Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal

Adnan H Siddiqui; Allison Shields; Ciprian N Ionita; Kyle A Williams; Mohammad Mahdi Shiraz Bhurwani; Parmita Mondal; Swetadri Vasan Setlur Nagesh

REVIEW 4 major objections 5 minor 20 references

Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Two-dimensional quantitative angiography can be made view- and injection-independent by dividing the angiogram by a 3D-derived path-length map, then deconvolving the dome curve with the arterial input function.

desk verdict A legitimate integration of the group's own PLC and SVD methods with clear self-consistency evidence, but the clinical claims outrun the validation, especially on 3D-2D registration and ground truth. read the letter →

arxiv 2411.08185 v2 pith:P345WQSA submitted 2024-11-12 physics.med-ph

Parmita Mondal , Allison Shields , Mohammad Mahdi Shiraz Bhurwani , Kyle A Williams , Swetadri Vasan Setlur Nagesh , Adnan H Siddiqui , Ciprian N Ionita This is my paper · ORCID

classification physics.med-ph

keywords quantitativeangiographydigitalsubtractionpath-lengthcorrectionforeshorteningbiasinjectionvariabilitysingularvaluedecompositiondeconvolutionintracranialaneurysmhemodynamics

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that two known biases in 2D quantitative angiography — foreshortening, where the X-ray path through a vessel changes with viewing angle, and injection variability, where manual contrast delivery changes the time-density curves — can be removed by a two-step correction pipeline. The first step divides the logarithmic angiogram by a 3D-derived path-length map, converting projection intensity into something closer to contrast concentration. The second step deconvolves the aneurysm's time-density curve with the arterial input function using SVD-based methods, stripping the injection waveform from the hemodynamic signal. In in-silico aneurysm models and a patient-specific in-vitro phantom, the corrected parameters — peak height, area under the curve, and mean transit time — become nearly independent of projection view and injection duration, with root-mean-square errors between arterial input functions falling sharply and mean-transit-time slopes versus injection duration approaching zero. If true, the same lesion can be compared across different C-arm angles and injection runs during a single procedure, which would make quantitative angiography a more trustworthy intraoperative tool.

What carries the argument

The load-bearing object is the path-length weight matrix: for each detector ray, the length of its intersection with the co-registered 3D vessel mask, obtained by forward-projecting the reconstruction onto the DSA geometry and ray tracing. Dividing the log-angiogram by this weight matrix converts the signal from path-length-weighted absorption into a concentration-like signal, which is what removes view dependence. The second mechanism is SVD deconvolution of the aneurysm dome time-density curve by the arterial input function, which removes the injection waveform; the paper compares sSVD, bSVD, and oSVD variants. The corrected impulse response function then yields the three parameters that the paper treats as the stable hemodynamic readouts.

What would settle it

Acquire DSA in two views at two injection rates for a single phantom, then deliberately mis-register the 3D mask by increasing translational offsets (for example, 1, 2, and 5 mm) and recompute the mean transit time; if the mean-transit-time-versus-duration slope grows with mis-registration rather than staying flat, the claimed injection independence is bounded by registration accuracy and fails outside that tolerance.

Watch

Extended reading notes

Core claim

The authors' central claim is that the projection and injection biases that make quantitative angiography unstable are not intrinsic to the hemodynamics, and that they can be corrected with a co-registered 3D geometry plus deconvolution. After ray tracing through the 3D vessel mask to build a path-length weight matrix, each logarithmically subtracted angiographic frame is divided by that matrix to remove foreshortening. Then the dome time-density curve is deconvolved by the arterial input function with one of three SVD variants (standard Tikhonov-regularized, block-circulant, and oscillation-index), producing an impulse response function whose peak height, area under the curve, and mean transit time are reported. The paper presents RMSE reductions between arterial input curves from frontal and lateral views after path-length correction, and $\mathrm{MTT}$-versus-injection-duration slopes near zero after the combined correction: $0.015 \pm 0.017$, $0.014 \pm 0.032$, and $0.013 \pm 0.025$ for sSVD, bSVD, and oSVD in silico, and $0.031 \pm 0.015$, $0.047 \pm 0.031$, and $0.013 \pm 0.004$ in vitro.

