness: 3
strengths: 1. The proposed dataset size is large.
2. The authors stated that this dataset is the first one in their specific use case, could be meaningful.	 
3. The manuscript is well-written.
summary: The authors introduced OPENPROS, the first large-scale benchmark dataset for limited-angle prostate Ultrasound computed tomography, designed to evaluate machine learning algorithms for inverse problems. Baseline comparisons proposed based on physics-based approaches and DL-based approaches. Dataset is open source, and seems like quite clinically meaningful in the specific use case. However, several limitations exists. For example but not limited to, it is hard for me to evaluate if it is truly clinically meaningful because I’m lacking the specific domain knowledge, etc.
weaknesses: 1. More detailed explanation of the relationships in between USTC, 2D SOS phantoms and ultrasound is needed. I cannot understand the clinical problem behind. Many reviewers have expertise in medical imaging, many also have expertise in prostate imaging, but I guess very limit readers and reviewers understand the relationship among them. For example, although I’m familiar with abdominal imaging and related applications, when I looked at the statement “Our dataset comprises over 280,000 paired 2D SOS phantoms and ultrasound full-waveform data derived from anatomically realistic 3D prostate models generated from clinical MRI/CT scans and ex vivo ultrasound measurements, annotated meticulously by clinical experts.”, I don’t know what does it really mean. 280,000 is a very large number in general, but what about in the context of USTC, 2D SOS phantoms, etc. ? What should I focus? I think this part needs to be emphasized since you are proposing a dataset, and it should impress the readers about the quantity of the data, quality of the data and the clinical meaningfulness about the proposal. Now I don’t feel them. Or, maybe it is purely my lack of expertise. If other reviewers have the similar problem or questions, I would like to suggest you to seek for conferences like MICCAI, or journals like IEEE TMI, MIA etc. I do believe that you put lots of effort into the dataset, but it’s hard for me to really measure it. 
2. “It contains anatomically realistic 2D speed-of-sound phantoms, organ segmentation labels, and corresponding simulated ultrasound waveforms derived from detailed 3D digital prostate models.” It seems like the data are generated from phantoms not patients, and the ultrasound waveforms were simulated. Therefore, how the study really means in clinical applications might be questionable.
3. “two data-driven models: CNN-based InversionNet Wu & Lin (2019) and a Vision Transformer (ViT)-based variant Dosovitskiy et al. (2020), referred to here as ViT-Inversion.” No image reconstruction methods related to inverse problems like UNet-like or Diffusion-based models (could be other generative models) introduced.


Decision: Accept (Poster)