one imaging modality that could address the challenge of imaging intact organ vascular networks is synchrotron phase - contrast tomography. hierarchical phase - contrast tomography ( hip - ct ) is a technique which leverages the european synchrotron radiation facility ’ s ( esrf ) extremely brilliant source ( ebs ), a high - energy fourth - generation synchrotron source, to image intact human organs. by utilising the high spatial coherence of the esrf - ebs and the long beamlines available at esrf, the development of hip - ct has allowed the scaling of synchrotron phase - contrast tomography to sample sizes up to and including intact human organs. datasets created with hip - ct are hierarchically nested three - dimensional ( 3d ) volumes at multiple resolutions, with exceptional soft tissue contrast spanning from small voi to the whole intact organ ( fig. 1a ). as an example of hip - ct ’ s potential, we have previously profiled human glomerular morphology and number across cubic centimetres of intact human kidney. however, the soft tissue contrast achievable with hip - ct, coupled with its high spatial resolution, potentiates the visualisation and quantification of vascular networks across whole human organs, and could address the limitations of current imaging technologies used to map kidney vascular architecture. here, we demonstrate how the arterial network of an intact human kidney can be extracted and quantified across multiple length scales using hip - ct without use of a vascular contrast agent. our pipeline utilises the benefits of hip - ct, such as validation of segmentation using multiscale data, whilst also providing solutions for the technical challenges associated with hip - ct, for example, the collapse of large vessels. within the human kidney, we delineated the extent and morphology of the vasculature, from the renal artery down to the interlobular arteries. in doing so, we were able to quantify heterogeneity in vascular architecture within the context of ordering schemes describing morphological network branching. we also demonstrate how the multiscale nature of hip - ct allows estimation of the vascular network between the interlobular arteries and afferent arterioles in smaller vois, which we describe as ' local ' scans, within the still - intact kidney. we perform a quantitative comparison between our human and previously published rodent kidney vascular networks, the latter of which has been used as inputs for biophysical modelling of kidney vascular blood flow. we further demonstrate how the label - free nature and exceptional soft tissue contrast of hip - ct allow vascular heterogeneity to be quantified in the context of other anatomical features, such as the different compartments of the kidney. such spatial variations highlight the link between regional structure and function, reinforcing the importance of quantitative analyses for understanding and modelling regional microenvironments within the human kidney. using hip - ct in a hierarchical fashion, we imaged the whole intact kidney obtained from a 63 - year - old male organ donor. we initially performed an overview scan of the entire kidney at 25 μm per voxel, followed by selecting and imaging representative vois at 6. 5 μm per voxel and 2. 6 μm per voxel ( fig. 1a ). as these image volumes are inherently aligned, expert annotation using renal anatomical landmarks ( fig. 1b ) was applied to the image volumes taken at each resolution to produce a multiscale segmented model of the kidney ’ s arterial network ( supplementary movie 1 ). from the segmented data, we were able to identify examples of and interconnect all known anatomical subdivisions of the kidney arterial system ( fig. 1c ). the segmental pattern of anterior, posterior, superior and inferior territories supplying the kidney parenchyma was clearly delineated. each vascular territory ( fig. 1d and supplementary movie 2 ) had a corresponding kidney arterial branch originating from the hilum, which bifurcated before hierarchical branching towards the cortical parenchyma. we next sought to quantitate the arterial network in a reliable and reproducible manner. as we have previously shown that quantitative features of vascular networks can vary by the image processing pipeline used, we developed our own bespoke image processing pipeline ( fig. 2 ), involving reduction of the initial hip - ct image to a skeleton, or spatial graph representation, of the arterial network. the graph representation comprises a set of ‘ nodes ’; defined as 3d locations where vessels meet or end, and ‘ segments ’; defined as the connections between these nodes ( see supplementary fig. 6a and fig. 4a ). our pipeline comprises 8 steps, which are fully detailed in our supplementary note 2, and enables the generation of a spatial graph from segmented hip - ct data, with quantification of error in segmentation ( from multiscale comparison ) and skeletonization ( through application of the skeletonization metric ). our pipeline first ( fig. 2, step 1 ) assesses validation of the segmentation. by aligning segmentations from scans taken at 13 µm per voxel, with vois captured at 50 µm per voxel, the higher resolution scans served as ‘ ground truth ’ for the lower resolution scanning. we used the cl - dice metric to quantify the overlapping vessel portions finding that 97 % of vessels with a vessel lumen radius greater than 50 µm are detectable at 50 µm per voxel. our next step ( fig. 2, step 2 ) comprises the optimisation of the skeletonization algorithm. we applied three different skeletonization algorithms, and utilised the recently developed skeleton super - metric to determine the most suitable algorithm and its parameter optimisation. we found that the centerline tree algorithm ( amira - avizo v2021. 1 ) was the best candidate algorithm, as indicated by its lower super - metric value in comparison to other skeletonization algorithms ( fig. 2, step 2 ). thereafter, several steps were implemented to correct the skeleton for hip - ct specific challenges ( fig. 2, steps 3 – 8 ), namely the multiscale nature of the vasculature, and the presence of collapsed vessels as a consequence of the ex vivo, label - free hip - ct protocol. the challenge of multiscale vascular trees was corrected using a truncated strahler ordering system, which partitions the network into larger or smaller calibre vessels. smoothing was then applied to all large calibre vessels to reduce tortuosity in the vessel centreline, an artefact which occurs due to the sensitivity of skeletonization algorithms to noise along the vessel surface ( fig. 2, steps 3 and 4 ). following this multiscale smoothing approach, fig. 2 steps 5 and 6 involved the identification and manual verification of collapsed vessels. initially, all large - calibre vessels were flagged as potentially collapsed. additionally, smaller calibre vessels that were potentially collapsed were identified based on their categorisation below the 10th percentile for radius in their truncated strahler order ( fig. 2, step 5 ). once identified, collapsed vessels were subject to a bounding box, automatically extracted and thereafter manually determined whether correction of the radius was required to account for collapse ( fig. 2, step 6 ). for vessels requiring correction, cross - sectional planes along the vessel centreline were extracted, and the radius was calculated based on the cross - sectional perimeter ( fig. 2, step 6 ). finally, the identification of outlier radii in these cross - sectional planes was performed, using a 95th and 5th percentile windowing for radius along the vessel length. additionally, an option was applied to manually flag planes that appeared compromised by residual tortuosity in the vessel centreline ( fig. 2, step 8 ).