Load-bearing premise

The entire correction rests on the 3D vessel geometry being accurately co-registered with the 2D DSA frame; if that alignment is off, the ray-traced path-length map is wrong and the correction could inject new artifacts instead of removing bias.

Editorial extensions

If this is right

Post-correction arterial input functions from frontal and lateral views agree, so angiographic comparisons no longer need to be restricted to a single projection angle.
Mean transit time stays nearly flat over injection durations from 0.25 to 2 seconds, so the speed of a 5 ml bolus does not change the hemodynamic readout.
The pipeline was demonstrated on both CFD-generated virtual angiograms and a physical patient-specific phantom, suggesting it can be transferred to clinical C-arm DSA data.
Stable parameters allow pre- and post-treatment comparisons to be made even when the C-arm angle or injection settings differ between runs, which is the common intraoperative situation.
All three SVD variants flattened the mean-transit-time dependence, so the choice among them can be made on noise handling, computational cost, or other practical grounds.

Reading between the lines

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

A testable extension would use the residual mean-transit-time-versus-duration slope as a per-case quality check: a large slope after correction would flag a failed 3D-to-2D co-registration or an incomplete vessel mask.
The same path-length weight matrix could be applied to other DSA-derived biomarkers, such as parametric color maps or wash-in/wash-out indices, not just the three impulse-response parameters studied here.
Registration accuracy is the unquantified tolerance: perturbing the 3D-to-2D alignment by millimeters and measuring the resulting mean-transit-time drift would define the clinical geometry requirement.
The method depends on having a pre-existing 3D volume, so workflows that acquire only biplane DSA would need a surrogate depth model before this correction could be applied.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things about arXiv:2411.08185. First, the paper is a straightforward integration of the same group's earlier path-length correction (ref 8) and SVD-based injection variability removal (ref 6), now applied together on new in-silico and in-vitro cases. Second, the validation is self-consistency, not accuracy: reduced AIF RMSE across views and flattened MTT versus injection-duration slopes. That is a reasonable demonstration that the pipeline removes the intended biases, but it does not show that the corrected parameters are closer to true hemodynamics.

What the paper does well: the in-silico experiments are clean geometry is known by construction, so PLC is exact there and the reported stability of AIFs and MTT across views and injection durations is genuine evidence that the concept works in simulation. The in-vitro setup uses a real biplane system and a patient-specific phantom, which is a step beyond pure simulation. The writing is clear and the limitations section is honest about the need for clinical validation. The authors do not overclaim in the abstract; they claim improved reliability, which is supported by their metrics.

The soft spots are real but not fatal. Most importantly, the stress-test concern about 3D-2D co-registration for the in-vitro data is on target: the paper reports no registration error (Dice, TRE, or similar) and no sensitivity analysis for misalignment. Because PLC divides by ray-traced path lengths, a small misregistration could nonuniformly scale the TDCs and produce exactly the kind of RMSE reduction and slope flattening they report. The in-silico experiments cannot rule this out because they assume perfect alignment. This does not invalidate the method, but it does mean the clinical reliability claim is not yet established.

Second, the MTT slope flattening after SVD is partly tautological: if the deconvolution successfully removes injection effects by construction, the slope should go to zero. A ground-truth comparison, e.g., MTT from the CFD simulation or from an independent flow measurement, would have made the result much stronger. Third, the SVD truncation threshold is described as empirically tuned and never disclosed, which makes the experiments hard to reproduce. No code or data are provided.

Overall, this is a solid incremental paper from a group that knows this problem well. The integration is legitimate and the in-silico evidence is decent. But the current evidence supports "the method behaves consistently," not "the measurements are reliable in the clinic." It deserves peer review because the underlying question matters and the in-vitro gap is addressable: report registration error, add a sensitivity analysis, and compare against a gold-standard MTT. The paper would be stronger after major revision.

If you work on quantitative angiography or intraprocedural hemodynamics, this is worth reading. If not, you can skip it. It is not a breakthrough, but it is neither sloppy nor dishonest.

Referee Report

4 major / 5 minor

Summary. This manuscript proposes a pipeline that combines path-length correction (PLC) with singular value decomposition (SVD) based deconvolution to reduce projection-induced foreshortening and injection-related variability in two-dimensional quantitative angiography. The PLC step co-registers a 3D vascular mask or reconstruction with the 2D DSA projection, ray-traces vessel-intersection lengths, and divides the logarithmic angiogram by this path-length map. Three SVD variants (sSVD, bSVD, oSVD) are then used to deconvolve the aneurysm time-density curve with an arterial input function, yielding impulse response parameters PH_IRF, AUC_IRF, and MTT. The method is evaluated in three in-silico intracranial aneurysm models and one in-vitro patient-specific phantom under different views, flow velocities, and injection durations. The authors report reduced RMSE between AIFs from different views/locations after PLC and flattening of MTT versus injection-duration slopes after PLC+SVD, and conclude that the combination significantly enhances the reliability of quantitative angiographic measurements.

Significance. If the proposed method truly removes projection and injection biases, it would fill a practical clinical need for stable quantitative angiographic measurements during neurovascular procedures. The paper's strengths are its combined in-silico and in-vitro experimental design, the systematic comparison of three standard SVD deconvolution variants, and a workflow that relies on routinely acquired 3D rotational angiography. However, the current validation is almost entirely based on self-consistency metrics—agreement between AIFs and constancy of MTT across injection durations—rather than on comparisons with independent ground truth. In addition, the in-vitro branch of the pipeline depends on an unquantified 3D-to-2D registration step. The contribution is therefore promising and potentially useful, but the central claim of enhanced reliability requires stronger validation before it can be considered established.

major comments (4)

[Section 2.2] The accuracy of the PLC pipeline in the in-vitro experiments depends on the co-registration between the forward-projected 3D reconstruction and the averaged DSA frame, but the paper reports no quantitative registration error (e.g., target registration error, Dice overlap, or manual landmark distances) and no sensitivity analysis to misalignment. Because PLC divides each pixel by a ray-traced path length, even small registration errors could nonuniformly scale the time-density curves and potentially produce or mask the RMSE reductions and slope flattening reported in Section 3. The in-silico experiments assume perfect alignment by construction and therefore cannot rule out such artifacts. Please add a registration accuracy metric and a perturbation study (e.g., applying realistic translations or rotations to the projected mask) to show that the correction is robust.
[Section 3 (Table 1 and Figures 5-6)] The validation is based entirely on self-consistency metrics: RMSE between AIFs from different views/locations and the flattening of MTT versus injection-duration slopes. There is no comparison to a gold-standard measurement, even though the in-silico CFD simulations provide an independent ground-truth transit time that could be computed directly from particle trajectories, and the in-vitro phantom could potentially be assessed with an independent measurement technique. Without such a comparison, the claim that PLC+SVD 'significantly enhances the reliability of quantitative angiographic measurements' is not fully supported; the method could be imposing an internally consistent but biologically or physically incorrect result. Please include direct comparisons with ground-truth MTT or other independent quantities where available.
[Section 2.3] The SVD truncation threshold is described as 'determined empirically, retaining singular values above a certain percentage of the maximum value,' but the manuscript does not specify the exact selection rule, the range of thresholds tested, or whether the same threshold was applied across all cases. If the threshold was chosen by inspecting the same datasets used to compute the MTT slopes, the reported flattening could partly reflect tuning rather than genuine removal of injection bias. Please provide a principled threshold selection criterion (e.g., based on noise level or the L-curve) and report the sensitivity of the MTT results to the threshold choice.
[Section 3 (in-vitro results)] The in-vitro conclusions are derived from a single patient-specific phantom, two views, and no repeated acquisitions, and the reported MTT slopes are means across views without measures of variability or statistical tests. The statement that the method 'nearly eliminated' injection effects is therefore based on a very small sample. Please report per-view slopes, standard errors, and formal tests of whether the slopes are consistent with zero, or explicitly state the exploratory nature of these in-vitro results.

minor comments (5)

[Abstract and Section 1] The phrase 'path-length correction (PLC) correction' is redundant; the second 'correction' should be deleted.
[Section 2.2 and Figure 3 captions] The term 'corregistration' is used in several places; it should be 'co-registration.'
[Table 1] The caption states that RMSEs are averaged across three in-silico models but does not indicate the spread of the values; please include standard deviations or error bars so the reader can assess consistency across models.
[Section 2.4] The hypothesis tests are described only qualitatively (e.g., 'significant reduction in RMSE') without statistical tests; please add confidence intervals or p-values, or soften the wording to describe observed reductions rather than significant effects.
[Figures 5 and 6] The captions do not define the line colors and styles in the figures (e.g., which lines correspond to 'without PLC and deconvolution,' 'SVD alone,' and 'PLC+SVD'); please ensure every plot element is identified in the caption or legend.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the in-vitro PLC validation is independent of the fitted geometry, and no QA parameter is optimized to the reported endpoints.

full rationale

The paper's central claim is that path-length correction plus SVD deconvolution makes QA parameters stable across projection views and injection durations. The load-bearing evidence includes in-vitro experiments in which the PLC map is obtained by co-registering a 3D rotational DSA reconstruction with 2D DSA frames (Section 2.2), then dividing the angiogram by the ray-traced path-length matrix; this is a physical normalization, not a fit to the AIF RMSE or MTT endpoints. The SVD truncation threshold is described only as 'determined empirically' (Section 2.3), with no statement that it was selected to minimize the reported MTT slopes, so a fitted-input-called-prediction step cannot be established from the paper text. The in-silico PLC test (Section 2.1) is indeed nearly an identity: virtual DSA is generated by cone-beam forward projection whose weights are the same ray-intersection lengths used for correction, so dividing by the weight matrix is the inverse of the forward model and the RMSE reduction is mathematically guaranteed. However, the authors explicitly acknowledge the geometry is 'already aligned' in the in-silico case, and the in-vitro arm provides the independent empirical test of the method. Self-citations (Refs. 6, 8, 14) are method and feasibility references for PLC and SVD variants; the present paper contains its own in-vitro measurements of the corrected parameters. The acknowledged limitation that PLC requires high-quality 3D imaging and accurate co-registration (Discussion) is a robustness concern, not a circularity: the paper does not define PLC in terms of the outcomes it predicts. No specific equation or quoted passage reduces the central derivation to a fitted parameter or to a self-citation chain.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are the empirically tuned SVD truncation threshold and registration settings. The axioms are standard assumptions for DSA, deconvolution, and CFD-based validation, but they are not independently verified in this work.

free parameters (2)

SVD truncation threshold = not reported
Section 2.3: 'The optimal truncation threshold (SVD trunc) was determined empirically, retaining singular values above a certain percentage of the maximum value.' This threshold directly affects the deconvolved IRF and hence PH, AUC, and MTT. The value is not disclosed, and it is tuned to the data.
Registration hyperparameters = grid spacing 128 to 0.01, 4-5 resolutions, LBFGS, etc.
Section 2.2: The affine and B-spline registration settings in Elastix are chosen manually. These affect the alignment of the 3D mask with the DSA and therefore the path-length matrix. They are not derived from first principles and are specific to this dataset.

assumptions (5)

domain assumption The recorded DSA gray values after logarithmic subtraction are linearly proportional to the integrated contrast concentration along the X-ray path (Beer-Lambert law).
This is the basis for dividing the angiogram by the path-length map to correct for foreshortening. It is standard in DSA but not explicitly stated or tested in the paper.
domain assumption The co-registration between the 3D vascular mask and the 2D DSA projection is accurate enough that the ray-traced path-length matrix faithfully represents the true X-ray paths through the vessel.
Section 2.2 describes the registration pipeline but does not quantify registration error. The entire PLC result depends on this alignment.
domain assumption SVD deconvolution with the AIF recovers the true impulse response function of the aneurysm dome.
Section 2.3 applies sSVD, bSVD, and oSVD, assuming that the AIF measured at the inlet is a valid input function and that the system is linear and time-invariant. This is a standard but nontrivial assumption in perfusion analysis.
domain assumption MTT invariance across injection durations is a valid indicator that injection bias has been removed.
The second hypothesis states that MTT should be independent of injection duration. This is reasonable, but it is a proxy; the true MTT from CFD is never compared, so a pipeline that over-smooths could also produce flat slopes.
domain assumption In-silico CFD simulations using steady-state laminar flow with parabolic inlet profiles adequately represent the hemodynamics of the aneurysms for validating the correction method.
Section 2.1 describes solving Navier-Stokes with steady-state laminar flow. Pulsatile flow is not modeled in-silico, which may affect the temporal characteristics of the contrast curves.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal." pith.science (2026). https://pith.science/paper/P345WQSA

@misc{pith2026241108185,
  author       = {Pith},
  title        = {Pith review of: Analysis of Quantitative Angiography using Projection Foreshortening Correction and Injection Bias Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P345WQSA}},
  note         = {Machine review of arXiv:2411.08185}
}

read the original abstract

This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics.

Figures

Figures reproduced from arXiv: 2411.08185 by the authors.

**Figure 1.** , where a simulated biplane is presented, foreshortening affects the TDCs extracted from both the aneurysm dome and the AIF. Assuming the same angiographic run from two projections, the amount of contrast in the aneurysm and vessel is identical; however, the recorded gray values at the detector are directly correlated with the X-ray path length through the aneurysm. Previous work proposed a baseline correction metho… view at source ↗

**Figure 2.** Snapshot of PLC for one frame in the angiographic sequence for three different aneurysms. Yellow arrows show the aneurysm dome and inlet ROI that will be used to extract the TDCs for data analysis [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Demonstrates the corregistration between 3D rotational DSA and 2D angiograms, followed by path-length correction, on in-vitro acquired angiograms [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Impact of path-length correction (PLC) on in-silico and in-vitro angiograms, demonstrated through alterations in arterial input functions (AIFs) and impulse response functions (IRFs). The top two rows display in-silico angiograms pre- and post-PLC, while the bottom row…

**Figure 5.** Figure 5: Plot of MTT after the implementation of sSVD (A), bSVD (B) and oSVD (C) is shown. The first column is at inlet velocity 0.25m/sec, the second column is at inlet velocity 0.35m/sec and the last column is at inlet velocity 0.45m/sec, respectively [PITH_FULL_IMAGE:figure…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages

[1]

Cerebral Aneurysm

Jersey AM, Foster DM. Cerebral Aneurysm. In: StatPearls. Treasure Island (FL)2023

work page 2023
[2]

Mistretta CA, Grist TM. X-ray digital subtraction angiography to magnetic resonance-digital subtraction angiography using three-dimensional TRICKS - Historical perspective and computer simulations: A review. Invest Radiol. 1998;33(9):496-505

work page 1998
[3]

Effect of injection technique on temporal parametric imaging derived from digital subtraction angiography in patient specific phantoms

Ionita CN, Garcia VL, Bednarek DR, et al. Effect of injection technique on temporal parametric imaging derived from digital subtraction angiography in patient specific phantoms. Proc Spie. 2014;9038. 11 11

work page 2014
[4]

Intra-operative optimal flow diverter selection for intracranial aneurysm treatment using angiographic parametric imaging: feasibility study

Mondal P, Udin M, Shiraz Bhurwani MM, Williams K, Ionita C. Intra-operative optimal flow diverter selection for intracranial aneurysm treatment using angiographic parametric imaging: feasibility study. Vol 12468: SPIE; 2023

work page 2023
[5]

Effect of computed tomography perfusion post-processing algorithms on optimal threshold selection for final infarct volume prediction

Rava RA, Snyder KV, Mokin M, et al. Effect of computed tomography perfusion post-processing algorithms on optimal threshold selection for final infarct volume prediction. Neuroradiol J. 2020;33(4):273-285

work page 2020
[6]

Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study

Mondal P, Setlur Nagesh SV, Sommers-Thaler S, et al. Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study. Med Phys.n/a(n/a)

work page
[7]

Intensity correction in all-sky auroral image projection transform

Yang YG, Liu RY, Sato NS. Intensity correction in all-sky auroral image projection transform. Chinese Sci Bull. 1997;42(8):700-703

work page 1997
[8]

Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study

Shields A, Williams K, Bhurwani MMS, et al. Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study. Med Phys. 2023. doi: 10.1002/mp.16808

work page doi:10.1002/mp.16808 2023

Show all 20 references

[9]

Analysis of STL files

Szilvsi-Nagy M, Mátyási G. Analysis of STL files. Mathematical and Computer Modelling. 2003;38

2003
[10]

Computational Fluid Dynamics: Science of the Future

Thabet S, Thabit T. Computational Fluid Dynamics: Science of the Future. International Journal of Research and Engineering. 2018;5:430-433

2018
[11]

Motion Correction in Cone Beam CT (CBCT) by Projection Image Warping

Marchant T, Price G, Moore C. Motion Correction in Cone Beam CT (CBCT) by Projection Image Warping. Clin Oncol-Uk. 2009;21(3):243-243

2009
[12]

On Tikhonov regularization, bias and variance in nonlinear system identification

Johansen TA. On Tikhonov regularization, bias and variance in nonlinear system identification. Automatica. 1997;33(3):441-446

1997
[13]

On singular values of block circulant matrices

Niezgoda M. On singular values of block circulant matrices. Linear Algebra Appl. 2019;563:400- 410

2019
[14]

Mondal P, Setlur Nagesh SV, Sommers-Thaler S, et al. Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study [published online ahead of print 20240828]. Med Phys. 2024. doi: 10.1002/mp.17357

2024 doi
[15]

Singular value decomposition as a tool for background corrections in time-resolved XFEL scattering data

Haldrup K. Singular value decomposition as a tool for background corrections in time-resolved XFEL scattering data. Philos T R Soc B. 2014;369(1647)

2014
[16]

Tikhonov regularization with a solution constraint

Calvetti D, Reichel L. Tikhonov regularization with a solution constraint. Siam J Sci Comput. 2004;26(1):224-239

2004
[17]

Tracer arrival timing- insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix

Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG. Tracer arrival timing- insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magnet Reson Med. 2003;50(1):164-174

2003
[18]

Feasibility of locating infarct core with 2D angiographic parametric imaging (API) using computed tomography perfusion data

Rava RA, Allman AB, Bhurwani MMS, et al. Feasibility of locating infarct core with 2D angiographic parametric imaging (API) using computed tomography perfusion data. Medical Imaging 2019: Physics of Medical Imaging. 2019;10948

2019
[19]

Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction

Shiraz Bhurwani MM, Waqas M, Podgorsak AR, et al. Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction. Journal of NeuroInterventional Surgery. 2020;12(7):714

2020
[20]

Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms

Podgorsak AR, Rava RA, Shiraz Bhurwani MM, et al. Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms. Journal of NeuroInterventional Surgery. 2020;12(4):417

2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Cerebral Aneurysm

Jersey AM, Foster DM. Cerebral Aneurysm. In: StatPearls. Treasure Island (FL)2023

work page 2023

[2] [2]

Mistretta CA, Grist TM. X-ray digital subtraction angiography to magnetic resonance-digital subtraction angiography using three-dimensional TRICKS - Historical perspective and computer simulations: A review. Invest Radiol. 1998;33(9):496-505

work page 1998

[3] [3]

Effect of injection technique on temporal parametric imaging derived from digital subtraction angiography in patient specific phantoms

Ionita CN, Garcia VL, Bednarek DR, et al. Effect of injection technique on temporal parametric imaging derived from digital subtraction angiography in patient specific phantoms. Proc Spie. 2014;9038. 11 11

work page 2014

[4] [4]

Intra-operative optimal flow diverter selection for intracranial aneurysm treatment using angiographic parametric imaging: feasibility study

Mondal P, Udin M, Shiraz Bhurwani MM, Williams K, Ionita C. Intra-operative optimal flow diverter selection for intracranial aneurysm treatment using angiographic parametric imaging: feasibility study. Vol 12468: SPIE; 2023

work page 2023

[5] [5]

Effect of computed tomography perfusion post-processing algorithms on optimal threshold selection for final infarct volume prediction

Rava RA, Snyder KV, Mokin M, et al. Effect of computed tomography perfusion post-processing algorithms on optimal threshold selection for final infarct volume prediction. Neuroradiol J. 2020;33(4):273-285

work page 2020

[6] [6]

Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study

Mondal P, Setlur Nagesh SV, Sommers-Thaler S, et al. Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study. Med Phys.n/a(n/a)

work page

[7] [7]

Intensity correction in all-sky auroral image projection transform

Yang YG, Liu RY, Sato NS. Intensity correction in all-sky auroral image projection transform. Chinese Sci Bull. 1997;42(8):700-703

work page 1997

[8] [8]

Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study

Shields A, Williams K, Bhurwani MMS, et al. Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study. Med Phys. 2023. doi: 10.1002/mp.16808

work page doi:10.1002/mp.16808 2023